diff --git a/litellm/router_strategy/complexity_router/README.md b/litellm/router_strategy/complexity_router/README.md
index 88ed374dd3f..d605b43e42a 100644
--- a/litellm/router_strategy/complexity_router/README.md
+++ b/litellm/router_strategy/complexity_router/README.md
@@ -207,17 +207,27 @@ custom_dimensions:
   - name: sqlMigration
     weight: 0.7
     patterns: ['\b(create|alter|drop)\s{1,4}table\b']
+  - name: dataPipeline
+    weight: 0.4
+    scoring_mode: match_count
+    keywords: [airflow, dbt, snowflake]
 ```
 
 Each dimension contributes its weight once when any matcher hits the current ask. Repeated matches do not increase it. The built-in score and tier boundaries are unchanged, and the total score is not renormalized. Keywords use the existing case-insensitive word-boundary and CJK rules. Regexes search the first 2048 characters case-insensitively and compile during configuration validation and router initialization, never per request
 
+`scoring_mode` is optional and defaults to `binary`, the behavior above. `match_count` grades the dimension by how many distinct matchers hit: none scores 0 and emits no signal, one scores half the weight, two or more score the full weight. Repeated occurrences of one matcher never raise the count, keywords are distinct case-insensitively, patterns are distinct by source, and a keyword and a pattern are always distinct from each other. Matching stops as soon as the selected mode's maximum is reached, so a binary dimension still stops at its first hit. Existing configurations without the field keep binary scoring and the same tuning fingerprint, so the field only counts as a tuning change when set to `match_count`
+
+### Weights through the API versus the dashboard
+
+The API and YAML store exactly the weights written. A `dimension_weights` map and inline custom weights are read literally, missing recognized built-in names score zero, and nothing renormalizes the vector, so a total other than 1 is legal and scores accordingly. The dashboard's heuristic scoring editor is the one place that rebalances: editing one weight there holds it and redistributes the remainder across the other active dimensions in the draft, then Save sends the resulting explicit values, which the backend stores and scores as written. Opening a router, applying a preset, editing matchers, changing `scoring_mode`, or saving unrelated fields never normalizes existing weights
+
 Only `heuristic`, `heuristic_first` and `hybrid` accept custom dimensions. Each name must be a unique ASCII identifier starting with a letter, at most 64 characters, and cannot reuse a built-in dimension name or a key in `dimension_weights`. Set its weight inline, greater than zero and at most one
 
 Patterns are checked at configuration time against a grammar whose worst case stays a few milliseconds on 2048 characters. Every quantifier needs an explicit upper bound of at most 64 and must repeat a single character or character class, so `\s{1,4}` is accepted while `\s+`, `(a|aa){0,12}` and `(?:ab){0,64}` are refused. Backreferences, lookarounds, atomic groups and possessive quantifiers are refused as well. Each pattern is then costed: alternation branches and repeat lengths multiply the ways the engine can retry, and every later piece of the pattern is charged once per path that can reach it, so `a?a?a?a?a?a?a?a?` followed by a long fixed tail is refused even though each quantifier is small. The budget is 2048 work units per pattern and 8192 across the router. An invalid or over-budget pattern fails the write with a message naming the pattern and the rule it broke
 
 Limits are 16 dimensions, 32 combined keywords/patterns per dimension, 256 characters per matcher and 4096 matcher characters per dimension. Matching runs inline on the request path with no timeout and no worker thread, because the grammar is what bounds the cost. These are routing hints, not security enforcement rules
 
-The existing heuristic-v1 tuning quota covers custom dimensions and their weights: one changed router without an auto-router license, unlimited with the entitlement. Omitting `custom_dimensions` preserves existing scoring. Routing decisions and spend logs include signals such as `custom (sqlMigration)` without recording the configured pattern or matched text. The field is configured through YAML or the model API; this change adds no dashboard editor
+The existing heuristic-v1 tuning quota covers custom dimensions, their weights and their scoring mode: one changed router without an auto-router license, unlimited with the entitlement. Omitting `custom_dimensions` preserves existing scoring. Routing decisions and spend logs include signals such as `custom (sqlMigration)` without recording the configured pattern or matched text. The field is configured through YAML, the model API, or the dashboard's heuristic scoring editor
 
 ## Usage
 
diff --git a/litellm/router_strategy/complexity_router/complexity_router.py b/litellm/router_strategy/complexity_router/complexity_router.py
index c8644f52c57..ae1f82fc40b 100644
--- a/litellm/router_strategy/complexity_router/complexity_router.py
+++ b/litellm/router_strategy/complexity_router/complexity_router.py
@@ -20,7 +20,7 @@ import random
 import re
 import time
 from collections.abc import Callable, Iterator, Mapping, Sequence
-from itertools import accumulate, islice, takewhile
+from itertools import accumulate, chain, islice, takewhile
 from threading import Lock
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Literal, NamedTuple, cast
@@ -82,6 +82,7 @@ from .config import (
     ClassificationRubric,
     ComplexityRouterConfig,
     ComplexityTier,
+    CustomDimension,
     TierDefinition,
 )
 from .stall_detector import detect_stalled_task
@@ -879,6 +880,15 @@ class DimensionScore:
         self.signal = signal
 
 
+class _CustomDimensionMatchers(NamedTuple):
+    """One custom dimension's distinct matchers and the number of hits that saturates its score."""
+
+    dimension: CustomDimension
+    keywords: tuple[str, ...]
+    patterns: tuple[re.Pattern[str], ...]
+    saturation: int
+
+
 class KeywordOverride(NamedTuple):
     """A keyword_tier_rules match: the winning tier and, on the lexical path, the keyword that fired."""
 
@@ -1121,7 +1131,12 @@ class ComplexityRouter(CustomLogger):
         )
         self.simple_keywords = self.config.simple_keywords or DEFAULT_SIMPLE_KEYWORDS
         self._custom_dimensions = tuple(
-            (dimension, tuple(re.compile(pattern, re.IGNORECASE) for pattern in dimension.patterns))
+            _CustomDimensionMatchers(
+                dimension,
+                tuple(dict.fromkeys(keyword.lower() for keyword in dimension.keywords)),
+                tuple(re.compile(pattern, re.IGNORECASE) for pattern in dict.fromkeys(dimension.patterns)),
+                2 if dimension.scoring_mode == "match_count" else 1,
+            )
             for dimension in self.config.custom_dimensions
         )
         if self.config.has_custom_tiers:
@@ -1325,15 +1340,26 @@ class ComplexityRouter(CustomLogger):
         score: Final = score_high if match_count >= high_threshold else score_low
         return DimensionScore(name, score, f"{signal_label} ({detail})"), match_count
 
+    def _count_custom_hits(self, matchers: _CustomDimensionMatchers, user_text: str, scanned: str) -> int:
+        hits: Final = chain(
+            (self._keyword_matches(user_text, keyword) for keyword in matchers.keywords),
+            (pattern.search(scanned) is not None for pattern in matchers.patterns),
+        )
+        return sum(islice((1 for hit in hits if hit), matchers.saturation))
+
     def _score_custom_dimensions(self, prompt: str, user_text: str) -> tuple[tuple[DimensionScore, float], ...]:
         if not self._custom_dimensions:
             return ()
         scanned: Final = prompt[:CUSTOM_PATTERN_SCAN_CHARS]
         return tuple(
-            (DimensionScore(dimension.name, 1.0, f"custom ({dimension.name})"), dimension.weight)
-            for dimension, patterns in self._custom_dimensions
-            if any(self._keyword_matches(user_text, keyword) for keyword in dimension.keywords)
-            or any(pattern.search(scanned) is not None for pattern in patterns)
+            (
+                DimensionScore(
+                    matchers.dimension.name, hits / matchers.saturation, f"custom ({matchers.dimension.name})"
+                ),
+                matchers.dimension.weight,
+            )
+            for matchers in self._custom_dimensions
+            if (hits := self._count_custom_hits(matchers, user_text, scanned))
         )
 
     def _score_multi_step(self, text: str) -> DimensionScore:
diff --git a/litellm/router_strategy/complexity_router/config.py b/litellm/router_strategy/complexity_router/config.py
index 3ec9f9b5394..37375d9727d 100644
--- a/litellm/router_strategy/complexity_router/config.py
+++ b/litellm/router_strategy/complexity_router/config.py
@@ -667,6 +667,14 @@ class CustomDimension(BaseModel):
     weight: float = Field(gt=0, le=1, allow_inf_nan=False)
     keywords: tuple[Annotated[str, Field(min_length=1, max_length=256)], ...] = Field(default=(), max_length=32)
     patterns: tuple[Annotated[str, Field(min_length=1, max_length=256)], ...] = Field(default=(), max_length=32)
+    scoring_mode: Literal["binary", "match_count"] = Field(
+        default="binary",
+        description=(
+            "'binary' scores 1 when any matcher hits. 'match_count' scores 0.5 when one distinct matcher hits and 1 "
+            "when two or more do; repeated occurrences of one matcher never raise it. Keywords are distinct "
+            "case-insensitively, patterns by source, and a keyword and a pattern are always distinct from each other."
+        ),
+    )
 
     @model_validator(mode="after")
     def _validate_matchers(self) -> "CustomDimension":
@@ -794,8 +802,9 @@ class ComplexityRouterConfig(BaseModel):
         default=(),
         max_length=16,
         description=(
-            "Named binary dimensions added to the heuristic-v1 score. Each contributes its inline weight once "
-            "when any keyword matches the current ask or a case-insensitive regex matches its first 2048 characters. "
+            "Named dimensions added to the heuristic-v1 score. Each contributes its inline weight once "
+            "when any keyword matches the current ask or a case-insensitive regex matches its first 2048 characters; "
+            "scoring_mode 'match_count' instead grades half weight for one distinct matcher and full for two or more. "
             "Regex quantifiers repeat one character or class at most 64 times. Unbounded quantifiers, repeated groups, "
             "backreferences and lookarounds are rejected. Conservative work limits include alternation paths, "
             "repeat lengths and subsequent matching: 2048 units per pattern, 8192 across the router. "
diff --git a/litellm/router_utils/auto_router_tuning_baseline.py b/litellm/router_utils/auto_router_tuning_baseline.py
index 74f7b82389a..9699ab886b9 100644
--- a/litellm/router_utils/auto_router_tuning_baseline.py
+++ b/litellm/router_utils/auto_router_tuning_baseline.py
@@ -52,7 +52,17 @@ def tuning_fingerprint(complexity_router_config: object) -> str | None:
     supplied: Final = ((_TUNING_FIELD_SET - frozenset(("tier_model_configs",))) & frozenset(raw)) | (
         frozenset(("tier_model_configs",)) if validated.tier_model_configs else frozenset()
     )
-    payload: Final = validated.model_dump(mode="json", include=supplied)
+    payload: Final = validated.model_dump(
+        mode="json",
+        include=supplied,
+        exclude={
+            "custom_dimensions": {
+                index: {"scoring_mode"}
+                for index, dimension in enumerate(validated.custom_dimensions)
+                if dimension.scoring_mode == "binary"
+            }
+        },
+    )
     return hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()).hexdigest()
 
 
diff --git a/tests/test_litellm/router_strategy/test_complexity_router.py b/tests/test_litellm/router_strategy/test_complexity_router.py
index 5b1d8562abd..b0f2a65bb9b 100644
--- a/tests/test_litellm/router_strategy/test_complexity_router.py
+++ b/tests/test_litellm/router_strategy/test_complexity_router.py
@@ -826,6 +826,8 @@ class TestCustomDimensions:
             pytest.param({"keywords": ["x"] * 32, "patterns": ["y"]}, {}, id="combined-matcher-count"),
             pytest.param({"keywords": ["x" * 256] * 17}, {}, id="matcher-character-budget"),
             pytest.param({"unknown": True}, {}, id="extra-field"),
+            pytest.param({"scoring_mode": "graded"}, {}, id="unknown-scoring-mode"),
+            pytest.param({"scoring_mode": None}, {}, id="null-scoring-mode"),
         ],
     )
     def test_custom_dimension_invalid_configuration_rejected(
@@ -878,43 +880,125 @@ class TestCustomDimensions:
             )
 
     @pytest.mark.asyncio
-    @pytest.mark.parametrize("current_ask", ("Hello!", "orbitmesh"))
+    @pytest.mark.parametrize("scoring_mode", ("binary", "match_count"))
+    @pytest.mark.parametrize("current_ask", ("Hello!", "orbitmesh", "orbitmesh fluxgate"))
     async def test_custom_dimensions_public_hook_scores_only_current_ask(
-        self, mock_router_instance: MagicMock, current_ask: str
+        self, mock_router_instance: MagicMock, current_ask: str, scoring_mode: str
     ) -> None:
         router: Final = ComplexityRouter(
             "test-router",
             mock_router_instance,
             {
                 "tiers": {"SIMPLE": "cheap", "MEDIUM": "mid", "COMPLEX": "strong", "REASONING": "top"},
-                "custom_dimensions": [{"name": "internalFrameworks", "weight": 0.7, "keywords": ["orbitmesh"]}],
+                "dimension_weights": {},
+                "custom_dimensions": [
+                    {
+                        "name": "internalFrameworks",
+                        "weight": 0.8,
+                        "keywords": ["orbitmesh", "fluxgate"],
+                        "scoring_mode": scoring_mode,
+                    }
+                ],
             },
         )
         result: Final = await router.async_pre_routing_hook(
             model="test-router",
             request_kwargs={},
             messages=[
-                {"role": "system", "content": "orbitmesh"},
-                {"role": "user", "content": "orbitmesh"},
-                {"role": "assistant", "content": "orbitmesh is ready"},
+                {"role": "system", "content": "orbitmesh fluxgate"},
+                {"role": "user", "content": "orbitmesh fluxgate"},
+                {"role": "assistant", "content": "orbitmesh fluxgate is ready"},
                 {"role": "user", "content": current_ask},
-                {"role": "tool", "tool_call_id": "previous", "content": "orbitmesh"},
+                {"role": "tool", "tool_call_id": "previous", "content": "orbitmesh fluxgate"},
             ],
         )
         assert result is not None
         assert result.routing_decision is not None
-        assert ("custom (internalFrameworks)" in result.routing_decision["signals"]) is (current_ask == "orbitmesh")
-        assert result.model == ("top" if current_ask == "orbitmesh" else "cheap")
+        expected_score: Final = (
+            0.0
+            if current_ask == "Hello!"
+            else 0.4
+            if scoring_mode == "match_count" and current_ask == "orbitmesh"
+            else 0.8
+        )
+        assert result.routing_decision["score"] == expected_score
+        assert ("custom (internalFrameworks)" in result.routing_decision["signals"]) is (expected_score > 0)
+        assert result.model == ("cheap" if expected_score == 0 else "strong" if expected_score == 0.4 else "top")
         assert "orbitmesh" not in " ".join(result.routing_decision["signals"])
 
-    def test_custom_patterns_scan_only_the_first_2048_characters(self, mock_router_instance: MagicMock) -> None:
+    @pytest.mark.parametrize("scoring_mode", ("binary", "match_count"))
+    def test_custom_patterns_scan_only_the_first_2048_characters(
+        self, mock_router_instance: MagicMock, scoring_mode: str
+    ) -> None:
         router: Final = ComplexityRouter(
             "test-router",
             mock_router_instance,
-            {"custom_dimensions": [{"name": "late", "weight": 0.7, "patterns": [r"zzz{1,3}"]}]},
+            {
+                "custom_dimensions": [
+                    {
+                        "name": "late",
+                        "weight": 0.7,
+                        "patterns": [r"zzz{1,3}", r"yyy{1,3}"],
+                        "scoring_mode": scoring_mode,
+                    }
+                ]
+            },
         )
+        baseline: Final = ComplexityRouter("test-router", mock_router_instance)
         assert "custom (late)" in router.classify("a" * 2040 + " zzz")[2]
         assert "custom (late)" not in router.classify("a" * 2048 + " zzz")[2]
+        second_hit_past_the_bound: Final = "yyy " + "a" * 2044 + " zzz"
+        contribution: Final = (
+            router.classify(second_hit_past_the_bound)[1] - baseline.classify(second_hit_past_the_bound)[1]
+        )
+        assert contribution == pytest.approx(0.7 if scoring_mode == "binary" else 0.35)
+
+    @pytest.mark.parametrize(
+        "prompt,expected_score",
+        [
+            pytest.param("Hello!", 0.0, id="no-hit"),
+            pytest.param("orbitmesh orbitmesh ORBITMESH again", 0.5, id="one-keyword-repeated"),
+            pytest.param("create table a; CREATE TABLE b; create  table c", 0.5, id="one-pattern-repeated"),
+            pytest.param("orbitmesh and fluxgate", 1.0, id="two-keywords"),
+            pytest.param("orbitmesh then create table t", 1.0, id="keyword-plus-pattern"),
+            pytest.param("create table a; alter table b", 1.0, id="two-patterns"),
+            pytest.param("orbitmesh fluxgate create table a alter table b", 1.0, id="all-matchers"),
+        ],
+    )
+    def test_match_count_grades_distinct_matchers(
+        self, mock_router_instance: MagicMock, prompt: str, expected_score: float
+    ) -> None:
+        dimension: Final = {
+            "name": "graded",
+            "weight": 0.6,
+            "keywords": ["orbitmesh", "ORBITMESH", "fluxgate"],
+            "patterns": [r"\bcreate\s{1,4}table\b", r"\bcreate\s{1,4}table\b", r"\balter\s{1,4}table\b"],
+        }
+        baseline: Final = ComplexityRouter("test-router", mock_router_instance)
+        binary: Final = ComplexityRouter("test-router", mock_router_instance, {"custom_dimensions": [dimension]})
+        graded: Final = ComplexityRouter(
+            "test-router",
+            mock_router_instance,
+            {"custom_dimensions": [{**dimension, "scoring_mode": "match_count"}]},
+        )
+        _, baseline_score, baseline_signals = baseline.classify(prompt)
+        _, binary_score, binary_signals = binary.classify(prompt)
+        _, graded_score, graded_signals = graded.classify(prompt)
+        assert graded_score == pytest.approx(baseline_score + 0.6 * expected_score)
+        assert binary_score == pytest.approx(baseline_score + (0.6 if expected_score else 0.0))
+        expected_signals: Final = [*baseline_signals, *(["custom (graded)"] if expected_score else [])]
+        assert graded_signals == expected_signals
+        assert binary_signals == expected_signals
+
+    def test_scoring_mode_round_trips_and_defaults_to_binary(self) -> None:
+        dimension: Final = {"name": "graded", "weight": 0.6, "keywords": ["orbitmesh"]}
+        legacy: Final = ComplexityRouterConfig.model_validate({"custom_dimensions": [dimension]})
+        graded: Final = ComplexityRouterConfig.model_validate(
+            {"custom_dimensions": [{**dimension, "scoring_mode": "match_count"}]}
+        )
+        assert legacy.custom_dimensions[0].scoring_mode == "binary"
+        assert graded.model_dump(mode="json")["custom_dimensions"][0]["scoring_mode"] == "match_count"
+        assert ComplexityRouterConfig.model_validate(graded.model_dump(mode="json")) == graded
 
     def test_custom_dimensions_router_wide_regex_work_is_capped(self) -> None:
         heavy: Final = {"weight": 0.5, "patterns": ["a?" * 8 + "z"]}
@@ -1511,6 +1595,7 @@ class TestRouterComplexityDeploymentMethods:
     def test_the_shipped_rubric_and_default_prompt_stay_free(self) -> None:
         """Only an operator-written prompt is gated: picking a shipped rubric preset, or writing no
         prompt at all, leaves a router unmetered, so several of them register under a ceiling of one."""
+
         def rubric(model_name: str, model_id: str, preset: str | None) -> dict[str, object]:
             llm_config: dict[str, object] = {"model": "gpt-4o-mini"}
             if preset is not None:
@@ -1647,6 +1732,7 @@ class TestRouterComplexityDeploymentMethods:
     def test_renaming_built_in_tiers_is_not_a_custom_tier_set(self) -> None:
         """tier_labels renames the built-in ladder without defining one, so it stays ungated: two such
         routers register under a ceiling of one."""
+
         def labeled(model_name: str, model_id: str) -> dict[str, object]:
             row = self._router_row(model_name, model_id, "heuristic")
             row["litellm_params"]["complexity_router_config"]["tier_labels"] = {"SIMPLE": "Cheap", "MEDIUM": "Standard"}
@@ -2520,9 +2606,7 @@ class TestLLMClassifier:
         assert outcome.classifier_cost == pytest.approx(1.35e-05)
 
     @pytest.mark.asyncio
-    async def test_aclassify_timeout_does_not_inherit_router_retries_or_fallbacks(
-        self, llm_classifier_config
-    ):
+    async def test_aclassify_timeout_does_not_inherit_router_retries_or_fallbacks(self, llm_classifier_config):
         real_router = Router(
             model_list=[
                 {
@@ -2565,9 +2649,7 @@ class TestLLMClassifier:
         assert real_router.total_calls["openai/mock-backup-classifier"] == 0
 
     @pytest.mark.asyncio
-    async def test_aclassify_enforces_total_classifier_deadline(
-        self, mock_router_instance, llm_classifier_config
-    ):
+    async def test_aclassify_enforces_total_classifier_deadline(self, mock_router_instance, llm_classifier_config):
         cancelled = asyncio.Event()
 
         async def slow_classifier(**_kwargs: object) -> None:
@@ -12414,9 +12496,7 @@ class TestTierHealthFailover:
                     llm_provider="",
                 )
             filtered = (*cooling, *blocked, *excluded)
-            healthy = [
-                {"model_name": model, "model_info": {"id": i}} for i in ids_by_model[model] if i not in filtered
-            ]
+            healthy = [{"model_name": model, "model_info": {"id": i}} for i in ids_by_model[model] if i not in filtered]
             if not healthy:
                 raise RouterRateLimitError(
                     model=model, cooldown_time=60.0, enable_pre_call_checks=False, cooldown_list=[]
@@ -12845,9 +12925,7 @@ class TestTierHealthFailover:
         assert all(probed is not request_kwargs for probed in router.litellm_router_instance.probed_kwargs)
 
     @pytest.mark.asyncio
-    async def test_a_peer_whose_every_deployment_is_over_its_rpm_is_not_a_failover_target(
-        self, mock_router_instance
-    ):
+    async def test_a_peer_whose_every_deployment_is_over_its_rpm_is_not_a_failover_target(self, mock_router_instance):
         """RPM exhaustion is its own verdict from the owner (RouterRateLimitErrorBasic). A peer
         in that state would be rejected downstream, so it cannot be the substitute."""
         from litellm.types.router import RouterRateLimitErrorBasic
@@ -12880,9 +12958,7 @@ class TestTierHealthFailover:
         assert {r.model for r in results} == {"live-c"}
 
     @pytest.mark.asyncio
-    async def test_the_probe_forwards_input_so_window_checks_run_on_input_only_surfaces(
-        self, mock_router_instance
-    ):
+    async def test_the_probe_forwards_input_so_window_checks_run_on_input_only_surfaces(self, mock_router_instance):
         """The Responses API carries its prompt as `input`, never as messages. The owner only
         runs its context-window pre-call check when one of them is present, so dropping `input`
         would silently skip window filtering on that whole surface."""
@@ -12908,9 +12984,7 @@ class TestTierHealthFailover:
         ), "the eligibility probe must forward `input` to the owner"
 
     @pytest.mark.asyncio
-    async def test_a_group_the_router_has_no_deployment_for_is_not_a_failover_target(
-        self, mock_router_instance
-    ):
+    async def test_a_group_the_router_has_no_deployment_for_is_not_a_failover_target(self, mock_router_instance):
         """The owner answers an unconfigured group with BadRequestError. Reading that as live
         would both skip failover off it and let it be chosen as a substitute."""
         router = self._router(
@@ -13099,9 +13173,7 @@ class TestClassifierVision:
         routed as default_fallback on text the request never contained.
         """
         router = self._router(mock_router_instance, vision={"enabled": True})
-        response = await router.async_pre_routing_hook(
-            model="m", request_kwargs={}, messages=self._turn(IMG_PART)
-        )
+        response = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self._turn(IMG_PART))
         assert response.routing_decision["cause"] == "llm_classifier"
         assert response.model == "t-complex"
         assert [block["type"] for block in self._classifier_user_content(mock_router_instance)] == [
@@ -13112,9 +13184,7 @@ class TestClassifierVision:
     @pytest.mark.asyncio
     async def test_image_only_turn_still_falls_back_when_vision_is_off(self, mock_router_instance):
         router = self._router(mock_router_instance, vision={"enabled": False})
-        response = await router.async_pre_routing_hook(
-            model="m", request_kwargs={}, messages=self._turn(IMG_PART)
-        )
+        response = await router.async_pre_routing_hook(model="m", request_kwargs={}, messages=self._turn(IMG_PART))
         assert response.routing_decision["cause"] == "default_fallback"
         mock_router_instance.acompletion.assert_not_awaited()
 
@@ -13184,9 +13254,7 @@ class TestClassifierVision:
         makes the image the only variable; a margin loose enough to leave the score undecided
         would pass whether or not the guard exists.
         """
-        router = self._router(
-            mock_router_instance, vision={"enabled": True}, classifier_type=classifier_type, **extra
-        )
+        router = self._router(mock_router_instance, vision={"enabled": True}, classifier_type=classifier_type, **extra)
         response = await router.async_pre_routing_hook(
             model="m", request_kwargs={}, messages=self._turn({"type": "text", "text": "what is this"}, IMG_PART)
         )
@@ -13200,9 +13268,7 @@ class TestClassifierVision:
         self, mock_router_instance, classifier_type, extra, short_circuit_cause
     ):
         """The negative class: same router, same text, no image, and the scorer still decides."""
-        router = self._router(
-            mock_router_instance, vision={"enabled": True}, classifier_type=classifier_type, **extra
-        )
+        router = self._router(mock_router_instance, vision={"enabled": True}, classifier_type=classifier_type, **extra)
         response = await router.async_pre_routing_hook(
             model="m", request_kwargs={}, messages=[{"role": "user", "content": "what is this"}]
         )
diff --git a/tests/test_litellm/router_utils/test_auto_router_tuning_baseline.py b/tests/test_litellm/router_utils/test_auto_router_tuning_baseline.py
index f686a62db76..75c115cd3ab 100644
--- a/tests/test_litellm/router_utils/test_auto_router_tuning_baseline.py
+++ b/tests/test_litellm/router_utils/test_auto_router_tuning_baseline.py
@@ -7,6 +7,7 @@ from typing import Final
 
 import pytest
 
+from litellm.router_strategy.complexity_router.config import ComplexityRouterConfig
 from litellm.router_utils.auto_router_tuning_baseline import (
     DEFAULT_TUNING_FINGERPRINT,
     HEURISTIC_V1_TUNING_FIELDS,
@@ -21,6 +22,33 @@ from litellm.router_utils.auto_router_tuning_baseline import (
 
 _TIERS = {"SIMPLE": "cheap", "MEDIUM": "mid", "COMPLEX": "strong", "REASONING": "top"}
 _ALT_TIERS = {**_TIERS, "COMPLEX": "other-strong"}
+_KEYWORD_DIMENSION: Final = {"name": "internalFrameworks", "weight": 0.7, "keywords": ["orbitmesh"]}
+_HISTORICAL_FINGERPRINTS: Final = (
+    ({}, "44136fa355b3678a1146ad16f7e8649e94fb4fc21fe77e8310c060f61caaff8a"),
+    (
+        {"custom_dimensions": [_KEYWORD_DIMENSION]},
+        "b5c3c3f3be6341a8a16148d68d9067e03f94ed01a0bbfcde7955763042744372",
+    ),
+    (
+        {"custom_dimensions": [{"name": "sqlDdl", "weight": 0.4, "patterns": [r"\bCREATE\s{1,4}TABLE\b"]}]},
+        "814ce0017fc7f60a160b262f658d910e9bdf784e6139a4ba4f1e2657aa203950",
+    ),
+    (
+        {
+            "tiers": _TIERS,
+            "dimension_weights": {"codePresence": 0.3},
+            "custom_dimensions": [
+                {
+                    "name": "internalFrameworks",
+                    "weight": 0.2,
+                    "keywords": ["orbitmesh", "fluxgate"],
+                    "patterns": [r"\bALTER\s{1,4}TABLE\b"],
+                }
+            ],
+        },
+        "38970dc9224e265ab38c89674563d8d0537822591f9239b45251db6f5ca6cc39",
+    ),
+)
 
 
 def _router(
@@ -77,6 +105,27 @@ class TestTuningFingerprint:
     def test_explicit_empty_tier_model_configs_follow_omission(self) -> None:
         assert tuning_fingerprint({"tier_model_configs": {}}) == DEFAULT_TUNING_FINGERPRINT
 
+    @pytest.mark.parametrize(("config", "fingerprint"), _HISTORICAL_FINGERPRINTS)
+    def test_fingerprints_recorded_before_scoring_mode_existed_are_preserved(
+        self, config: Mapping[str, object], fingerprint: str
+    ) -> None:
+        """Literal hashes captured from the merged implementation at 9bc9104102, before CustomDimension.scoring_mode."""
+        assert tuning_fingerprint(config) == fingerprint
+
+    def test_binary_scoring_mode_hashes_like_its_absence(self) -> None:
+        historical: Final = tuning_fingerprint({"custom_dimensions": [_KEYWORD_DIMENSION]})
+        explicit: Final = tuning_fingerprint({"custom_dimensions": [{**_KEYWORD_DIMENSION, "scoring_mode": "binary"}]})
+        reserialized: Final = ComplexityRouterConfig.model_validate(
+            {"custom_dimensions": [_KEYWORD_DIMENSION]}
+        ).model_dump(mode="json", include={"custom_dimensions"})
+        assert reserialized["custom_dimensions"][0]["scoring_mode"] == "binary"
+        assert reserialized["custom_dimensions"][0]["patterns"] == []
+        assert historical == explicit == tuning_fingerprint(reserialized)
+        assert (
+            tuning_fingerprint({"custom_dimensions": [{**_KEYWORD_DIMENSION, "scoring_mode": "match_count"}]})
+            != historical
+        )
+
     def test_tier_model_overrides_change_the_fingerprint(self) -> None:
         plain = tuning_fingerprint({"tiers": {"SIMPLE": "x"}})
         with_override = tuning_fingerprint(
@@ -218,15 +267,18 @@ class TestQuota:
             is None
         )
 
-    def test_custom_dimension_add_edit_and_revert_share_one_quota_slot(self) -> None:
+    @pytest.mark.parametrize(
+        "edit",
+        [
+            pytest.param({"weight": 0.9}, id="weight"),
+            pytest.param({"scoring_mode": "match_count"}, id="scoring-mode"),
+        ],
+    )
+    def test_custom_dimension_add_edit_and_revert_share_one_quota_slot(self, edit: Mapping[str, object]) -> None:
         baselines: Final = snapshot_tuning_baselines(())
         original: Final = _router("a", {})
-        config: Final = {
-            "custom_dimensions": [{"name": "internalFrameworks", "weight": 0.7, "keywords": ["orbitmesh"]}]
-        }
-        edited_config: Final = {
-            "custom_dimensions": [{"name": "internalFrameworks", "weight": 0.9, "keywords": ["orbitmesh"]}]
-        }
+        config: Final = {"custom_dimensions": [_KEYWORD_DIMENSION]}
+        edited_config: Final = {"custom_dimensions": [{**_KEYWORD_DIMENSION, **edit}]}
         added: Final = _router("a", config)
         edited: Final = _router("a", edited_config)
         second: Final = _router("b", config)
@@ -240,6 +292,13 @@ class TestQuota:
         assert mutable_tuned_identities((original,), baselines) == frozenset()
         assert tuning_quota_violation(candidate=second, others=(original,), baselines=baselines, limit=1) is None
 
+    def test_graded_dimension_recorded_at_snapshot_is_its_own_baseline(self) -> None:
+        graded: Final = _router("a", {"custom_dimensions": [{**_KEYWORD_DIMENSION, "scoring_mode": "match_count"}]})
+        baselines: Final = snapshot_tuning_baselines((graded,))
+        assert mutable_tuned_identities((graded,), baselines) == frozenset()
+        reverted_to_binary: Final = _router("a", {"custom_dimensions": [_KEYWORD_DIMENSION]})
+        assert mutable_tuned_identities((reverted_to_binary,), baselines) == {router_identity(graded)}
+
     def test_violation_message_names_the_limit_and_remedy(self) -> None:
         message = tuning_limit_violation(held=2, limit=1)
         assert message is not None
diff --git a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx
index 15cfff01766..111eeebe5a3 100644
--- a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx
@@ -44,8 +44,9 @@ import {
 } from "./ComplexityRouterConfig";
 
 const DEFAULT_SCORING_EXPLANATION =
-  "The router scores each request across 7 dimensions: token count, code presence, reasoning markers, technical " +
-  "terms, simple indicators, multi-step patterns, and question complexity. The weighted score determines the tier:";
+  "The router scores each request across 7 built-in dimensions: token count, code presence, reasoning markers, technical " +
+  "terms, simple indicators, multi-step patterns, and question complexity, plus any custom dimensions you add. " +
+  "The weighted score determines the tier:";
 
 const HEURISTIC_V2_EXPLANATION =
   "The router estimates success probability for all four tiers with the bundled calibrated model, then selects " +
diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx
index 2970e14b335..0c12cc0ba1a 100644
--- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.test.tsx
@@ -1521,14 +1521,16 @@ describe("ComplexityRouterConfig tier editing", () => {
     renderWithProviders(<ComplexityRouterConfig {...baseProps} value={customValue} onEditingTiersChange={vi.fn()} />);
     fireEvent.click(screen.getByText("Advanced: Classification Method"));
     expect(screen.queryByText("How Classification Works")).not.toBeInTheDocument();
-    expect(screen.queryByText("scores each request across 7 dimensions", { exact: false })).not.toBeInTheDocument();
+    expect(
+      screen.queryByText("scores each request across 7 built-in dimensions", { exact: false }),
+    ).not.toBeInTheDocument();
   });
 
   it("keeps the scorer card on a built-in router, whose tiers the score still decides", () => {
     renderWithProviders(<ComplexityRouterConfig {...baseProps} onEditingTiersChange={vi.fn()} />);
     fireEvent.click(screen.getByText("Advanced: Classification Method"));
     expect(screen.getByText("How Classification Works")).toBeInTheDocument();
-    expect(screen.getByText("scores each request across 7 dimensions", { exact: false })).toBeInTheDocument();
+    expect(screen.getByText("scores each request across 7 built-in dimensions", { exact: false })).toBeInTheDocument();
   });
 
   it("says why a custom row is blocked instead of only reddening its border", () => {
diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx
index d7df6ce33bb..de80e714e66 100644
--- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx
@@ -50,6 +50,7 @@ import EscalationKeywords from "./EscalationKeywords";
 import KeywordTierRules, { KeywordTierRule } from "./KeywordTierRules";
 import SemanticKeywordMatching from "./SemanticKeywordMatching";
 import { type DimensionWeights, type TierBoundaries, type TokenThresholds } from "./heuristic_scoring_knobs";
+import { type CustomDimensionRow } from "./custom_dimensions";
 import CompressionControls from "./CompressionControls";
 import { type AutoRouterCompressionState, DEFAULT_AUTO_ROUTER_COMPRESSION } from "./buildAutoRouterCompression";
 
@@ -432,6 +433,11 @@ export interface ComplexityRouterConfigValue {
   tier_boundaries?: TierBoundaries;
   token_thresholds?: TokenThresholds;
   dimension_weights?: DimensionWeights;
+  /**
+   * Operator-added scoring dimensions, each carrying its own inline weight. Undefined means the router has
+   * none and keeps the key out of the payload; an empty array is a real "the last row was removed" state.
+   */
+  custom_dimensions?: CustomDimensionRow[];
   /**
    * Score floor the reasoning-marker override must clear. Undefined keeps the key out of the payload, so the
    * floor tracks tier_boundaries.simple_medium; an explicit 0 is a real floor that promotes on the markers alone.
diff --git a/ui/litellm-dashboard/src/components/add_model/CustomDimensionRows.tsx b/ui/litellm-dashboard/src/components/add_model/CustomDimensionRows.tsx
new file mode 100644
index 00000000000..34d8370aafb
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/add_model/CustomDimensionRows.tsx
@@ -0,0 +1,136 @@
+import { Trash2 } from "lucide-react";
+import { useState } from "react";
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { Label } from "@/components/ui/label";
+import { Textarea } from "@/components/ui/textarea";
+import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select";
+import { Slider } from "@/components/ui/slider";
+import type { CustomDimensionRow } from "./custom_dimensions";
+
+const SCORING_MODES = [
+  { value: "binary", label: "Binary" },
+  { value: "match_count", label: "Match count" },
+] as const;
+
+interface Props {
+  rows: CustomDimensionRow[];
+  disabled: boolean;
+  onChange: (rows: CustomDimensionRow[]) => void;
+  onWeight: (id: string, weight: number) => void;
+  onAdd: () => void;
+  onRemove: (id: string) => void;
+}
+
+export default function CustomDimensionRows({ rows, disabled, onChange, onWeight, onAdd, onRemove }: Props) {
+  const [draft, setDraft] = useState<{ id: string; raw: string } | null>(null);
+  const update = (id: string, patch: Partial<CustomDimensionRow>) =>
+    onChange(rows.map((row) => (row.id === id ? { ...row, ...patch } : row)));
+  const editWeight = (id: string, raw: string) => {
+    setDraft({ id, raw });
+    if (raw.trim() && Number.isFinite(Number(raw))) onWeight(id, Number(raw));
+  };
+  return (
+    <div className="space-y-4">
+      {rows.map((row, index) => (
+        <fieldset key={row.id} className="min-w-0 space-y-3 rounded-md border p-3">
+          <legend className="float-left text-sm font-semibold">Custom dimension {index + 1}</legend>
+          <div className="flex items-center gap-2">
+            <Button
+              type="button"
+              variant="ghost"
+              size="sm"
+              className="text-destructive hover:text-destructive/80"
+              aria-label={`Remove custom dimension ${index + 1}`}
+              disabled={disabled}
+              onClick={() => onRemove(row.id)}
+            >
+              <Trash2 />
+              Remove
+            </Button>
+          </div>
+          <div className="space-y-1">
+            <Label htmlFor={`${row.id}-name`}>Name</Label>
+            <Input
+              id={`${row.id}-name`}
+              value={row.name}
+              maxLength={64}
+              onChange={(event) => update(row.id, { name: event.target.value })}
+            />
+          </div>
+          <div className="flex flex-wrap items-center gap-3">
+            <Label htmlFor={`${row.id}-weight`}>Weight</Label>
+            <Slider
+              min={0}
+              max={1}
+              step={0.01}
+              disabled={disabled}
+              value={[row.weight]}
+              className="min-w-24 flex-1"
+              aria-label={`${row.name || `Custom dimension ${index + 1}`} weight`}
+              onValueChange={(value) => onWeight(row.id, Array.isArray(value) ? value[0] : value)}
+            />
+            <Input
+              id={`${row.id}-weight`}
+              className="w-24"
+              inputMode="decimal"
+              disabled={disabled}
+              value={draft?.id === row.id ? draft.raw : Number(row.weight.toPrecision(6)).toString()}
+              onBlur={() => setDraft(null)}
+              onChange={(event) => editWeight(row.id, event.target.value)}
+            />
+          </div>
+          <div className="grid grid-cols-1 gap-3 sm:grid-cols-2">
+            {(["keywords", "patterns"] as const).map((field) => (
+              <div key={field} className="min-w-0 space-y-1">
+                <Label htmlFor={`${row.id}-${field}`}>
+                  {field === "keywords" ? "Keywords" : "Regex patterns"} (one per line)
+                </Label>
+                <Textarea
+                  id={`${row.id}-${field}`}
+                  rows={2}
+                  value={row[field]?.join("\n") ?? ""}
+                  onChange={(event) =>
+                    update(row.id, { [field]: event.target.value ? event.target.value.split("\n") : [] })
+                  }
+                />
+              </div>
+            ))}
+          </div>
+          <div className="space-y-1">
+            <Label htmlFor={`${row.id}-scoring`}>Scoring</Label>
+            <Select
+              items={SCORING_MODES}
+              value={row.scoring_mode ?? "binary"}
+              onValueChange={(mode) => {
+                if (mode === "binary" || mode === "match_count") update(row.id, { scoring_mode: mode });
+              }}
+            >
+              <SelectTrigger id={`${row.id}-scoring`}>
+                <SelectValue />
+              </SelectTrigger>
+              <SelectContent>
+                {SCORING_MODES.map((mode) => (
+                  <SelectItem key={mode.value} value={mode.value}>
+                    {mode.label}
+                  </SelectItem>
+                ))}
+              </SelectContent>
+            </Select>
+            <p className="text-xs text-muted-foreground">
+              Binary uses the full weight for any hit. Match count uses half for one distinct matcher and full weight
+              for two or more.
+            </p>
+          </div>
+        </fieldset>
+      ))}
+      <Button type="button" variant="outline" size="sm" disabled={disabled || rows.length >= 16} onClick={onAdd}>
+        Add custom dimension
+      </Button>
+      <p className="text-xs text-muted-foreground">
+        Keywords match the current ask. Regex scans its first 2,048 characters and permits bounded single-character
+        repeats up to 64. The proxy validates patterns on save.
+      </p>
+    </div>
+  );
+}
diff --git a/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.integration.test.tsx b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.integration.test.tsx
new file mode 100644
index 00000000000..9c4600e3b97
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.integration.test.tsx
@@ -0,0 +1,90 @@
+import { useState } from "react";
+import { beforeEach, describe, expect, it, vi } from "vitest";
+import userEvent from "@testing-library/user-event";
+import { fireEvent, renderWithProviders, screen, testQueryClient, chooseSelectOption } from "../../../tests/test-utils";
+import { SHIPPED_SCORER_DEFAULTS } from "../../../tests/mocks/complexityScorerDefaults";
+import HeuristicScoringConfig from "./HeuristicScoringConfig";
+import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig";
+import { getComplexityScorerDefaults } from "@/components/networking";
+
+vi.mock("@/components/networking", () => ({ getComplexityScorerDefaults: vi.fn() }));
+
+const base: ComplexityRouterConfigValue = {
+  classifier_type: "heuristic",
+  tiers: { SIMPLE: [], MEDIUM: [], COMPLEX: [], REASONING: [] },
+};
+function Editor({ initial = base }: { initial?: ComplexityRouterConfigValue }) {
+  const [value, setValue] = useState(initial);
+  return (
+    <>
+      <HeuristicScoringConfig value={value} onChange={setValue} />
+      <output aria-label="Draft config">{JSON.stringify(value)}</output>
+    </>
+  );
+}
+const draft = (): ComplexityRouterConfigValue => JSON.parse(screen.getByLabelText("Draft config").textContent!);
+
+beforeEach(() => {
+  testQueryClient.clear();
+  vi.mocked(getComplexityScorerDefaults).mockResolvedValue(SHIPPED_SCORER_DEFAULTS);
+});
+
+describe("combined heuristic editor", () => {
+  it("adds and edits a graded dimension, rebalances builtins, preserves matcher-only edits, and removes it", async () => {
+    const user = userEvent.setup();
+    renderWithProviders(<Editor />);
+    await user.click(screen.getByText("Advanced scoring"));
+    expect(await screen.findByRole("button", { name: "Restore default weights" })).toBeDisabled();
+    await user.click(screen.getByRole("button", { name: "Add custom dimension" }));
+    expect(screen.getByRole("button", { name: "Restore default weights" })).toBeEnabled();
+    expect(draft().dimension_weights?.codePresence).toBeCloseTo(0.27, 12);
+    expect(draft().custom_dimensions?.[0].scoring_mode).toBe("match_count");
+    expect(screen.getByRole("group", { name: "Custom dimension 1" })).toContainElement(
+      screen.getByLabelText("Name", { exact: true }),
+    );
+    fireEvent.change(screen.getByLabelText("Name", { exact: true }), { target: { value: "domain" } });
+    fireEvent.change(screen.getByLabelText("Keywords (one per line)"), { target: { value: "orbitmesh\nfluxgate" } });
+    fireEvent.change(screen.getByLabelText("Weight", { exact: true }), { target: { value: "0.2" } });
+    expect(screen.getByLabelText("Code presence", { exact: true })).toHaveValue("0.24");
+    expect(screen.getByTestId("dimension-weight-total")).toHaveTextContent("total 1.00");
+    const beforeMatchers = draft().dimension_weights;
+    fireEvent.change(screen.getByLabelText("Regex patterns (one per line)"), { target: { value: "abc" } });
+    await chooseSelectOption(user, screen.getByLabelText("Scoring"), "Binary");
+    expect(draft().dimension_weights).toEqual(beforeMatchers);
+    expect(draft().custom_dimensions?.[0].scoring_mode).toBe("binary");
+    await user.click(screen.getByRole("button", { name: "Remove custom dimension 1" }));
+    expect(draft().custom_dimensions).toBeUndefined();
+    expect(draft().dimension_weights?.codePresence).toBeCloseTo(0.3, 12);
+  });
+
+  it("preserves a legacy vector on load and resets both fields only when requested", async () => {
+    const legacy = {
+      ...base,
+      dimension_weights: { codePresence: 0.4 },
+      custom_dimensions: [{ id: "stored-0", name: "domain", weight: 0.7, keywords: ["a"] }],
+    };
+    renderWithProviders(<Editor initial={legacy} />);
+    await userEvent.click(screen.getByText("Advanced scoring"));
+    expect(await screen.findByTestId("dimension-weight-total")).toHaveTextContent("total 1.10");
+    expect(draft()).toEqual(legacy);
+    expect(screen.getByLabelText("Token count", { exact: true })).toHaveValue("0");
+    await userEvent.click(screen.getByRole("button", { name: "Restore default weights" }));
+    expect(draft().custom_dimensions).toBeUndefined();
+    expect(draft().dimension_weights).toBeUndefined();
+  });
+
+  it("drops hidden custom drafts on a fallback-only weight edit so switching back cannot exceed the budget", async () => {
+    const initial: ComplexityRouterConfigValue = {
+      ...base,
+      classifier_type: "llm",
+      classifier_fallback: "heuristic",
+      custom_dimensions: [{ id: "a", name: "domain", weight: 0.7, keywords: ["a"] }],
+    };
+    renderWithProviders(<Editor initial={initial} />);
+    await userEvent.click(screen.getByText("Advanced scoring"));
+    fireEvent.change(await screen.findByLabelText("Code presence", { exact: true }), { target: { value: "0.5" } });
+    expect(draft().custom_dimensions).toBeUndefined();
+    expect(Object.values(draft().dimension_weights!).reduce((sum, weight) => sum + weight, 0)).toBeCloseTo(1, 12);
+    expect(screen.queryByRole("button", { name: "Add custom dimension" })).not.toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.test.tsx b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.test.tsx
index f5fb32bb654..61779318fff 100644
--- a/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.test.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.test.tsx
@@ -69,13 +69,12 @@ describe("HeuristicScoringConfig", () => {
     expect(onChange).not.toHaveBeenCalled();
   });
 
-  // min and max are inert attributes on a text input, so without the explicit clamp these would persist
-  // a weight of 999, or an infinite boundary, into the router config.
-  it.each([
-    ["Code presence", "999", 1],
-    ["Code presence", "-2", 0],
-    ["Long above", "100000", 100000],
-  ])("clamps %s = %s to %s", async (label, raw, expected) => {
+  it.each(["999", "-2"])("rejects an invalid weight %s instead of silently clamping", async (raw) => {
+    expect(await commit("Code presence", raw)).toBeUndefined();
+    expect(screen.getByRole("alert")).toHaveTextContent("Use a weight from 0 to 1");
+  });
+
+  it.each([["Long above", "100000", 100000]])("clamps %s = %s to %s", async (label, raw, expected) => {
     const next = await commit(label, raw);
     expect(
       { ...next?.dimension_weights, ...next?.token_thresholds }[label === "Long above" ? "complex" : "codePresence"],
@@ -264,6 +263,7 @@ describe("HeuristicScoringConfig degraded states", () => {
     await userEvent.click(screen.getByText("Advanced scoring"));
 
     expect(screen.getByLabelText("Code presence")).toHaveValue("0.5");
+    expect(screen.getByLabelText("Code presence")).toBeDisabled();
     expect(screen.queryByTestId("dimension-weight-total")).not.toBeInTheDocument();
   });
 });
diff --git a/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.tsx b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.tsx
index 540beff8177..4d59ac5bd33 100644
--- a/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/HeuristicScoringConfig.tsx
@@ -8,7 +8,14 @@ import { Input } from "@/components/ui/input";
 import { Label } from "@/components/ui/label";
 import { Slider } from "@/components/ui/slider";
 import { type ComplexityRouterConfigValue, heuristicScoringRole } from "./ComplexityRouterConfig";
-import { dimensionLabel, weightTotal } from "./heuristic_scoring_knobs";
+import {
+  dimensionLabel,
+  effectiveDimensionWeights,
+  rebalanceDimensionWeights,
+  type WeightEdit,
+} from "./heuristic_scoring_knobs";
+import CustomDimensionRows from "./CustomDimensionRows";
+import { customDimensionsError } from "./custom_dimensions";
 
 export type KnobGroup = "tier_boundaries" | "token_thresholds" | "dimension_weights";
 
@@ -54,7 +61,8 @@ const GROUPS: GroupSpec[] = [
   {
     group: "dimension_weights",
     title: "Dimension weights",
-    blurb: "How much each signal contributes to the score. Absolute multipliers, so the total need not be 1.00.",
+    blurb:
+      "Changing a weight rebalances the other built-in and custom weights to total 1.00. Save stores those values. Untouched routers keep their existing weights.",
     min: 0,
     max: 1,
     step: 0.01,
@@ -89,6 +97,19 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
   // The panel owns its own visibility: the scorer does not run at all when an LLM classifier
   // falls back to the default model, so there is nothing here to configure.
   const scorerRuns = heuristicScoringRole(value) !== "never";
+  const customEnabled = heuristicScoringRole(value) === "decides";
+  const customRows = customEnabled ? value.custom_dimensions : undefined;
+  const [weightError, setWeightError] = useState<string | null>(null);
+  const rowError = customDimensionsError(customRows);
+  const changeWeights = (edit: WeightEdit) => {
+    const result = rebalanceDimensionWeights(defaults?.dimension_weights, value.dimension_weights, customRows, edit);
+    if (!result.ok) {
+      setWeightError(result.error);
+      return;
+    }
+    setWeightError(null);
+    onChange({ ...value, dimension_weights: result.dimension_weights, custom_dimensions: result.custom_dimensions });
+  };
 
   // What an untouched override floor follows: the boundary in effect, override included, not the shipped one.
   const trackedFloor: number | undefined = { ...defaults?.tier_boundaries, ...value.tier_boundaries }.simple_medium;
@@ -102,6 +123,10 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
   const commit = (spec: GroupSpec, effective: Record<string, number>, key: string, raw: string) => {
     const parsed = Number(raw);
     if (raw.trim() === "" || !Number.isFinite(parsed)) return;
+    if (spec.group === "dimension_weights") {
+      changeWeights({ type: "set", target: { kind: "builtin", id: key }, weight: parsed });
+      return;
+    }
     const clamped = Math.min(spec.max ?? Infinity, Math.max(spec.min, parsed));
     onChange({
       ...value,
@@ -154,9 +179,19 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
                 </div>
               )}
               {GROUPS.map((spec) => {
-                const shipped = defaults?.[spec.group] ?? {};
-                const effective: Record<string, number> = { ...shipped, ...value[spec.group] };
+                const shipped = defaults?.[spec.group] ?? value[spec.group] ?? {};
+                const effective: Record<string, number> =
+                  spec.group === "dimension_weights"
+                    ? effectiveDimensionWeights(shipped, value.dimension_weights)
+                    : { ...shipped, ...value[spec.group] };
+                const total =
+                  Object.values(effective).reduce((sum, weight) => sum + weight, 0) +
+                  (customRows ?? []).reduce((sum, row) => sum + row.weight, 0);
                 const problem = warn(spec.group, effective);
+                const overridden =
+                  value[spec.group] !== undefined || (spec.withSlider && value.custom_dimensions !== undefined);
+                const resettable = spec.withSlider || overridden;
+                const scoringError = spec.withSlider ? weightError || rowError : null;
                 return (
                   <section key={spec.group} className="space-y-2">
                     <div className="flex items-center justify-between">
@@ -166,18 +201,26 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
                             alone would state a total that is not the router's. */}
                         {spec.withSlider && defaults !== undefined && (
                           <span className="text-xs text-muted-foreground" data-testid="dimension-weight-total">
-                            total {weightTotal(effective).toFixed(2)}
+                            total {total.toFixed(2)}
                           </span>
                         )}
                       </div>
-                      {value[spec.group] !== undefined && (
+                      {resettable && (
                         <Button
                           type="button"
                           variant="link"
                           size="xs"
-                          onClick={() => onChange({ ...value, [spec.group]: undefined })}
+                          disabled={!overridden}
+                          onClick={() => {
+                            setWeightError(null);
+                            onChange({
+                              ...value,
+                              [spec.group]: undefined,
+                              ...(spec.withSlider && { custom_dimensions: undefined }),
+                            });
+                          }}
                         >
-                          Reset to defaults
+                          {spec.withSlider ? "Restore default weights" : "Reset to defaults"}
                         </Button>
                       )}
                     </div>
@@ -196,6 +239,7 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
                               min={spec.min}
                               max={spec.max}
                               step={spec.step}
+                              disabled={defaults === undefined}
                               value={[effective[key]]}
                               onValueChange={(next) =>
                                 commit(spec, effective, key, String(Array.isArray(next) ? next[0] : next))
@@ -209,7 +253,8 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
                             type="text"
                             inputMode="decimal"
                             className={spec.withSlider ? "w-24" : "w-28"}
-                            value={draft?.id === id ? draft.raw : String(effective[key])}
+                            disabled={spec.withSlider && defaults === undefined}
+                            value={draft?.id === id ? draft.raw : Number(effective[key].toPrecision(6)).toString()}
                             onChange={(event) => {
                               setDraft({ id, raw: event.target.value });
                               commit(spec, effective, key, event.target.value);
@@ -220,6 +265,28 @@ const HeuristicScoringConfig: React.FC<HeuristicScoringConfigProps> = ({ value,
                       );
                     })}
 
+                    {spec.withSlider && customEnabled && (
+                      <CustomDimensionRows
+                        rows={customRows ?? []}
+                        disabled={defaults === undefined}
+                        onChange={(rows) => onChange({ ...value, custom_dimensions: rows })}
+                        onWeight={(id, weight) =>
+                          changeWeights({ type: "set", target: { kind: "custom", id }, weight })
+                        }
+                        onAdd={() =>
+                          changeWeights({
+                            type: "add",
+                            row: { id: crypto.randomUUID(), name: "", weight: 0.1, scoring_mode: "match_count" },
+                          })
+                        }
+                        onRemove={(id) => changeWeights({ type: "remove", id })}
+                      />
+                    )}
+                    {scoringError && (
+                      <p className="text-xs text-destructive" role="alert">
+                        {scoringError}
+                      </p>
+                    )}
                     {problem && (
                       <p className="text-xs font-medium text-destructive" role="alert">
                         {problem}
diff --git a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx
index 2d0d6bc2fd8..cf65a093dfb 100644
--- a/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/add_auto_router_tab.tsx
@@ -25,12 +25,14 @@ import ComplexityRouterConfig, {
   ComplexityRouterConfigValue,
   effectiveClassifierType,
   usesLlmClassifier,
+  heuristicScoringRole,
   DEFAULT_ADAPTIVE_WEIGHTS,
   DEFAULT_SESSION_AFFINITY,
   DEFAULT_DEPLOYMENT_AFFINITY,
   DEFAULT_TIER_DISTANCE_PENALTY,
 } from "./ComplexityRouterConfig";
 import { KeywordTierRule } from "./KeywordTierRules";
+import { customDimensionsError } from "./custom_dimensions";
 import { DEFAULT_ESCALATION_KEYWORDS } from "./EscalationKeywords";
 import {
   type AutoRouterCompressionState,
@@ -139,6 +141,7 @@ export const getSubmitBlockedReason = (
     getPlanModeTierError(config.plan_mode_min_tier, activeTierRows(config)) ??
     getKeywordTierRulesError(keywordTierRules, activeTierRows(config)) ??
     getClassifierModelError(config) ??
+    (heuristicScoringRole(config) === "decides" ? customDimensionsError(config.custom_dimensions) : null) ??
     getClassifierReasoningEffortError(config, modelInfo) ??
     getReferencedModelsError(referencedModelsParams, availability)
   );
@@ -411,6 +414,7 @@ const AddAutoRouterTab: React.FC<AddAutoRouterTabProps> = ({
     tierBoundaries: complexityRouterConfig.tier_boundaries,
     tokenThresholds: complexityRouterConfig.token_thresholds,
     dimensionWeights: complexityRouterConfig.dimension_weights,
+    customDimensions: complexityRouterConfig.custom_dimensions,
     reasoningOverrideMinScore: complexityRouterConfig.reasoning_override_min_score,
     enableContextWindowEscalation: complexityRouterConfig.enable_context_window_escalation,
     contextWindowEscalationBuffer: complexityRouterConfig.context_window_escalation_buffer,
diff --git a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts
index baf04822e4f..9973aec7616 100644
--- a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts
+++ b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts
@@ -704,6 +704,49 @@ describe("buildComplexityRouterConfig scorer knobs", () => {
     expect(buildComplexityRouterConfig(tuned).tier_boundaries).toEqual(BOUNDARIES);
   });
 
+  it("serializes custom rows without changing weights, matcher order, or optional-field absence", () => {
+    const weights = { codePresence: 0.12345678901234568, unknownStoredWeight: 9 };
+    const dimension = { name: "internalFrameworks", weight: 0.3765432109876543, keywords: ["ORBITMESH", "fluxgate"] };
+    const graded = { name: "sqlDdl", weight: 0.5, patterns: ["create table"], scoring_mode: "match_count" as const };
+    const payload = buildComplexityRouterConfig({
+      ...baseParams,
+      dimensionWeights: weights,
+      customDimensions: [
+        { id: "row-1", ...dimension },
+        { id: "row-2", ...graded },
+      ],
+    });
+    expect(payload.dimension_weights).toEqual(weights);
+    expect(payload.custom_dimensions).toEqual([dimension, graded]);
+    expect(buildComplexityRouterConfig(baseParams)).not.toHaveProperty("custom_dimensions");
+    expect(buildComplexityRouterConfig({ ...baseParams, customDimensions: [] }).custom_dimensions).toEqual([]);
+  });
+
+  it.each([
+    ["heuristic", undefined, true],
+    ["heuristic_first", "heuristic", true],
+    ["hybrid", "heuristic", true],
+    ["llm", "heuristic", false],
+    ["custom", "heuristic", false],
+    ["llm", "default_model", false],
+    ["custom", "default_model", false],
+    ["heuristic_v2", undefined, false],
+  ] as const)(
+    "%s with fallback %s only emits custom dimensions when its scorer decides",
+    (classifierType, classifierFallback, emits) => {
+      const dimension = { name: "d", weight: 0.4, keywords: ["orbitmesh"] };
+      const params = {
+        ...baseParams,
+        classifierType,
+        classifierFallback,
+        customDimensions: [{ id: "row", ...dimension }],
+      };
+      const payload = buildComplexityRouterConfig(params);
+      if (emits) expect(payload.custom_dimensions).toEqual([dimension]);
+      else expect(payload).not.toHaveProperty("custom_dimensions");
+    },
+  );
+
   it("drops them when the classifier falls back to the default model and nothing is scored", () => {
     expect(buildComplexityRouterConfig(llmWithDefaultFallback)).not.toHaveProperty("tier_boundaries");
   });
@@ -1060,6 +1103,7 @@ describe("buildComplexityRouterConfig with an edited tier set", () => {
       tierBoundaries: { simple_medium: 0.1, medium_complex: 0.25, complex_reasoning: 0.5 },
       tokenThresholds: { short: 1, long: 2 },
       dimensionWeights: { length: 1 },
+      customDimensions: [{ id: "row-1", name: "sqlDdl", weight: 0.4, keywords: ["orbitmesh"] }],
       reasoningOverrideMinScore: 0.5,
       heuristicFirstMaxTier: "SIMPLE",
       hybridBoundaryMargin: 0.03,
@@ -1068,7 +1112,8 @@ describe("buildComplexityRouterConfig with an edited tier set", () => {
       stallEscalationWindow: 6,
       stallEscalationRepeatThreshold: 3,
     };
-    const emittingType = key === "heuristic_first_max_tier" ? "heuristic_first" : "llm";
+    // custom_dimensions only ever ship when the scorer decides, so "llm" cannot prove it emits.
+    const emittingType = key === "heuristic_first_max_tier" || key === "custom_dimensions" ? "heuristic_first" : "llm";
     const typeForKey = key === "hybrid_boundary_margin" ? "hybrid" : emittingType;
     expect(buildComplexityRouterConfig({ ...baseParams, ...loaded, classifierType: typeForKey })).toHaveProperty(key);
     expect(build(loaded)).not.toHaveProperty(key);
diff --git a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts
index 7769fb832fe..d04d48198a8 100644
--- a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts
+++ b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.ts
@@ -11,6 +11,7 @@ import {
   tierRowByName,
 } from "./tier_rows";
 import { emptyKeywordTierRuleIndexes, serializeKeywordTierRules } from "./complexity_router_keywords";
+import { type CustomDimension, type CustomDimensionRow, serializeCustomDimensions } from "./custom_dimensions";
 import {
   TierModelParams,
   TierModelParamsByTier,
@@ -92,6 +93,7 @@ interface ScorerKnobInputs {
   tierBoundaries: TierBoundaries | undefined;
   tokenThresholds: TokenThresholds | undefined;
   dimensionWeights: DimensionWeights | undefined;
+  customDimensions: CustomDimensionRow[] | undefined;
   reasoningOverrideMinScore: number | undefined;
 }
 
@@ -106,16 +108,23 @@ const scorerKnobPayload = ({
   tierBoundaries,
   tokenThresholds,
   dimensionWeights,
+  customDimensions,
   reasoningOverrideMinScore,
-}: ScorerKnobInputs) =>
-  heuristicScoringRoleFor(classifierType, classifierFallback) === "never"
+}: ScorerKnobInputs) => {
+  const role = heuristicScoringRoleFor(classifierType, classifierFallback);
+  return role === "never"
     ? {}
     : {
         ...(tierBoundaries && { tier_boundaries: tierBoundaries }),
         ...(tokenThresholds && { token_thresholds: tokenThresholds }),
         ...(dimensionWeights && { dimension_weights: dimensionWeights }),
+        // Only a scorer that decides accepts these; the backend rejects them on every other
+        // classifier, so a fallback-only router must not carry rows a switch left behind.
+        ...(role === "decides" &&
+          customDimensions !== undefined && { custom_dimensions: serializeCustomDimensions(customDimensions) }),
         ...(reasoningOverrideMinScore !== undefined && { reasoning_override_min_score: reasoningOverrideMinScore }),
       };
+};
 
 export interface BuildComplexityRouterConfigParams {
   tiers: ComplexityTiers;
@@ -155,6 +164,7 @@ export interface BuildComplexityRouterConfigParams {
   tierBoundaries?: TierBoundaries;
   tokenThresholds?: TokenThresholds;
   dimensionWeights?: DimensionWeights;
+  customDimensions?: CustomDimensionRow[];
   reasoningOverrideMinScore?: number;
   tierModelParams?: TierModelParamsByTier;
   enableContextWindowEscalation?: boolean;
@@ -219,6 +229,7 @@ export interface ComplexityRouterConfigPayload {
   tier_boundaries?: TierBoundaries;
   token_thresholds?: TokenThresholds;
   dimension_weights?: DimensionWeights;
+  custom_dimensions?: CustomDimension[];
   reasoning_override_min_score?: number;
   enable_context_window_escalation?: boolean;
   context_window_escalation_buffer?: number;
@@ -496,6 +507,7 @@ export const buildComplexityRouterConfig = ({
   tierBoundaries,
   tokenThresholds,
   dimensionWeights,
+  customDimensions,
   reasoningOverrideMinScore,
   tierModelParams,
   enableContextWindowEscalation,
@@ -517,6 +529,7 @@ export const buildComplexityRouterConfig = ({
     tierBoundaries,
     tokenThresholds,
     dimensionWeights,
+    customDimensions,
     reasoningOverrideMinScore,
   };
   const scorerKnobs = scorerKnobPayload(scorerInputs);
diff --git a/ui/litellm-dashboard/src/components/add_model/custom_dimensions.test.ts b/ui/litellm-dashboard/src/components/add_model/custom_dimensions.test.ts
new file mode 100644
index 00000000000..3c6060fd083
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/add_model/custom_dimensions.test.ts
@@ -0,0 +1,41 @@
+import { describe, expect, it } from "vitest";
+import { customDimensionsError, hydrateCustomDimensions, serializeCustomDimensions } from "./custom_dimensions";
+
+describe("custom dimension drafts", () => {
+  it.each([
+    [[{ name: "domain", weight: 0.2, keywords: [" orbitmesh "] }]],
+    [[{ name: "domain", weight: 0.2, patterns: ["abc"], keywords: [], scoring_mode: "binary" }]],
+    [[{ name: "domain", weight: 0.2, keywords: ["a", "b"], scoring_mode: "match_count" }]],
+    [[]],
+  ])("round-trips stored optional fields and matcher text without normalization: %j", (raw) => {
+    const rows = hydrateCustomDimensions(raw);
+    expect(rows).toBeDefined();
+    expect(serializeCustomDimensions(rows!)).toEqual(raw);
+  });
+
+  it("does not insert a default mode or custom list on load", () => {
+    expect(hydrateCustomDimensions(undefined)).toBeUndefined();
+    const rows = hydrateCustomDimensions([{ name: "domain", weight: 0.2, keywords: ["a"] }])!;
+    expect(rows[0].scoring_mode).toBeUndefined();
+    expect(customDimensionsError(rows)).toBeNull();
+  });
+
+  it.each([
+    { name: "", weight: 0.2, keywords: ["a"] },
+    { name: "codePresence", weight: 0.2, keywords: ["a"] },
+    { name: "bad name", weight: 0.2, keywords: ["a"] },
+    { name: "domain", weight: 0, keywords: ["a"] },
+    { name: "domain", weight: 0.2, keywords: [] },
+    { name: "domain", weight: 0.2, keywords: [" "] },
+    { name: "domain", weight: 0.2, keywords: ["a".repeat(257)] },
+  ])("rejects an invalid draft %j", (row) => {
+    expect(customDimensionsError([{ ...row, id: "draft" }])).not.toBeNull();
+  });
+
+  it("rejects duplicate names and aggregate matcher limits", () => {
+    const row = { id: "a", name: "domain", weight: 0.2, keywords: ["a"] };
+    expect(customDimensionsError([row, { ...row, id: "b", name: "DOMAIN" }])).not.toBeNull();
+    expect(customDimensionsError([{ ...row, keywords: Array(33).fill("a") }])).not.toBeNull();
+    expect(customDimensionsError([{ ...row, keywords: Array(32).fill("a".repeat(256)) }])).not.toBeNull();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/add_model/custom_dimensions.ts b/ui/litellm-dashboard/src/components/add_model/custom_dimensions.ts
new file mode 100644
index 00000000000..8cf5a16c303
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/add_model/custom_dimensions.ts
@@ -0,0 +1,54 @@
+import { z } from "zod";
+import { DIMENSION_LABELS } from "./heuristic_scoring_knobs";
+
+const customDimensionShape = {
+  name: z.string(),
+  weight: z.number(),
+  keywords: z.array(z.string()).optional(),
+  patterns: z.array(z.string()).optional(),
+  scoring_mode: z.enum(["binary", "match_count"]).optional(),
+};
+const customDimensionSchema = z.object(customDimensionShape);
+
+export type CustomDimension = z.infer<typeof customDimensionSchema>;
+export type CustomDimensionRow = CustomDimension & { id: string };
+
+export const hydrateCustomDimensions = (raw: unknown): CustomDimensionRow[] | undefined => {
+  if (raw === undefined) return undefined;
+  const parsed = z.array(customDimensionSchema).safeParse(raw);
+  return parsed.success ? parsed.data.map((row, index) => ({ ...row, id: `stored-${index}` })) : undefined;
+};
+
+export const serializeCustomDimensions = (rows: CustomDimensionRow[]): CustomDimension[] =>
+  rows.map(({ id: _id, ...dimension }) => dimension);
+
+export const customDimensionsError = (
+  rows: CustomDimensionRow[] | undefined,
+  builtinNames: string[] = Object.keys(DIMENSION_LABELS),
+): string | null => {
+  if (!rows) return null;
+  if (rows.length > 16) return "A router can have at most 16 custom dimensions";
+  const names = rows.map((row) => row.name.toLowerCase());
+  for (const [index, row] of rows.entries()) {
+    const prefix = `Custom dimension ${index + 1}: `;
+    if (!/^[A-Za-z][A-Za-z0-9_]{0,63}$/.test(row.name))
+      return (
+        prefix + "use a name starting with a letter, followed by letters, numbers or underscores (64 characters max)"
+      );
+    if (builtinNames.some((name) => name.toLowerCase() === row.name.toLowerCase()))
+      return prefix + "choose a name that is not already a built-in weight";
+    if (names.indexOf(row.name.toLowerCase()) !== index) return prefix + "names must be unique";
+    if (!Number.isFinite(row.weight) || row.weight <= 0 || row.weight > 1)
+      return prefix + "weight must be greater than 0 and at most 1";
+    const matchers = [...(row.keywords ?? []), ...(row.patterns ?? [])];
+    if (!matchers.length || matchers.some((matcher) => !matcher.trim()))
+      return prefix + "add at least one nonblank keyword or pattern";
+    if (
+      matchers.length > 32 ||
+      matchers.some((matcher) => [...matcher].length > 256) ||
+      matchers.reduce((total, matcher) => total + [...matcher].length, 0) > 4096
+    )
+      return prefix + "use at most 32 matchers, 256 characters each and 4096 characters combined";
+  }
+  return null;
+};
diff --git a/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.test.ts b/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.test.ts
index ee1d819872b..ea785ddb594 100644
--- a/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.test.ts
+++ b/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.test.ts
@@ -3,6 +3,8 @@ import { describe, expect, it } from "vitest";
 import { heuristicScoringRoleFor } from "./ComplexityRouterConfig";
 import {
   dimensionLabel,
+  effectiveDimensionWeights,
+  rebalanceDimensionWeights,
   hydrateDimensionWeights,
   hydrateReasoningOverrideMinScore,
   hydrateTierBoundaries,
@@ -68,6 +70,123 @@ describe("hydrating the scorer knobs", () => {
   });
 });
 
+describe("rebalancing the complete weight vector", () => {
+  const defaults = {
+    codePresence: 0.3,
+    reasoningMarkers: 0.25,
+    technicalTerms: 0.25,
+    tokenCount: 0.1,
+    simpleIndicators: 0.05,
+    multiStepPatterns: 0.03,
+    questionComplexity: 0.02,
+  };
+  const row = { id: "domain", name: "domainMarkers", weight: 0.2, keywords: ["orbitmesh"] };
+  const success = (result: ReturnType<typeof rebalanceDimensionWeights>) => {
+    if (!result.ok) throw new Error(result.error);
+    const total =
+      Object.keys(defaults).reduce((sum, name) => sum + result.dimension_weights[name], 0) +
+      (result.custom_dimensions ?? []).reduce((sum, dimension) => sum + dimension.weight, 0);
+    expect(total).toBeCloseTo(1, 12);
+    return result;
+  };
+
+  it("adds a dimension, edits either kind, and redistributes its share on removal", () => {
+    const added = success(rebalanceDimensionWeights(defaults, undefined, undefined, { type: "add", row }));
+    expect(added.dimension_weights.codePresence).toBeCloseTo(0.24, 12);
+    expect(added.custom_dimensions?.[0].weight).toBe(0.2);
+    const edited = success(
+      rebalanceDimensionWeights(defaults, added.dimension_weights, added.custom_dimensions, {
+        type: "set",
+        target: { kind: "custom", id: row.id },
+        weight: 0.4,
+      }),
+    );
+    expect(edited.dimension_weights.codePresence).toBeCloseTo(0.18, 12);
+    const builtin = success(
+      rebalanceDimensionWeights(defaults, edited.dimension_weights, edited.custom_dimensions, {
+        type: "set",
+        target: { kind: "builtin", id: "codePresence" },
+        weight: 0.5,
+      }),
+    );
+    expect(builtin.dimension_weights.codePresence).toBe(0.5);
+    expect(builtin.custom_dimensions?.[0].weight).toBeCloseTo((0.4 * 0.5) / 0.82, 12);
+    const removed = success(
+      rebalanceDimensionWeights(defaults, added.dimension_weights, added.custom_dimensions, {
+        type: "remove",
+        id: row.id,
+      }),
+    );
+    expect(removed.custom_dimensions).toBeUndefined();
+    expect(removed.dimension_weights.codePresence).toBeCloseTo(defaults.codePresence, 12);
+  });
+
+  it("uses zero for omitted keys of an explicit map and preserves ignored keys", () => {
+    expect(effectiveDimensionWeights(defaults, { codePresence: 0.2 }).technicalTerms).toBe(0);
+    expect(effectiveDimensionWeights(defaults, undefined)).toEqual(defaults);
+    const result = success(
+      rebalanceDimensionWeights(defaults, { codePresence: 0.2, unknown: 7 }, undefined, { type: "add", row }),
+    );
+    expect(result.dimension_weights.unknown).toBe(7);
+    expect(result.dimension_weights.codePresence).toBeCloseTo(0.8, 12);
+    expect(result.dimension_weights.technicalTerms).toBe(0);
+  });
+
+  it("distributes an all-zero vector without inventing custom matchers", () => {
+    const result = success(rebalanceDimensionWeights(defaults, {}, undefined, { type: "add", row }));
+    expect(result.dimension_weights.codePresence).toBeCloseTo(0.8 / 7, 12);
+    expect(result.custom_dimensions).toEqual([row]);
+  });
+
+  it("keeps small positive custom weights and full precision", () => {
+    const tiny = { ...row, weight: 1e-8 };
+    const result = success(
+      rebalanceDimensionWeights(defaults, defaults, [tiny], {
+        type: "set",
+        target: { kind: "builtin", id: "codePresence" },
+        weight: 0.123456789,
+      }),
+    );
+    expect(result.dimension_weights.codePresence).toBe(0.123456789);
+    expect(result.custom_dimensions?.[0].weight).toBeGreaterThan(0);
+    expect(result.custom_dimensions?.[0].weight).toBeLessThan(0.01);
+  });
+
+  it.each([0, 1, Number.NaN, -0.1, 1.1])(
+    "rejects invalid or impossible custom weight %s without changing the input",
+    (weight) => {
+      const original = structuredClone(row);
+      const result = rebalanceDimensionWeights(defaults, defaults, [row, { ...row, id: "other" }], {
+        type: "set",
+        target: { kind: "custom", id: row.id },
+        weight,
+      });
+      expect(result.ok).toBe(false);
+      expect(row).toEqual(original);
+    },
+  );
+
+  it("allows one dimension to take the whole budget when no custom sibling needs a share", () => {
+    const result = success(
+      rebalanceDimensionWeights(defaults, defaults, [row], {
+        type: "set",
+        target: { kind: "custom", id: row.id },
+        weight: 1,
+      }),
+    );
+    expect(Object.values(result.dimension_weights).every((weight) => weight === 0)).toBe(true);
+    expect(result.custom_dimensions?.[0].weight).toBe(1);
+  });
+
+  it("refuses missing defaults and invalid stored weights, preserving absence on built-in edits", () => {
+    const edit = { type: "set", target: { kind: "builtin", id: "codePresence" }, weight: 0.2 } as const;
+    expect(rebalanceDimensionWeights(undefined, defaults, undefined, edit).ok).toBe(false);
+    expect(rebalanceDimensionWeights(defaults, { codePresence: -1 }, undefined, edit).ok).toBe(false);
+    expect(success(rebalanceDimensionWeights(defaults, undefined, undefined, edit)).custom_dimensions).toBeUndefined();
+    expect(success(rebalanceDimensionWeights(defaults, undefined, [], edit)).custom_dimensions).toEqual([]);
+  });
+});
+
 describe("heuristicScoringRoleFor", () => {
   it.each([
     ["heuristic", undefined, "decides"],
diff --git a/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.ts b/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.ts
index 1f44a479c60..3de4af92b9c 100644
--- a/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.ts
+++ b/ui/litellm-dashboard/src/components/add_model/heuristic_scoring_knobs.ts
@@ -1,3 +1,5 @@
+import type { CustomDimensionRow } from "./custom_dimensions";
+
 export type TierBoundaries = Record<string, number>;
 
 export type TokenThresholds = Record<string, number>;
@@ -27,8 +29,8 @@ const asRecord = (raw: unknown): Record<string, unknown> | undefined =>
 /**
  * Absent means the router is tracking the shipped defaults, so it must hydrate to undefined rather than to
  * a copy of them: hydrating defaults would make an untouched save write them out and pin the router to
- * whatever they were the day the modal was opened. A stored dict is kept exactly as stored, since the
- * backend fills in any key it omits at scoring time.
+ * whatever they were the day the modal was opened. A stored weight map replaces the defaults;
+ * missing dimension names score zero.
  */
 const hydrateNumericMap = (raw: unknown): Record<string, number> | undefined => {
   const stored = asRecord(raw);
@@ -53,3 +55,94 @@ export const hydrateReasoningOverrideMinScore = (raw: unknown): number | undefin
 
 export const weightTotal = (weights: DimensionWeights): number =>
   Math.round(Object.values(weights).reduce((total, weight) => total + weight, 0) * 100) / 100;
+
+export const effectiveDimensionWeights = (
+  defaults: DimensionWeights,
+  stored: DimensionWeights | undefined,
+): DimensionWeights =>
+  Object.fromEntries(
+    Object.keys(defaults).map((name) => [name, stored === undefined ? defaults[name] : stored[name] ?? 0]),
+  );
+
+type WeightTarget = { kind: "builtin" | "custom"; id: string };
+const weightValid = ({ kind, weight }: { kind: WeightTarget["kind"]; weight: number }): boolean => {
+  const inRange = Number.isFinite(weight) && weight >= 0 && weight <= 1;
+  return inRange && (kind === "builtin" || weight > 0);
+};
+export type WeightEdit =
+  | { type: "set"; target: WeightTarget; weight: number }
+  | { type: "add"; row: CustomDimensionRow }
+  | { type: "remove"; id: string };
+type WeightResult =
+  | { ok: true; dimension_weights: DimensionWeights; custom_dimensions: CustomDimensionRow[] | undefined }
+  | { ok: false; error: string };
+
+export const rebalanceDimensionWeights = (
+  defaults: DimensionWeights | undefined,
+  stored: DimensionWeights | undefined,
+  custom: CustomDimensionRow[] | undefined,
+  edit: WeightEdit,
+): WeightResult => {
+  if (!defaults || !Object.keys(defaults).length)
+    return { ok: false, error: "Load the shipped defaults before changing weights" };
+  const builtin = effectiveDimensionWeights(defaults, stored);
+  const rows =
+    edit.type === "add"
+      ? [...(custom ?? []), edit.row]
+      : (custom ?? []).filter((row) => edit.type !== "remove" || row.id !== edit.id);
+  const vector = [
+    ...Object.entries(builtin).map(([id, weight]) => ({ kind: "builtin" as const, id, weight })),
+    ...rows.map(({ id, weight }) => ({ kind: "custom" as const, id, weight })),
+  ];
+  if (!vector.every(weightValid))
+    return {
+      ok: false,
+      error: "Existing weights must be finite and nonnegative; custom weights must be greater than 0 and at most 1",
+    };
+  const target = edit.type === "set" ? edit.target : undefined;
+  const pinned = (entry: WeightTarget) =>
+    edit.type === "add"
+      ? entry.kind === "custom" && entry.id === edit.row.id
+      : entry.kind === target?.kind && entry.id === target.id;
+  if (edit.type === "set" && !vector.some(pinned)) return { ok: false, error: "The dimension is no longer available" };
+  const requestedWeight = (): number => {
+    if (edit.type === "set") return edit.weight;
+    return edit.type === "add" ? edit.row.weight : 0;
+  };
+  const weight = requestedWeight();
+  if (!weightValid({ kind: target?.kind ?? "builtin", weight }))
+    return { ok: false, error: "Use a weight from 0 to 1; custom dimensions must stay greater than 0" };
+  const others = vector.filter((entry) => !pinned(entry));
+  const total = others.reduce((sum, entry) => sum + entry.weight, 0);
+  if (!Number.isFinite(total)) return { ok: false, error: "Existing weights are too large to rebalance" };
+  const remainder = 1 - weight;
+  const builtinCount = others.filter((entry) => entry.kind === "builtin").length;
+  const redistributed = (entry: WeightTarget & { weight: number }): number => {
+    if (pinned(entry)) return weight;
+    if (total > 0) return remainder * (entry.weight / total);
+    return entry.kind === "builtin" ? remainder / builtinCount : 0;
+  };
+  const balanced = vector.map((entry) => ({ ...entry, weight: redistributed(entry) }));
+  const residual = 1 - balanced.reduce((sum, entry) => sum + entry.weight, 0);
+  const receiver = balanced
+    .filter((entry) => entry.kind === "builtin" && !pinned(entry) && entry.weight > 0)
+    .sort((a, b) => b.weight - a.weight)[0];
+  const corrected = balanced.map((entry) =>
+    entry === receiver ? { ...entry, weight: entry.weight + residual } : entry,
+  );
+  const offBudget = Math.abs(corrected.reduce((sum, entry) => sum + entry.weight, 0) - 1) > 1e-12;
+  if (!corrected.every(weightValid) || offBudget)
+    return { ok: false, error: "Leave a positive share for every custom dimension, or remove it first" };
+  const weights = Object.fromEntries(
+    corrected.filter((entry) => entry.kind === "builtin").map(({ id, weight }) => [id, weight]),
+  );
+  const customWeights = new Map(
+    corrected.filter((entry) => entry.kind === "custom").map(({ id, weight }) => [id, weight]),
+  );
+  const emptyRows = edit.type === "remove" ? undefined : custom;
+  return {
+    ok: true,
+    dimension_weights: { ...stored, ...weights },
+    custom_dimensions: rows.length ? rows.map((row) => ({ ...row, weight: customWeights.get(row.id)! })) : emptyRows,
+  };
+};
diff --git a/ui/litellm-dashboard/src/components/add_model/tier_rows.ts b/ui/litellm-dashboard/src/components/add_model/tier_rows.ts
index 5e2a32addee..d2cec15d75b 100644
--- a/ui/litellm-dashboard/src/components/add_model/tier_rows.ts
+++ b/ui/litellm-dashboard/src/components/add_model/tier_rows.ts
@@ -136,6 +136,7 @@ export const CUSTOM_TIER_RESTRICTIONS = {
       "tier_boundaries",
       "token_thresholds",
       "dimension_weights",
+      "custom_dimensions",
       "reasoning_override_min_score",
       "custom_technical_keywords",
     ],
diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts b/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts
index 0144dff3498..4a57e9d5e13 100644
--- a/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts
+++ b/ui/litellm-dashboard/src/components/edit_auto_router/build_updated_complexity_router_config.test.ts
@@ -63,6 +63,54 @@ describe("buildUpdatedComplexityRouterConfig keyword matching", () => {
     expect(result.some_future_backend_key).toEqual({ nested: true });
   });
 
+  describe("custom dimensions", () => {
+    const stored = [
+      { name: "internalFrameworks", weight: 0.7, keywords: ["orbitmesh"] },
+      { name: "sqlDdl", weight: 0.3, patterns: ["create table"], scoring_mode: "match_count" },
+    ];
+    const withDimensions = { ...STORED, custom_dimensions: stored };
+
+    it("round-trips stored rows through hydration and save without dropping or reshaping one", () => {
+      const hydrated = hydrateComplexityRouterConfig(withDimensions, null);
+      expect(hydrated.custom_dimensions).toEqual([
+        { id: "stored-0", ...stored[0] },
+        { id: "stored-1", ...stored[1] },
+      ]);
+      const saved = buildUpdatedComplexityRouterConfig(withDimensions, {
+        ...FORM_VALUE,
+        custom_dimensions: hydrated.custom_dimensions,
+      });
+      expect(saved.custom_dimensions).toEqual(stored);
+    });
+
+    it("is a managed key, so removing the last row does not resurrect the stored dimensions", () => {
+      expect(MANAGED_COMPLEXITY_ROUTER_KEYS.has("custom_dimensions")).toBe(true);
+      const saved = buildUpdatedComplexityRouterConfig(withDimensions, { ...FORM_VALUE, custom_dimensions: [] });
+      expect(saved.custom_dimensions).toEqual([]);
+    });
+
+    it("omits the key entirely when the editor never held rows, so an untouched router gains nothing", () => {
+      expect(hydrateComplexityRouterConfig(STORED, null).custom_dimensions).toBeUndefined();
+      expect(buildUpdatedComplexityRouterConfig(STORED, FORM_VALUE)).not.toHaveProperty("custom_dimensions");
+    });
+
+    it("drops rows a custom tier set forbids rather than sending them to a scorer that never runs", () => {
+      const customTierStored = storedCustomConfig({ custom_dimensions: stored });
+      const saved = buildUpdatedComplexityRouterConfig(customTierStored, {
+        tiers: { SIMPLE: [], MEDIUM: [], COMPLEX: [], REASONING: [] },
+        classifier_type: "llm" as const,
+        custom_tier_set: {
+          tiers: [
+            { id: "a", name: "CASUAL", definition: "small talk", models: ["gpt-4o-mini"] },
+            { id: "b", name: "AUDIT", definition: "security review", models: ["o1"] },
+          ],
+          fallback_tier_id: "a",
+        },
+      });
+      expect(saved).not.toHaveProperty("custom_dimensions");
+    });
+  });
+
   it("persists an edited keyword rule", () => {
     const result = buildUpdatedComplexityRouterConfig(STORED, FORM_VALUE, undefined, {
       ...hydratedState,
@@ -580,6 +628,7 @@ describe("managed keys survive an untouched open-and-save", () => {
     tier_boundaries: { simple_medium: 0.2, medium_complex: 0.4, complex_reasoning: 0.7 },
     token_thresholds: { simple: 20, complex: 500 },
     dimension_weights: { tokenCount: 0.1 },
+    custom_dimensions: [{ name: "domain", weight: 0.9, keywords: ["orbitmesh"] }],
     reasoning_override_min_score: 0.3,
     enable_context_window_escalation: false,
     context_window_escalation_buffer: 0.9,
diff --git a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx
index 3c0013e267e..b23040ab80c 100644
--- a/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx
+++ b/ui/litellm-dashboard/src/components/edit_auto_router/edit_auto_router_modal.tsx
@@ -48,6 +48,7 @@ import {
   hydrateAutoRouterCompression,
 } from "../add_model/buildAutoRouterCompression";
 import { hydrateKeywordTierRules } from "../add_model/complexity_router_keywords";
+import { customDimensionsError, hydrateCustomDimensions } from "../add_model/custom_dimensions";
 import {
   hydrateDimensionWeights,
   hydrateReasoningOverrideMinScore,
@@ -61,6 +62,7 @@ import ComplexityRouterConfig, {
   ClassifierType,
   ComplexityRouterConfigValue,
   ComplexityTiers,
+  heuristicScoringRole,
   DEFAULT_ADAPTIVE_WEIGHTS,
   DEFAULT_SESSION_AFFINITY,
   DEFAULT_DEPLOYMENT_AFFINITY,
@@ -109,6 +111,7 @@ export interface StoredComplexityRouterConfig {
   tier_boundaries?: unknown;
   token_thresholds?: unknown;
   dimension_weights?: unknown;
+  custom_dimensions?: unknown;
   reasoning_override_min_score?: unknown;
   session_affinity?: unknown;
   session_affinity_ttl_seconds?: unknown;
@@ -194,6 +197,7 @@ export const hydrateComplexityRouterConfig = (
     tier_boundaries: hydrateTierBoundaries(parsedConfig.tier_boundaries),
     token_thresholds: hydrateTokenThresholds(parsedConfig.token_thresholds),
     dimension_weights: hydrateDimensionWeights(parsedConfig.dimension_weights),
+    custom_dimensions: hydrateCustomDimensions(parsedConfig.custom_dimensions),
     reasoning_override_min_score: hydrateReasoningOverrideMinScore(parsedConfig.reasoning_override_min_score),
     session_affinity:
       typeof parsedConfig.session_affinity === "boolean" ? parsedConfig.session_affinity : DEFAULT_SESSION_AFFINITY,
@@ -264,6 +268,7 @@ export const MANAGED_COMPLEXITY_ROUTER_KEYS = new Set([
   "tier_boundaries",
   "token_thresholds",
   "dimension_weights",
+  "custom_dimensions",
   "reasoning_override_min_score",
   "enable_context_window_escalation",
   "context_window_escalation_buffer",
@@ -373,6 +378,7 @@ export const buildUpdatedComplexityRouterConfig = (
     tierBoundaries: value.tier_boundaries,
     tokenThresholds: value.token_thresholds,
     dimensionWeights: value.dimension_weights,
+    customDimensions: value.custom_dimensions,
     reasoningOverrideMinScore: value.reasoning_override_min_score,
     tierModelParams: value.tier_model_params,
     enableContextWindowEscalation: value.enable_context_window_escalation,
@@ -481,7 +487,10 @@ const EditAutoRouterModal: React.FC<EditAutoRouterModalProps> = ({
             : null) ?? getTierLabelsError(complexityRouterConfig.tier_labels)) ??
       getPlanModeTierError(complexityRouterConfig.plan_mode_min_tier, activeTierRows(complexityRouterConfig)) ??
       getKeywordTierRulesError(keywordTierRules, activeTierRows(complexityRouterConfig)) ??
-      getClassifierModelError(complexityRouterConfig);
+      getClassifierModelError(complexityRouterConfig) ??
+      (heuristicScoringRole(complexityRouterConfig) === "decides"
+        ? customDimensionsError(complexityRouterConfig.custom_dimensions)
+        : null);
 
   useEffect(() => {
     if (isVisible && modelData) {
@@ -601,7 +610,11 @@ const EditAutoRouterModal: React.FC<EditAutoRouterModalProps> = ({
         toast.fromError(tierSetError);
         return;
       }
-      const classifierError = getClassifierModelError(complexityRouterConfig);
+      const classifierError =
+        getClassifierModelError(complexityRouterConfig) ??
+        (heuristicScoringRole(complexityRouterConfig) === "decides"
+          ? customDimensionsError(complexityRouterConfig.custom_dimensions)
+          : null);
       if (classifierError) {
         setShowValidationErrors(true);
         toast.fromError(classifierError);
diff --git a/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts b/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts
index 344964c4434..5d1c7f73c71 100644
--- a/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts
+++ b/ui/litellm-dashboard/src/lib/autorouter_presets.test.ts
@@ -87,6 +87,19 @@ describe("autorouter_presets", () => {
     }
   });
 
+  it("keeps every preset free of custom dimensions, so applying one never adds scoring rows", () => {
+    for (const { complexity_router_config: config } of getAllPresets()) {
+      expect(config.custom_dimensions).toBeUndefined();
+    }
+    const config = getPresetByKey("anthropic_family")!.complexity_router_config;
+    expect(buildPresetPrefill(config, groupsOnly([])).complexityRouterConfig.custom_dimensions).toBeUndefined();
+  });
+
+  it("resets both scoring overrides when the form falls back to an empty prefill", () => {
+    expect(buildEmptyPrefill().complexityRouterConfig.custom_dimensions).toBeUndefined();
+    expect(buildEmptyPrefill().complexityRouterConfig.dimension_weights).toBeUndefined();
+  });
+
   it("carries a preset's session affinity idle window into the prefilled form state", () => {
     const config = getPresetByKey("anthropic_family")!.complexity_router_config;
     const prefill = buildPresetPrefill({ ...config, session_affinity_ttl_seconds: 300 }, groupsOnly([]));
@@ -96,6 +109,21 @@ describe("autorouter_presets", () => {
     ).toBeUndefined();
   });
 
+  it("carries a preset's stored weights and custom dimensions into the prefill without rebalancing them", () => {
+    const config = getPresetByKey("anthropic_family")!.complexity_router_config;
+    const weights = { codePresence: 0.4 };
+    const dimension = { name: "domain", weight: 0.9, keywords: ["orbitmesh"] };
+    const prefill = buildPresetPrefill(
+      { ...config, dimension_weights: weights, custom_dimensions: [dimension] },
+      groupsOnly([]),
+    ).complexityRouterConfig;
+    expect(prefill.dimension_weights).toEqual(weights);
+    expect(prefill.custom_dimensions).toEqual([{ ...dimension, id: "stored-0" }]);
+    const plain = buildPresetPrefill(config, groupsOnly([])).complexityRouterConfig;
+    expect(plain.dimension_weights).toBeUndefined();
+    expect(plain.custom_dimensions).toBeUndefined();
+  });
+
   it("keeps the model-family presets on the heuristic classifier", () => {
     for (const key of ["anthropic_family", "gemini_family", "openai_family"]) {
       expect(getPresetByKey(key)!.complexity_router_config.classifier_type).toBe("heuristic");
diff --git a/ui/litellm-dashboard/src/lib/autorouter_presets.ts b/ui/litellm-dashboard/src/lib/autorouter_presets.ts
index 2aafbfcfcd9..f2df55fc310 100644
--- a/ui/litellm-dashboard/src/lib/autorouter_presets.ts
+++ b/ui/litellm-dashboard/src/lib/autorouter_presets.ts
@@ -13,6 +13,13 @@ import {
 } from "@/components/add_model/ComplexityRouterConfig";
 import { KeywordTierRule } from "@/components/add_model/KeywordTierRules";
 import { hydrateKeywordTierRules } from "@/components/add_model/complexity_router_keywords";
+import { hydrateCustomDimensions } from "@/components/add_model/custom_dimensions";
+import {
+  hydrateDimensionWeights,
+  hydrateTierBoundaries,
+  hydrateTokenThresholds,
+  hydrateReasoningOverrideMinScore,
+} from "@/components/add_model/heuristic_scoring_knobs";
 import {
   TierModelParams,
   TierModelParamsByTier,
@@ -293,6 +300,11 @@ export const buildPresetPrefill = (
       tier_distance_penalty: config.tier_distance_penalty,
       adaptive_eligible: config.adaptive_eligible,
       return_raw_model_name: config.return_raw_model_name,
+      dimension_weights: hydrateDimensionWeights(config.dimension_weights),
+      custom_dimensions: hydrateCustomDimensions(config.custom_dimensions),
+      tier_boundaries: hydrateTierBoundaries(config.tier_boundaries),
+      token_thresholds: hydrateTokenThresholds(config.token_thresholds),
+      reasoning_override_min_score: hydrateReasoningOverrideMinScore(config.reasoning_override_min_score),
       enable_context_window_escalation: config.enable_context_window_escalation,
       context_window_escalation_buffer: config.context_window_escalation_buffer,
     },
diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts
index 232b366d8ce..d1431d8956f 100644
--- a/ui/litellm-dashboard/src/lib/http/schema.d.ts
+++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts
@@ -26591,6 +26591,13 @@ export interface components {
              * @default []
              */
             patterns: string[];
+            /**
+             * Scoring Mode
+             * @description 'binary' scores 1 when any matcher hits. 'match_count' scores 0.5 when one distinct matcher hits and 1 when two or more do; repeated occurrences of one matcher never raise it. Keywords are distinct case-insensitively, patterns by source, and a keyword and a pattern are always distinct from each other.
+             * @default binary
+             * @enum {string}
+             */
+            scoring_mode: "binary" | "match_count";
             /** Weight */
             weight: number;
         };
@@ -34909,7 +34916,7 @@ export interface components {
             context_window_escalation_buffer: number;
             /**
              * Custom Dimensions
-             * @description Named binary dimensions added to the heuristic-v1 score. Each contributes its inline weight once when any keyword matches the current ask or a case-insensitive regex matches its first 2048 characters. Regex quantifiers repeat one character or class at most 64 times. Unbounded quantifiers, repeated groups, backreferences and lookarounds are rejected. Conservative work limits include alternation paths, repeat lengths and subsequent matching: 2048 units per pattern, 8192 across the router. Only heuristic, heuristic_first and hybrid accept this field. Uses the existing heuristic tuning quota.
+             * @description Named dimensions added to the heuristic-v1 score. Each contributes its inline weight once when any keyword matches the current ask or a case-insensitive regex matches its first 2048 characters; scoring_mode 'match_count' instead grades half weight for one distinct matcher and full for two or more. Regex quantifiers repeat one character or class at most 64 times. Unbounded quantifiers, repeated groups, backreferences and lookarounds are rejected. Conservative work limits include alternation paths, repeat lengths and subsequent matching: 2048 units per pattern, 8192 across the router. Only heuristic, heuristic_first and hybrid accept this field. Uses the existing heuristic tuning quota.
              * @default []
              */
             custom_dimensions: components["schemas"]["CustomDimension"][];