diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py
index f323d78e22d..a92e5208471 100644
--- a/litellm/llms/fireworks_ai/cost_calculator.py
+++ b/litellm/llms/fireworks_ai/cost_calculator.py
@@ -68,24 +68,25 @@ def _resolve_model_info(model: str) -> ModelInfo:
 
 
 def _with_cache_read_fallback(model_info: ModelInfo) -> ModelInfo:
-    """Most fireworks_ai price-map entries publish no cache-read rate, and the provider bills
-    cached reads at the input rate. generic_cost_per_token prices a missing rate at $0, so the
-    fallback is written into a copy of the entry (the shared model-cost dict must not be
-    mutated), including inside off_peak_pricing so cached reads track the off-peak input rate
-    the way the previous calculator did."""
-    if model_info.get("cache_read_input_token_cost") is not None:
-        return model_info
+    """Most fireworks_ai price-map entries publish no cache-read rate though the provider bills
+    cached reads at the input rate; the shared map is never mutated, so a copy carries the fallback."""
     input_rate: Final = model_info.get("input_cost_per_token")
-    if input_rate is None:
+    if model_info.get("cache_read_input_token_cost") is not None or input_rate is None:
         return model_info
-    effective: Final[dict[str, object]] = dict(model_info)
-    effective["cache_read_input_token_cost"] = input_rate
     off_peak: Final = model_info.get("off_peak_pricing")
-    if off_peak is not None and "cache_read_input_token_cost" not in off_peak:
-        off_peak_copy: Final[dict[str, object]] = dict(off_peak)
-        off_peak_copy["cache_read_input_token_cost"] = off_peak_copy.get("input_cost_per_token", input_rate)
-        effective["off_peak_pricing"] = off_peak_copy
-    return cast(ModelInfo, effective)
+    if off_peak is None or "cache_read_input_token_cost" in off_peak:
+        return cast(ModelInfo, {**model_info, "cache_read_input_token_cost": input_rate})
+    return cast(
+        ModelInfo,
+        {
+            **model_info,
+            "cache_read_input_token_cost": input_rate,
+            "off_peak_pricing": {
+                **off_peak,
+                "cache_read_input_token_cost": off_peak.get("input_cost_per_token", input_rate),
+            },
+        },
+    )
 
 
 def cost_per_token(model: str, usage: Usage, current_time: datetime | None = None) -> tuple[float, float]:
diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py
index 0ad5eddcc7a..f461f17b627 100644
--- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py
+++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py
@@ -1,5 +1,6 @@
 import math
 from datetime import datetime, timezone
+from typing import Final
 
 import pytest
 
@@ -51,13 +52,16 @@ STANDARD_CACHE_READ_COST = 1.5e-08
 def _register_off_peak_model(
     off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST
 ) -> None:
-    litellm.model_cost[f"fireworks_ai/{OFF_PEAK_MODEL}"] = {
-        "litellm_provider": "fireworks_ai",
-        "mode": "chat",
-        "input_cost_per_token": STANDARD_INPUT_COST,
-        "output_cost_per_token": STANDARD_OUTPUT_COST,
-        "off_peak_pricing": off_peak_pricing,
-        **({} if cache_read_cost is None else {"cache_read_input_token_cost": cache_read_cost}),
+    litellm.model_cost = {  # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing
+        **litellm.model_cost,
+        f"fireworks_ai/{OFF_PEAK_MODEL}": {
+            "litellm_provider": "fireworks_ai",
+            "mode": "chat",
+            "input_cost_per_token": STANDARD_INPUT_COST,
+            "output_cost_per_token": STANDARD_OUTPUT_COST,
+            "off_peak_pricing": off_peak_pricing,
+            **({} if cache_read_cost is None else {"cache_read_input_token_cost": cache_read_cost}),
+        },
     }
 
 
@@ -145,20 +149,19 @@ COMPONENT_AUDIO_OUT_COST = 6e-06
 
 
 def test_cache_write_reasoning_and_audio_tokens_are_billed_at_their_component_rates():
-    """Regression (LIT-7837): the hand-rolled fireworks_ai calculator billed every
-    cache-creation, reasoning and audio token at $0. The shared calculator treats the
-    prompt detail counts as subsets of prompt_tokens and the completion detail counts as
-    subsets of completion_tokens, billing each remainder at the text rate."""
-    litellm.model_cost[f"fireworks_ai/{COMPONENT_MODEL}"] = {
-        "litellm_provider": "fireworks_ai",
-        "mode": "chat",
-        "input_cost_per_token": COMPONENT_INPUT_COST,
-        "output_cost_per_token": COMPONENT_OUTPUT_COST,
-        "cache_read_input_token_cost": COMPONENT_CACHE_READ_COST,
-        "cache_creation_input_token_cost": COMPONENT_CACHE_CREATION_COST,
-        "output_cost_per_reasoning_token": COMPONENT_REASONING_COST,
-        "input_cost_per_audio_token": COMPONENT_AUDIO_IN_COST,
-        "output_cost_per_audio_token": COMPONENT_AUDIO_OUT_COST,
+    litellm.model_cost = {  # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing
+        **litellm.model_cost,
+        f"fireworks_ai/{COMPONENT_MODEL}": {
+            "litellm_provider": "fireworks_ai",
+            "mode": "chat",
+            "input_cost_per_token": COMPONENT_INPUT_COST,
+            "output_cost_per_token": COMPONENT_OUTPUT_COST,
+            "cache_read_input_token_cost": COMPONENT_CACHE_READ_COST,
+            "cache_creation_input_token_cost": COMPONENT_CACHE_CREATION_COST,
+            "output_cost_per_reasoning_token": COMPONENT_REASONING_COST,
+            "input_cost_per_audio_token": COMPONENT_AUDIO_IN_COST,
+            "output_cost_per_audio_token": COMPONENT_AUDIO_OUT_COST,
+        },
     }
     usage = Usage(
         prompt_tokens=1000,
@@ -188,3 +191,20 @@ def test_cache_write_reasoning_and_audio_tokens_are_billed_at_their_component_ra
     )
     assert prompt_cost == pytest.approx(expected_prompt_cost)
     assert completion_cost == pytest.approx(expected_completion_cost)
+
+
+def test_an_entry_without_an_input_rate_gets_no_cache_read_fallback():
+    litellm.model_cost = {  # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing
+        **litellm.model_cost,  # pyright: ignore[reportUnknownMemberType]  # the SDK types model_cost as dict[Unknown, Unknown]
+        "fireworks_ai/accounts/fireworks/models/no-input-rate-test": {
+            "litellm_provider": "fireworks_ai",
+            "mode": "chat",
+            "output_cost_per_token": 2e-06,
+        },
+    }
+    usage: Final = _usage(prompt_tokens=1000, cached_tokens=300, completion_tokens=200)
+
+    prompt_cost, completion_cost = cost_per_token(model="accounts/fireworks/models/no-input-rate-test", usage=usage)
+
+    assert prompt_cost == 0
+    assert completion_cost == 200 * 2e-06