fix(token_counter): count images sent with detail original

OpenAI and Azure OpenAI accept "original" as an image detail value, but
token_counter only knew low, high and auto and raised on it. The router
then logged an ERROR per request and skipped the context window check.

Count original images with OpenAI's patch formula at full resolution,
capped at the largest image any model accepts.

litellm/constants.py

@@ -2,6 +2,7 @@ import os
 import re
 import sys
 from enum import Enum
+from fractions import Fraction
 from types import MappingProxyType
 from typing import Final, Literal
 
@@ -504,6 +505,10 @@ DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT: Final = get_env_int("DEFAULT_MOCK_RESP
 DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT: Final = get_env_int("DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT", 20)
 MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES: Final = get_env_int("MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES", 768)
 MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES: Final = get_env_int("MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES", 2000)
+# OpenAI's "original" image detail, https://developers.openai.com/api/docs/guides/images-vision (2026-10-19)
+OPENAI_IMAGE_PATCH_SIZE_PX: Final = 32
+OPENAI_IMAGE_MAX_PATCHES: Final = 30_000
+OPENAI_IMAGE_PATCH_TOKEN_MULTIPLIER: Final = Fraction(6, 5)
 # tiktoken's BPE merge loop is quadratic in the length of a single regex piece, so a long run of one
 # repeated character (dot leaders, whitespace, zero-padded base64) can take minutes on a multi-MB payload.
 # Encoding in chunks makes the cost linear, at a drift of at most ~1 token per chunk boundary. The upper

litellm/litellm_core_utils/token_counter.py

@@ -6,7 +6,7 @@ import math
 import struct
 from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence
 from itertools import accumulate
-from typing import TYPE_CHECKING, Final, Literal, cast
+from typing import TYPE_CHECKING, Final, Literal, TypeAlias, cast
 
 import anyio
 import anyio.lowlevel
@@ -29,6 +29,9 @@ from litellm.constants import (
     MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES,
     MAX_TILE_HEIGHT,
     MAX_TILE_WIDTH,
+    OPENAI_IMAGE_MAX_PATCHES,
+    OPENAI_IMAGE_PATCH_SIZE_PX,
+    OPENAI_IMAGE_PATCH_TOKEN_MULTIPLIER,
     PDF_DATA_URL_PREFIX,
     TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS,
     TOKEN_COUNTER_MAX_CONCURRENT_COUNTS,
@@ -63,6 +66,9 @@ from litellm.types.llms.openai import (
 )
 from litellm.types.utils import Message, SelectTokenizerResponse
 
+ImageDetail: TypeAlias = Literal["low", "high", "auto", "original"]
+IMAGE_DETAILS: Final[frozenset[ImageDetail]] = frozenset(("low", "high", "auto", "original"))
+
 
 def get_modified_max_tokens(
     model: str,
@@ -191,6 +197,11 @@ def calculate_tiles_needed(
     return total_tiles
 
 
+def original_detail_image_tokens(width: int, height: int) -> int:
+    patches: Final = math.ceil(width / OPENAI_IMAGE_PATCH_SIZE_PX) * math.ceil(height / OPENAI_IMAGE_PATCH_SIZE_PX)
+    return math.ceil(min(patches, OPENAI_IMAGE_MAX_PATCHES) * OPENAI_IMAGE_PATCH_TOKEN_MULTIPLIER)
+
+
 def high_detail_image_token_upper_bound(base_tokens: int = 85) -> int:
     largest_tile_count: Final = calculate_tiles_needed(
         MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES, MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES
@@ -305,7 +316,7 @@ def get_image_dimensions(
 
 def calculate_img_tokens(
     data,
-    mode: Literal["low", "high", "auto"] = "auto",
+    mode: ImageDetail = "auto",
     base_tokens: int = 85,  # openai default - https://openai.com/pricing
     use_default_image_token_count: bool = False,
 ):
@@ -314,7 +325,7 @@ def calculate_img_tokens(
 
     Args:
         data (str): The URL or base64 encoded string of the image.
-        mode (Literal["low", "high", "auto"]): The mode to use for calculating the number of tokens.
+        mode (ImageDetail): The mode to use for calculating the number of tokens.
         base_tokens (int): The base number of tokens for an image.
         use_default_image_token_count (bool): When True, will NOT make a GET request to the image URL and instead return the default image dimensions.
 
@@ -326,6 +337,8 @@ def calculate_img_tokens(
         return DEFAULT_IMAGE_TOKEN_COUNT
     if mode == "low" or mode == "auto":
         return base_tokens
+    elif mode == "original":
+        return original_detail_image_tokens(*get_image_dimensions(data=data))
     elif mode == "high":
         # Run the async function using the helper
         width, height = get_image_dimensions(
@@ -748,8 +761,8 @@ def _count_image_tokens(
     """
     if isinstance(image_url, dict):
         detail: Final = image_url.get("detail", "auto")
-        if detail not in ["low", "high", "auto"]:
-            raise ValueError(f"Invalid detail value: {detail}. Expected 'low', 'high', or 'auto'.")
+        if detail not in IMAGE_DETAILS:
+            raise ValueError(f"Invalid detail value: {detail}. Expected 'low', 'high', 'auto', or 'original'.")
         url: Final = image_url.get("url")
         if not url:
             raise ValueError("Missing required key 'url' in image_url dict.")

tests/unit/litellm_core_utils/test_token_counter.py

@@ -1635,6 +1635,25 @@ def test_high_detail_image_token_upper_bound_is_reached_by_the_largest_high_res_
     assert calculate_img_tokens(_png_data_url(1, 1), mode="high") < high_detail_image_token_upper_bound()
 
 
+def _original_detail_message(width: int, height: int) -> list[dict[str, object]]:
+    image: Final = {"url": _png_data_url(width, height), "detail": "original"}
+    return [{"role": "user", "content": [{"type": "image_url", "image_url": image}]}]
+
+
+def test_original_detail_image_is_counted_at_its_full_resolution() -> None:
+    large_image_tokens: Final = token_counter(messages=_original_detail_message(4000, 3000))
+
+    assert large_image_tokens > high_detail_image_token_upper_bound()
+    assert token_counter(messages=_original_detail_message(512, 512)) < large_image_tokens
+
+
+def test_original_detail_image_tokens_stop_growing_at_the_largest_accepted_image() -> None:
+    oversized: Final = calculate_img_tokens(_png_data_url(20_000, 20_000), mode="original")
+
+    assert calculate_img_tokens(_png_data_url(40_000, 40_000), mode="original") == oversized
+    assert calculate_img_tokens(_png_data_url(4000, 3000), mode="original") < oversized
+
+
 HUB_SETUP_SCRIPT: Final = """
 import json
 import sys

tests/unit/test_router/test_router.py

@@ -1,4 +1,5 @@
 import ast, asyncio, importlib, time
+import base64
 import contextlib
 import copy
 import functools
@@ -38,6 +39,7 @@ from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.integrations.otel.plumbing.context import set_request_root_span
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from litellm.litellm_core_utils.token_counter import high_detail_image_token_upper_bound
 from litellm.llms.anthropic.pass_through.messages.streaming_iterator import closing_superseded_attempt
 from litellm.llms.anthropic.pass_through.messages.agentic_streaming_iterator import (
     SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES,
@@ -7813,6 +7815,33 @@ async def test_aresponses_enforces_context_window_pre_call_check():
         )
 
 
+@pytest.mark.asyncio
+async def test_aresponses_counts_an_original_detail_image_against_the_context_window():
+    ihdr: Final = b"\x89PNG\r\n\x1a\n" + (13).to_bytes(4, "big") + b"IHDR" + (4000).to_bytes(4, "big") * 2
+    large_image: Final = "data:image/png;base64," + base64.b64encode(ihdr + b"\x08\x06\x00\x00\x00").decode()
+    router: Final = litellm.Router(
+        model_list=[
+            {
+                "model_name": "vision",
+                "litellm_params": {"model": "gpt-5.5", "mock_response": "hi"},
+                "model_info": {"max_input_tokens": high_detail_image_token_upper_bound() * 2},
+            }
+        ],
+        enable_pre_call_checks=True,
+    )
+
+    with pytest.raises(litellm.ContextWindowExceededError):
+        await router.aresponses(
+            model="vision",
+            input=[
+                {
+                    "role": "user",
+                    "content": [{"type": "input_image", "image_url": large_image, "detail": "original"}],
+                }
+            ],
+        )
+
+
 def test_get_deployment_model_info_base_model_flow():
     """Test that get_deployment_model_info correctly handles the base model flow"""
     from unittest.mock import patch