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fix(ollama): report model capabilities via runtime API lookup

Browse source 
Ollama models were missing capabilities (supports_vision,
supports_function_calling) and context window data in LiteLLM's
/model/info endpoint. The runtime /api/show lookup was never reached
because api_base and api_key were not threaded through the enrichment
pipeline, and max_output_tokens was incorrectly set to the context
length.

Changes:

1. Thread api_base and api_key through the model info enrichment passes
   (proxy_server.py). The duplicated 3-pass lookup in
   _enrich_model_info_with_litellm_data and _get_proxy_model_info is
   consolidated into get_litellm_model_info, which passes api_base and
   api_key from litellm_params on every attempt so the Ollama provider
   can reach /api/show.

2. Snapshot built-in cost map keys at class definition time in
   _is_static_ollama_model (common_utils.py). The Router registers
   every deployment into litellm.model_cost at startup via
   register_model(persist_across_reloads=False). These registrations
   are not tracked in _runtime_registered_model_cost, so the previous
   check treated all configured Ollama models as static, skipping the
   runtime lookup. Snapshotting at class definition time ensures
   dynamically-registered entries don't pollute the static check.

3. Check the Ollama capabilities list for function calling detection
   (common_utils.py). _supports_function_calling previously only checked
   the template string for "tools", missing models like qwen3-coder and
   deepseek-r1 that have "tools" in their capabilities list but not in
   the template. Now checks capabilities first, falling back to the
   template heuristic.

4. Add supports_vision detection from Ollama capabilities
   (common_utils.py). New _supports_vision method checks if "vision" is
   in the Ollama capabilities list.

5. Set max_output_tokens to None in get_runtime_model_info
   (common_utils.py). The Ollama /api/show endpoint only provides
   context_length (the total context window), not max_output_tokens.
   Setting it to the context length caused clients to send max_tokens
   values exceeding the model's actual output limit, which Ollama
   rejected.

6. Cache the /api/show lookup with lru_cache (common_utils.py). Passing
   api_key to litellm.get_model_info bypasses its LRU cache, so the
   network call is extracted into _cached_ollama_show keyed on (model,
   api_base) only, matching _cached_get_model_info and
   _cached_get_model_group_info elsewhere in the codebase.

7. Add missing capability fields to all 29 ollama/ cost map entries
   (model_prices_and_context_window.json). All entries were missing
   supports_vision; 12 were also missing supports_function_calling.
   None of these models support vision, and the 12 without function
   calling are older models (llama2, llama3, orca-mini, vicuna,
   codellama, codegemma).
This commit is contained in:
fortesoftware 2026-08-11 16:25:26 -05:00
parent ff02d5cfc0
commit e4949e9d35
5 changed files with 256 additions and 107 deletions
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63
litellm/llms/ollama/common_utils.py
View file

@ -1,11 +1,31 @@
from functools import lru_cache
from typing import Any, Final
import httpx
from litellm import model_cost as _model_cost
from litellm import verbose_logger
from litellm.constants import DEFAULT_MAX_LRU_CACHE_SIZE
from litellm.llms.base_llm.chat.transformation import BaseLLMException
@lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE)
def _cached_ollama_show(model: str, api_base: str, headers: tuple[tuple[str, str], ...] = ()) -> dict[str, Any] | None:
from litellm import module_level_client
try:
response: Final = module_level_client.post(
url=f"{api_base}/api/show",
json={"name": model},
headers=dict(headers),
)
response.raise_for_status()
return response.json()
except Exception:
verbose_logger.debug("OllamaError: Could not get model info.")
return None
class OllamaError(BaseLLMException):
def __init__(self, status_code: int, message: str, headers: dict | httpx.Headers):
super().__init__(status_code=status_code, message=message, headers=headers)
@ -52,6 +72,8 @@ class OllamaModelInfo(BaseLLMModelInfo):
Returns the union of all model names.
"""
_builtin_model_cost_keys: Final = frozenset(key.lower() for key in _model_cost)
@staticmethod
def get_api_key(api_key=None) -> str | None:
"""Get API key from environment variables or litellm configuration"""
@ -141,8 +163,8 @@ class OllamaModelInfo(BaseLLMModelInfo):
@staticmethod
def _is_static_ollama_model(model: str) -> bool:
from litellm import model_cost
# Snapshot at class definition time so Router-registered keys
# (added via register_model at startup) don't pollute the check
stripped_model: Final = OllamaModelInfo._strip_ollama_model_prefix(model)
potential_model_names: Final = {
model,
@ -150,14 +172,21 @@ class OllamaModelInfo(BaseLLMModelInfo):
"ollama/" + stripped_model,
"ollama_chat/" + stripped_model,
}
model_cost_keys: Final = {key.lower() for key in model_cost}
return any(name.lower() in model_cost_keys for name in potential_model_names)
return any(name.lower() in OllamaModelInfo._builtin_model_cost_keys for name in potential_model_names)
@staticmethod
def _supports_function_calling(ollama_model_info: dict) -> bool:
capabilities: Final = ollama_model_info.get("capabilities", [])
if isinstance(capabilities, list) and "tools" in capabilities:
return True
_template: Final[str] = str(ollama_model_info.get("template", "") or "")
return "tools" in _template.lower()
@staticmethod
def _supports_vision(ollama_model_info: dict) -> bool:
capabilities: Final = ollama_model_info.get("capabilities", [])
return isinstance(capabilities, list) and "vision" in capabilities
@staticmethod
def _get_max_tokens(ollama_model_info: dict) -> int | None:
_model_info: Final[dict] = ollama_model_info.get("model_info", {})
@ -173,23 +202,15 @@ class OllamaModelInfo(BaseLLMModelInfo):
api_base: str | None = None,
api_key: str | None = None,
) -> dict[str, Any]:
from litellm import module_level_client
model = self._strip_ollama_model_prefix(model)
passed_api_base: Final = api_base
api_base = self.get_server_api_base(api_base)
api_key = self.get_api_key(api_key) if passed_api_base is None or api_key else None
headers: Final = {"Authorization": f"Bearer {api_key}"} if api_key else {}
resolved_api_key: Final = self.get_api_key(api_key) if passed_api_base is None or api_key else None
headers: Final = {"Authorization": f"Bearer {resolved_api_key}"} if resolved_api_key else {}
try:
response: Final = module_level_client.post(
url=f"{api_base}/api/show",
json={"name": model},
headers=headers,
)
response.raise_for_status()
except Exception:
verbose_logger.debug("OllamaError: Could not get model info.")
ollama_model_info = _cached_ollama_show(model, api_base, tuple(sorted(headers.items())))
if ollama_model_info is None:
return {
"key": model,
"litellm_provider": "ollama",
@ -201,19 +222,19 @@ class OllamaModelInfo(BaseLLMModelInfo):
"max_output_tokens": None,
}
model_info: Final = response.json()
max_tokens: Final = self._get_max_tokens(model_info)
max_tokens: Final = self._get_max_tokens(ollama_model_info)
return {
"key": model,
"litellm_provider": "ollama",
"mode": "chat",
"supports_function_calling": self._supports_function_calling(model_info),
"supports_function_calling": self._supports_function_calling(ollama_model_info),
"supports_vision": self._supports_vision(ollama_model_info),
"input_cost_per_token": 0.0,
"output_cost_per_token": 0.0,
"max_tokens": max_tokens,
"max_input_tokens": max_tokens,
"max_output_tokens": max_tokens,
"max_output_tokens": None,
}
def get_model_info(

91
litellm/proxy/proxy_server.py
View file

@ -8443,18 +8443,42 @@ def select_data_generator(
)
def get_litellm_model_info(model: dict = {}):
model_info: Final = model.get("model_info", {})
model_to_lookup = model.get("litellm_params", {}).get("model", None)
try:
if "azure" in model_to_lookup or model_info.get("base_model"):
model_to_lookup = model_info.get("base_model", None)
litellm_model_info: Final = litellm.get_model_info(model_to_lookup)
return litellm_model_info
except Exception:
# this should not block returning on /model/info
# if litellm does not have info on the model it should return {}
return {}
def get_litellm_model_info(model: dict) -> dict:
litellm_params: Final = model.get("litellm_params", {})
config_model_info: Final = model.get("model_info", {})
api_base: Final = litellm_params.get("api_base")
api_key: Final = litellm_params.get("api_key")
model_to_lookup: Final = litellm_params.get("model")
base_model: Final = config_model_info.get("base_model")
candidates: Final = tuple(m for m in (base_model, model_to_lookup) if m)
for candidate in candidates:
try:
result = litellm.get_model_info(
model=candidate,
api_base=api_base,
api_key=api_key,
)
if result:
return result
except Exception:
continue
if model_to_lookup:
split_model: Final = model_to_lookup.split("/")
if len(split_model) > 1:
try:
return litellm.get_model_info(
model=split_model[-1],
custom_llm_provider=split_model[0],
api_base=api_base,
api_key=api_key,
)
except Exception:
pass
return {}
def on_backoff(details):
@ -12467,28 +12491,6 @@ def _enrich_model_info_with_litellm_data(
# input_cost_per_token, output_cost_per_token, max_tokens
litellm_model_info = get_litellm_model_info(model=model)
# 2nd pass on the model, try seeing if we can find model in litellm model_cost map
if litellm_model_info == {}:
# use litellm_param model_name to get model_info
litellm_params = model.get("litellm_params", {})
litellm_model = litellm_params.get("model", None)
try:
litellm_model_info = litellm.get_model_info(model=litellm_model)
except Exception:
litellm_model_info = {}
# 3rd pass on the model, try seeing if we can find model but without the "/" in model cost map
if litellm_model_info == {}:
# use litellm_param model_name to get model_info
litellm_params = model.get("litellm_params", {})
litellm_model = litellm_params.get("model", None)
if litellm_model:
split_model: Final = litellm_model.split("/")
if len(split_model) > 0:
litellm_model = split_model[-1]
try:
litellm_model_info = litellm.get_model_info(model=litellm_model, custom_llm_provider=split_model[0])
except Exception:
litellm_model_info = {}
for k, v in litellm_model_info.items():
if k not in model_info:
model_info[k] = v
@ -13890,27 +13892,6 @@ def _get_proxy_model_info(model: dict) -> dict:
# input_cost_per_token, output_cost_per_token, max_tokens
litellm_model_info = get_litellm_model_info(model=model)
# 2nd pass on the model, try seeing if we can find model in litellm model_cost map
if litellm_model_info == {}:
# use litellm_param model_name to get model_info
litellm_params = model.get("litellm_params", {})
litellm_model = litellm_params.get("model", None)
try:
litellm_model_info = litellm.get_model_info(model=litellm_model)
except Exception:
litellm_model_info = {}
# 3rd pass on the model, try seeing if we can find model but without the "/" in model cost map
if litellm_model_info == {}:
# use litellm_param model_name to get model_info
litellm_params = model.get("litellm_params", {})
litellm_model = litellm_params.get("model", None)
split_model: Final = litellm_model.split("/")
if len(split_model) > 0:
litellm_model = split_model[-1]
try:
litellm_model_info = litellm.get_model_info(model=litellm_model, custom_llm_provider=split_model[0])
except Exception:
litellm_model_info = {}
for k, v in litellm_model_info.items():
if k not in model_info:
model_info[k] = v

99
model_prices_and_context_window.json
View file

@ -33060,7 +33060,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": false
"supports_function_calling": false,
"supports_vision": false
},
"ollama/codegemma": {
"input_cost_per_token": 0.0,
@ -33069,7 +33070,9 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "completion",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/codellama": {
"input_cost_per_token": 0.0,
@ -33078,7 +33081,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "completion",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/deepseek-coder-v2-base": {
"input_cost_per_token": 0.0,
@ -33088,7 +33093,8 @@
"max_tokens": 8192,
"mode": "completion",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/deepseek-coder-v2-instruct": {
"input_cost_per_token": 0.0,
@ -33098,7 +33104,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/deepseek-coder-v2-lite-base": {
"input_cost_per_token": 0.0,
@ -33108,7 +33115,8 @@
"max_tokens": 8192,
"mode": "completion",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/deepseek-coder-v2-lite-instruct": {
"input_cost_per_token": 0.0,
@ -33118,7 +33126,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/deepseek-v3.1:671b-cloud": {
"input_cost_per_token": 0.0,
@ -33128,7 +33137,8 @@
"max_tokens": 163840,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/gpt-oss:120b-cloud": {
"input_cost_per_token": 0.0,
@ -33138,7 +33148,8 @@
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/gpt-oss:20b-cloud": {
"input_cost_per_token": 0.0,
@ -33148,7 +33159,8 @@
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/internlm2_5-20b-chat": {
"input_cost_per_token": 0.0,
@ -33158,7 +33170,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/llama2": {
"input_cost_per_token": 0.0,
@ -33167,7 +33180,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama2-uncensored": {
"input_cost_per_token": 0.0,
@ -33176,7 +33191,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "completion",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama2:13b": {
"input_cost_per_token": 0.0,
@ -33185,7 +33202,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama2:70b": {
"input_cost_per_token": 0.0,
@ -33194,7 +33213,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama2:7b": {
"input_cost_per_token": 0.0,
@ -33203,7 +33224,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama3": {
"input_cost_per_token": 0.0,
@ -33212,7 +33235,9 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama3.1": {
"input_cost_per_token": 0.0,
@ -33222,7 +33247,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/llama3:70b": {
"input_cost_per_token": 0.0,
@ -33231,7 +33257,9 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/llama3:8b": {
"input_cost_per_token": 0.0,
@ -33240,7 +33268,9 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/mistral": {
"input_cost_per_token": 0.0,
@ -33250,7 +33280,8 @@
"max_tokens": 8192,
"mode": "completion",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/mistral-7B-Instruct-v0.1": {
"input_cost_per_token": 0.0,
@ -33260,7 +33291,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/mistral-7B-Instruct-v0.2": {
"input_cost_per_token": 0.0,
@ -33270,7 +33302,8 @@
"max_tokens": 32768,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/mistral-large-instruct-2407": {
"input_cost_per_token": 0.0,
@ -33280,7 +33313,8 @@
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/mixtral-8x22B-Instruct-v0.1": {
"input_cost_per_token": 0.0,
@ -33290,7 +33324,8 @@
"max_tokens": 65536,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/mixtral-8x7B-Instruct-v0.1": {
"input_cost_per_token": 0.0,
@ -33300,7 +33335,8 @@
"max_tokens": 32768,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/orca-mini": {
"input_cost_per_token": 0.0,
@ -33309,7 +33345,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "completion",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"ollama/qwen3-coder:480b-cloud": {
"input_cost_per_token": 0.0,
@ -33319,7 +33357,8 @@
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 0.0,
"supports_function_calling": true
"supports_function_calling": true,
"supports_vision": false
},
"ollama/vicuna": {
"input_cost_per_token": 0.0,
@ -33328,7 +33367,9 @@
"max_output_tokens": 2048,
"max_tokens": 2048,
"mode": "completion",
"output_cost_per_token": 0.0
"output_cost_per_token": 0.0,
"supports_vision": false,
"supports_function_calling": false
},
"omni-moderation-2024-09-26": {
"input_cost_per_token": 0.0,

2
tests/proxy_unit_tests/test_proxy_server.py
View file

@ -3116,4 +3116,4 @@ def test_get_litellm_model_info(data):
new=get_info_mock,
):
get_litellm_model_info(model=model)
get_info_mock.assert_called_once_with(data["expected"])
get_info_mock.assert_called_once_with(model=data["expected"], api_base=None, api_key=None)

108
tests/test_litellm/llms/ollama/test_ollama_model_info.py
View file

@ -24,7 +24,15 @@ if "httpx" not in sys.modules:
import httpx
import litellm
from litellm.llms.ollama.common_utils import OllamaModelInfo
from litellm.llms.ollama.common_utils import OllamaModelInfo, _cached_ollama_show
@pytest.fixture(autouse=True)
def _clear_ollama_show_cache():
"""Clear the lru_cache on _cached_ollama_show before each test."""
_cached_ollama_show.cache_clear()
yield
_cached_ollama_show.cache_clear()
class DummyResponse:
@ -517,6 +525,7 @@ class TestOllamaGetModelInfo:
)
assert captured_json[0]["name"] == "my-custom-model"
_cached_ollama_show.cache_clear()
config.get_model_info(
"ollama_chat/my-custom-model", api_base="http://localhost:11434"
)
@ -580,6 +589,103 @@ class TestOllamaGetModelInfo:
assert model_info["litellm_provider"] == "ollama"
class TestOllamaModelInfoCapabilities:
"""Tests for capability detection from Ollama /api/show response."""
def test_get_runtime_model_info_reports_vision_capability(self, monkeypatch):
"""supports_vision should be True when capabilities includes 'vision'."""
from litellm.llms.ollama.completion.transformation import OllamaConfig
def mock_post(url, json, headers=None):
return DummyResponse(
{
"template": "{{ .System }} tools {{ .Prompt }}",
"capabilities": ["completion", "tools", "vision"],
"model_info": {"llama.context_length": 131072},
},
status_code=200,
)
monkeypatch.setattr("litellm.module_level_client.post", mock_post)
config = OllamaConfig()
result = config.get_model_info("my-vision-model", api_base="http://localhost:11434")
assert result["supports_vision"] is True
assert result["supports_function_calling"] is True
assert result["max_input_tokens"] == 131072
def test_get_runtime_model_info_no_vision_capability(self, monkeypatch):
"""supports_vision should be False when capabilities lacks 'vision'."""
from litellm.llms.ollama.completion.transformation import OllamaConfig
def mock_post(url, json, headers=None):
return DummyResponse(
{
"template": "{{ .System }} {{ .Prompt }}",
"capabilities": ["completion"],
"model_info": {"llama.context_length": 8192},
},
status_code=200,
)
monkeypatch.setattr("litellm.module_level_client.post", mock_post)
config = OllamaConfig()
result = config.get_model_info("my-text-model", api_base="http://localhost:11434")
assert result["supports_vision"] is False
def test_get_runtime_model_info_no_capabilities_field(self, monkeypatch):
"""supports_vision should be False when capabilities field is absent."""
from litellm.llms.ollama.completion.transformation import OllamaConfig
def mock_post(url, json, headers=None):
return DummyResponse(
{
"template": "{{ .System }} {{ .Prompt }}",
"model_info": {"llama.context_length": 8192},
},
status_code=200,
)
monkeypatch.setattr("litellm.module_level_client.post", mock_post)
config = OllamaConfig()
result = config.get_model_info("my-text-model", api_base="http://localhost:11434")
assert result["supports_vision"] is False
def test_litellm_get_model_info_threads_api_base_to_ollama(self, monkeypatch):
"""litellm.get_model_info should pass api_base through to the Ollama provider hook."""
captured_urls = []
def mock_post(url, json, headers=None):
captured_urls.append(url)
return DummyResponse(
{
"template": "{{ .System }} tools {{ .Prompt }}",
"capabilities": ["completion", "tools", "vision"],
"model_info": {"llama.context_length": 32768},
},
status_code=200,
)
litellm.get_model_info.cache_clear()
monkeypatch.setattr("litellm.module_level_client.post", mock_post)
try:
model_info = litellm.get_model_info(
"ollama_chat/llama3.2-vision:11b",
api_base="http://remote-ollama:11434",
)
finally:
litellm.get_model_info.cache_clear()
assert captured_urls[0] == "http://remote-ollama:11434/api/show"
assert model_info["supports_vision"] is True
assert model_info["supports_function_calling"] is True
assert model_info["max_input_tokens"] == 32768
class TestOllamaAuthHeaders:
"""Tests for Ollama authentication header handling in completion calls."""