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feat(mistral): add Mistral Large 4 (Le Chonk) support (#44870)
Co-authored-by: moyai-devin-berriai[bot] <336287033+moyai-devin-berriai[bot]@users.noreply.github.com>
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3 changed files with 275 additions and 0 deletions
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@ -39226,6 +39226,48 @@
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"max_tokens": 8192,
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"mode": "embedding"
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},
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"mistral/mistral-large-4": {
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"cache_read_input_token_cost": 6.8e-08,
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"input_cost_per_token": 6.8e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 524288,
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"max_tokens": 524288,
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"mode": "chat",
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"output_cost_per_token": 2.09e-06,
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"reasoning_effort_levels": [
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"none",
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"high"
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],
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"source": "https://docs.mistral.ai/models/mistral-large-4",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_response_schema": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/mistral-large-4-0": {
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"cache_read_input_token_cost": 6.8e-08,
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"input_cost_per_token": 6.8e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 524288,
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"max_tokens": 524288,
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"mode": "chat",
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"output_cost_per_token": 2.09e-06,
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"reasoning_effort_levels": [
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"none",
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"high"
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],
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"source": "https://docs.mistral.ai/models/mistral-large-4",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_response_schema": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/mistral-large-latest": {
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"cache_read_input_token_cost": 5e-08,
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"input_cost_per_token": 5e-07,
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@ -39226,6 +39226,48 @@
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"max_tokens": 8192,
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"mode": "embedding"
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},
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"mistral/mistral-large-4": {
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"cache_read_input_token_cost": 6.8e-08,
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"input_cost_per_token": 6.8e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 524288,
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"max_tokens": 524288,
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"mode": "chat",
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"output_cost_per_token": 2.09e-06,
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"reasoning_effort_levels": [
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"none",
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"high"
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],
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"source": "https://docs.mistral.ai/models/mistral-large-4",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_response_schema": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/mistral-large-4-0": {
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"cache_read_input_token_cost": 6.8e-08,
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"input_cost_per_token": 6.8e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 524288,
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"max_tokens": 524288,
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"mode": "chat",
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"output_cost_per_token": 2.09e-06,
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"reasoning_effort_levels": [
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"none",
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"high"
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],
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"source": "https://docs.mistral.ai/models/mistral-large-4",
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"supports_assistant_prefill": true,
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"supports_function_calling": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_response_schema": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/mistral-large-latest": {
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"cache_read_input_token_cost": 5e-08,
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"input_cost_per_token": 5e-07,
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191
tests/unit/test_mistral_large_4_model_metadata.py
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191
tests/unit/test_mistral_large_4_model_metadata.py
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@ -0,0 +1,191 @@
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"""Le Chonk (Mistral Large 4) metadata and offline API-contract regressions."""
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import json
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from pathlib import Path
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import pytest
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import litellm
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from litellm.llms.mistral.chat.transformation import MistralConfig
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REPO_ROOT = Path(__file__).parents[2]
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MODELS = ("mistral/mistral-large-4", "mistral/mistral-large-4-0")
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@pytest.fixture(autouse=True)
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def local_model_cost_map(monkeypatch):
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"""Never depend on the published cost map having this new model yet."""
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monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
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monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
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litellm.get_model_info.cache_clear()
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yield
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litellm.get_model_info.cache_clear()
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@pytest.mark.parametrize("model", MODELS)
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def test_metadata_and_backup(model):
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main = json.loads((REPO_ROOT / "model_prices_and_context_window.json").read_text())
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backup = json.loads((REPO_ROOT / "litellm/model_prices_and_context_window_backup.json").read_text())
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assert main[model] == backup[model] == main[MODELS[0]]
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info = litellm.get_model_info(model)
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assert info["litellm_provider"] == "mistral"
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assert info["mode"] == "chat"
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assert info["max_input_tokens"] == 524288
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assert info["input_cost_per_token"] == 6.8e-07
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assert info["output_cost_per_token"] == 2.09e-06
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assert info["cache_read_input_token_cost"] == 6.8e-08
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assert info["reasoning_effort_levels"] == ["none", "high"]
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for capability in (
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"supports_vision",
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"supports_reasoning",
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"supports_function_calling",
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"supports_tool_choice",
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"supports_response_schema",
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"supports_assistant_prefill",
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"supports_prompt_caching",
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):
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assert info[capability] is True
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assert litellm.get_llm_provider(model=model)[:2] == (model.split("/", 1)[1], "mistral")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("effort", ["none", "high"])
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def test_reasoning_effort(model, effort):
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config = MistralConfig()
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assert "reasoning_effort" in config.get_supported_openai_params(model)
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assert config.map_openai_params({"reasoning_effort": effort}, {}, model, False) == {"reasoning_effort": effort}
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@pytest.mark.parametrize("model", MODELS)
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def test_completion_cost(model):
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uncached_response = litellm.ModelResponse(
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model=model.split("/", 1)[1],
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usage={"prompt_tokens": 1000, "completion_tokens": 100, "total_tokens": 1100},
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)
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assert litellm.completion_cost(completion_response=uncached_response, model=model) == pytest.approx(
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1000 * 6.8e-07 + 100 * 2.09e-06
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)
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response = litellm.ModelResponse(
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model=model.split("/", 1)[1],
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usage={
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"prompt_tokens": 1000,
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"completion_tokens": 100,
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"total_tokens": 1100,
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"prompt_tokens_details": {"cached_tokens": 600},
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},
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)
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assert litellm.completion_cost(completion_response=response, model=model) == pytest.approx(
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400 * 6.8e-07 + 600 * 6.8e-08 + 100 * 2.09e-06
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)
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def test_large_3_alias_is_not_retargeted():
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info = litellm.get_model_info("mistral/mistral-large-latest")
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assert info["input_cost_per_token"] == 5e-07
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assert info["max_input_tokens"] == 262144
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assert not info["supports_reasoning"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.parametrize("effort", ["none", "high"])
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@pytest.mark.asyncio
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async def test_completion_contract(model, sync_mode, effort, respx_mock):
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"""Exercise the real adapter against explicitly mocked Mistral HTTP responses."""
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api_model = model.split("/", 1)[1]
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content = "Test answer"
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if effort == "high":
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content = [
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{"type": "thinking", "thinking": [{"type": "text", "text": "Test reasoning"}]},
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{"type": "text", "text": "Test answer"},
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]
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route = respx_mock.post("https://api.mistral.ai/v1/chat/completions").respond(
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json={
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"id": "test-large-4",
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"object": "chat.completion",
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"created": 1,
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"model": api_model,
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"choices": [{"index": 0, "message": {"role": "assistant", "content": content}, "finish_reason": "stop"}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
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}
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)
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kwargs = {
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"model": model,
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"messages": [{"role": "user", "content": "Hello"}],
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"reasoning_effort": effort,
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"max_tokens": 64,
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"api_key": "test-key",
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"num_retries": 0,
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}
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response = litellm.completion(**kwargs) if sync_mode else await litellm.acompletion(**kwargs)
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assert response.choices[0].message.content == "Test answer"
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if effort == "high":
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assert response.choices[0].message.reasoning_content == "Test reasoning"
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assert route.call_count == 1
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payload = json.loads(route.calls[0].request.content)
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assert payload["model"] == api_model
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assert payload["reasoning_effort"] == effort
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assert payload["max_tokens"] == 64
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assert payload["messages"] == kwargs["messages"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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async def test_streaming_completion_contract(model, sync_mode, respx_mock):
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"""Verify thinking chunks and text are normalized on the streaming path."""
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api_model = model.split("/", 1)[1]
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deltas = [
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{
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"role": "assistant",
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"content": [{"type": "thinking", "thinking": [{"type": "text", "text": "Test reasoning"}]}],
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},
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{"content": "Test answer"},
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]
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events = [
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{
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"id": "test-stream",
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"object": "chat.completion.chunk",
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"created": 1,
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"model": api_model,
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"choices": [{"index": 0, "delta": delta, "finish_reason": None}],
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}
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for delta in deltas
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]
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events.append(
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{
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"id": "test-stream",
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"object": "chat.completion.chunk",
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"created": 1,
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"model": api_model,
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"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
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}
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)
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route = respx_mock.post("https://api.mistral.ai/v1/chat/completions").respond(
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text="".join(f"data: {json.dumps(event)}\n\n" for event in events) + "data: [DONE]\n\n",
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headers={"Content-Type": "text/event-stream"},
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)
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kwargs = {
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"model": model,
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"messages": [{"role": "user", "content": "Hello"}],
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"reasoning_effort": "high",
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"stream": True,
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"api_key": "test-key",
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"num_retries": 0,
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}
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if sync_mode:
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chunks = list(litellm.completion(**kwargs))
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else:
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stream = await litellm.acompletion(**kwargs)
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chunks = [chunk async for chunk in stream]
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assert "".join(chunk.choices[0].delta.content or "" for chunk in chunks) == "Test answer"
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assert (
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"".join(getattr(chunk.choices[0].delta, "reasoning_content", None) or "" for chunk in chunks)
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== "Test reasoning"
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)
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assert chunks[-1].choices[0].finish_reason == "stop"
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payload = json.loads(route.calls[0].request.content)
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assert payload["model"] == api_model
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assert payload["reasoning_effort"] == "high"
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assert payload["stream"] is True
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