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refactor(model_info): drop Gemini vertex routing rule, keep baseline to certain capabilities
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry
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024a887521
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082f02bd73
3 changed files with 9 additions and 69 deletions
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@ -58150,36 +58150,18 @@
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"supports_reasoning": true
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}
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},
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{
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"name": "vertex-gemini-ids",
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"pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$",
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"description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-<major>[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.",
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"model_info": {
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"litellm_provider": "vertex_ai"
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}
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},
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{
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"name": "gemini-family-baseline",
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"pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])",
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"description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-<major>[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.",
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"description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-<major>[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.",
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"model_info": {
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"mode": "chat",
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"max_input_tokens": 1048576,
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"max_output_tokens": 65536,
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"max_tokens": 65536,
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"supports_function_calling": true,
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"supports_parallel_function_calling": true,
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"supports_vision": true,
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"supports_pdf_input": true,
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"supports_audio_input": true,
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"supports_video_input": true,
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"supports_tool_choice": true,
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"supports_system_messages": true,
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"supports_vision": true,
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"supports_response_schema": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_web_search": true,
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"supports_url_context": true
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"supports_reasoning": true
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}
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}
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]
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@ -58150,36 +58150,18 @@
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"supports_reasoning": true
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}
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},
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{
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"name": "vertex-gemini-ids",
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"pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$",
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"description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-<major>[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.",
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"model_info": {
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"litellm_provider": "vertex_ai"
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}
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},
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{
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"name": "gemini-family-baseline",
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"pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])",
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"description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-<major>[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.",
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"description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-<major>[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.",
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"model_info": {
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"mode": "chat",
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"max_input_tokens": 1048576,
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"max_output_tokens": 65536,
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"max_tokens": 65536,
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"supports_function_calling": true,
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"supports_parallel_function_calling": true,
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"supports_vision": true,
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"supports_pdf_input": true,
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"supports_audio_input": true,
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"supports_video_input": true,
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"supports_tool_choice": true,
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"supports_system_messages": true,
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"supports_vision": true,
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"supports_response_schema": true,
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"supports_prompt_caching": true,
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"supports_reasoning": true,
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"supports_web_search": true,
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"supports_url_context": true
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"supports_reasoning": true
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}
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}
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]
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@ -449,31 +449,6 @@ def shipped_cost_map(monkeypatch):
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set_fallback_generalizations(previous_rules)
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def test_shipped_bare_gemini_id_routes_to_vertex_ai(shipped_cost_map):
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for model in (
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"gemini-3.9-flash",
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"gemini-4-pro",
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"gemini-4-pro-preview",
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"gemini-3.9-flash-lite-preview-09-2026",
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):
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assert model not in litellm.model_cost
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assert litellm.get_llm_provider(model=model)[1] == "vertex_ai"
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assert litellm.get_llm_provider(model="gemini-2.5-pro")[1] == "vertex_ai"
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def test_shipped_gemini_routing_rule_is_anchored(shipped_cost_map):
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for model in (
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"gemini-4-flash-image",
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"gemini-3.9-flash-preview-tts",
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"gemini-2.0-flash-new",
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"gemini-1.5-flash-new",
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"gemini-4-flashy",
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"gemini/gemini-4-pro",
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):
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assert match_routing_generalization(model) is None, model
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@pytest.mark.parametrize(
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"model,provider",
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[
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@ -491,10 +466,11 @@ def test_shipped_gemini_baseline_resolves_unmapped_ids_provider_neutral(shipped_
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info = litellm.get_model_info(model, custom_llm_provider=provider)
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assert info["litellm_provider"] == provider
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assert info["mode"] == "chat"
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assert info["max_input_tokens"] == 1048576
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assert info["max_tokens"] == 65536
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assert not info.get("max_input_tokens")
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assert info["supports_reasoning"] is True
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assert info["supports_function_calling"] is True
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assert info["supports_tool_choice"] is True
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assert info["supports_response_schema"] is True
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assert info["supports_vision"] is True
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assert not info.get("input_cost_per_token")
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assert not info.get("output_cost_per_token")
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