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https://github.com/BerriAI/litellm.git
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Added vertex_ai/qwen models and azure/gpt-5-codex (#14844)
* added qwen models and gpt-5-codex * fix flaky test * fix failing test
This commit is contained in:
Mubashir Osmani
GitHub
parent
22eef373eb
commit
0cd91a82d2
3 changed files with 159 additions and 6 deletions
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@ -2032,6 +2032,36 @@
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"supports_tool_choice": false,
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"supports_vision": true
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},
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"azure/gpt-5-codex": {
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"cache_read_input_token_cost": 1.25e-07,
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "azure",
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"max_input_tokens": 272000,
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"max_output_tokens": 128000,
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"max_tokens": 128000,
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"mode": "responses",
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"output_cost_per_token": 1e-05,
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"supported_endpoints": [
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"/v1/responses"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text"
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],
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"supports_function_calling": true,
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"supports_native_streaming": true,
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"supports_parallel_function_calling": true,
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"supports_pdf_input": 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_system_messages": true,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"azure/gpt-5-mini": {
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"cache_read_input_token_cost": 2.5e-08,
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"input_cost_per_token": 2.5e-07,
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@ -5282,6 +5312,49 @@
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"deepseek-chat": {
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"cache_read_input_token_cost": 6e-08,
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"input_cost_per_token": 6e-07,
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"litellm_provider": "deepseek",
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"max_input_tokens": 131072,
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"max_output_tokens": 8192,
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"max_tokens": 131072,
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"mode": "chat",
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"output_cost_per_token": 1.7e-06,
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"source": "https://api-docs.deepseek.com/quick_start/pricing",
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"supported_endpoints": [
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"/v1/chat/completions"
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],
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"supports_function_calling": true,
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"supports_native_streaming": true,
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"supports_parallel_function_calling": true,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_tool_choice": true
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},
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"deepseek-reasoner": {
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"cache_read_input_token_cost": 6e-08,
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"input_cost_per_token": 6e-07,
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"litellm_provider": "deepseek",
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"max_input_tokens": 131072,
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"max_output_tokens": 65536,
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"max_tokens": 131072,
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"mode": "chat",
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"output_cost_per_token": 1.7e-06,
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"source": "https://api-docs.deepseek.com/quick_start/pricing",
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"supported_endpoints": [
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"/v1/chat/completions"
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],
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"supports_function_calling": false,
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"supports_native_streaming": true,
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"supports_parallel_function_calling": false,
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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_system_messages": true,
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"supports_tool_choice": false
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},
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"dashscope/qwen-coder": {
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"input_cost_per_token": 3e-07,
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"litellm_provider": "dashscope",
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@ -12427,6 +12500,36 @@
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"supports_tool_choice": false,
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"supports_vision": true
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},
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"gpt-5-codex": {
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"cache_read_input_token_cost": 1.25e-07,
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"input_cost_per_token": 1.25e-06,
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"litellm_provider": "openai",
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"max_input_tokens": 272000,
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"max_output_tokens": 128000,
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"max_tokens": 128000,
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"mode": "responses",
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"output_cost_per_token": 1e-05,
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"supported_endpoints": [
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"/v1/responses"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text"
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],
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"supports_function_calling": true,
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"supports_native_streaming": false,
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"supports_parallel_function_calling": true,
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"supports_pdf_input": false,
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"supports_prompt_caching": true,
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"supports_reasoning": false,
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"supports_response_schema": true,
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"supports_system_messages": false,
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"gpt-5-mini": {
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"cache_read_input_token_cost": 2.5e-08,
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"cache_read_input_token_cost_flex": 1.25e-08,
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@ -20527,6 +20630,24 @@
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"vertex_ai/deepseek-ai/deepseek-v3.1-maas": {
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"input_cost_per_token": 1.35e-06,
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"litellm_provider": "vertex_ai-deepseek_models",
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"max_input_tokens": 163840,
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"max_output_tokens": 32768,
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"max_tokens": 163840,
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"mode": "chat",
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"output_cost_per_token": 5.4e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
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"supported_regions": [
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"us-west2"
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],
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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_tool_choice": true
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},
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"vertex_ai/deepseek-ai/deepseek-r1-0528-maas": {
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"input_cost_per_token": 1.35e-06,
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"litellm_provider": "vertex_ai-deepseek_models",
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@ -20940,6 +21061,30 @@
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"vertex_ai/qwen/qwen3-next-80b-a3b-instruct-maas": {
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"input_cost_per_token": 1.5e-07,
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"litellm_provider": "vertex_ai-qwen_models",
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"max_tokens": 262144,
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"mode": "chat",
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"output_cost_per_token": 1.2e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"vertex_ai/qwen/qwen3-next-80b-a3b-thinking-maas": {
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"input_cost_per_token": 1.5e-07,
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"litellm_provider": "vertex_ai-qwen_models",
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"max_input_tokens": 262144,
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"max_output_tokens": 262144,
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"max_tokens": 262144,
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"mode": "chat",
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"output_cost_per_token": 1.2e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"vertex_ai/veo-2.0-generate-001": {
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"litellm_provider": "vertex_ai-video-models",
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"max_input_tokens": 1024,
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@ -27,6 +27,7 @@ import litellm
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from litellm import (
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ModelResponse,
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RateLimitError,
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ServiceUnavailableError,
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Timeout,
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completion,
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completion_cost,
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@ -2020,10 +2021,16 @@ def test_bedrock_context_window_error():
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def test_bedrock_converse_route():
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litellm.set_verbose = True
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litellm.completion(
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model="bedrock/converse/us.amazon.nova-pro-v1:0",
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messages=[{"role": "user", "content": "Hello, world!"}],
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)
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try:
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litellm.completion(
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model="bedrock/converse/us.amazon.nova-pro-v1:0",
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messages=[{"role": "user", "content": "Hello, world!"}],
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)
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except ServiceUnavailableError as e:
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if "Too many requests" in str(e):
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pytest.skip("Skipping test due to AWS Bedrock rate limiting")
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else:
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raise
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def test_bedrock_mapped_converse_models():
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@ -43,8 +43,9 @@ async def test_anthropic_basic_completion_with_headers():
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json.dumps(response_json, indent=4, default=str),
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)
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reported_usage = response_json.get("usage", None)
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anthropic_api_input_tokens = reported_usage.get("input_tokens", None)
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anthropic_api_output_tokens = reported_usage.get("output_tokens", None)
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# fix null checks for reported_usage
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anthropic_api_input_tokens = reported_usage.get("input_tokens", None) if reported_usage else None
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anthropic_api_output_tokens = reported_usage.get("output_tokens", None) if reported_usage else None
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litellm_call_id = response_headers.get("x-litellm-call-id")
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print(f"LiteLLM Call ID: {litellm_call_id}")
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