* fix: align max_tokens with max_output_tokens for consistency
Fixed inconsistent max_tokens definitions in model_prices_and_context_window.json.
According to LiteLLM convention, max_tokens should equal max_output_tokens when available.
Models fixed:
- deepseek-chat: 131072 → 8192 (now equals max_output_tokens)
- dashscope/qwen-flash: 1000000 → 32768 (now equals max_output_tokens)
- databricks/databricks-gemma-3-12b: 128000 → 32000 (now equals max_output_tokens)
This ensures consistency across all providers where max_tokens represents
the maximum number of tokens that can be generated in the output.
* fix: align max_tokens with max_output_tokens for 244 models
- Fix 244 models where max_tokens != max_output_tokens
- Add test to validate max_tokens consistency and prevent regressions
According to model_prices_and_context_window.json spec:
- max_tokens is a LEGACY parameter
- Should always equal max_output_tokens when both are present
This ensures consistency across all model definitions.
* fix: align max_tokens with max_output_tokens for consistency
Fixed inconsistent max_tokens definitions in model_prices_and_context_window.json.
According to LiteLLM convention, max_tokens should equal max_output_tokens when available.
Models fixed:
- deepseek-chat: 131072 → 8192 (now equals max_output_tokens)
- dashscope/qwen-flash: 1000000 → 32768 (now equals max_output_tokens)
- databricks/databricks-gemma-3-12b: 128000 → 32000 (now equals max_output_tokens)
This ensures consistency across all providers where max_tokens represents
the maximum number of tokens that can be generated in the output.
* fix(workflow): Update issue labeling with working regex pattern
- Replace contains() with regex pattern using \s* for flexible whitespace matching
- Consolidate 4 separate steps into single unified component labeling step
- Tested and verified pattern works for all components: SDK, Proxy, UI Dashboard, Docs
- Pattern handles GitHub's issue body formatting with ### headers and variable newlines
Add support for Z.AI GLM-4.7, latest flagship model with enhanced reasoning capabilities.
Changes:
- Add zai/glm-4.7 to model pricing with /bin/bash.60/M input, .20/M output
- Add cached input pricing (/bin/bash.11/M) for GLM-4.7
- Add supports_reasoning flag to enable thinking parameter
- Update ZAIChatConfig to support thinking parameter for models with reasoning
- Update documentation with GLM-4.7 as latest flagship model
- Add cached input column to pricing table (GLM-4.7 only)
- Add tests for GLM-4.7 reasoning support and cost calculation
- Update all examples to use GLM-4.7
Model specifications:
- Context: 200K input, 128K output
- Supports: reasoning, function calling, tool choice, prompt caching
- Pricing: Same as GLM-4.6 with cache support
See: https://docs.z.ai/guides/llm/glm-4.7
* fix(cost_calculator): correct gpt-image-1 cost calculation using token-based pricing (#13847)
gpt-image-1 uses token-based pricing (like chat models), not pixel-based pricing
like DALL-E. The old code was calculating incorrect costs by treating it as DALL-E.
Changes:
- Update model pricing JSON with correct token-based costs for gpt-image-1
- Add dedicated cost calculator for OpenAI gpt-image models
- Route gpt-image-1 to token-based calculator in cost router
- Add comprehensive tests for the new calculator
* refactor: simplify gpt-image-1 cost calculator using responses API helper
Reuse _transform_response_api_usage_to_chat_usage and generic_cost_per_token
for gpt-image-1 cost calculation since ImageUsage has the same spec as
ResponseAPIUsage.
