* feat: add GMI Cloud provider support
Add GMI Cloud as an OpenAI-compatible provider with:
- Provider configuration in providers.json
- Documentation page with usage examples
- Model pricing for 16 models (Claude, GPT, DeepSeek, Gemini, etc.)
- Sidebar entry for docs navigation
* Add gmi_cloud to provider_endpoints_support.json
Add provider entry to pass CI validation check that ensures all
providers in openai_like/providers.json are documented.
* Fix provider key: gmi_cloud -> gmi
Match the provider key with providers.json
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Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Update audio token pricing for gpt-4o-audio-preview and
gpt-4o-audio-preview-2024-10-01 to match OpenAI's official pricing:
- input_cost_per_audio_token: 0.0001 -> 4e-05 ($40/1M tokens)
- output_cost_per_audio_token: 0.0002 -> 8e-05 ($80/1M tokens)
The previous values were 2.5x higher than OpenAI's actual pricing.
OpenAI's 400k context window is split between input and output:
- GPT-5/5.1/5.2 models: 272k input + 128k output = 400k context
- GPT-5-pro models: 128k input + 272k output = 400k context
Reference: https://openai.com/index/introducing-gpt-5-for-developers/
"In the API, all GPT-5 models can accept a maximum of 272,000 input
tokens and emit a maximum of 128,000 reasoning & output tokens"
Fixes incorrect 400k max_input_tokens values across 32 models.
- Add cerebras/zai-glm-4.7 with same specs as 4.6 (128K context, $2.25/M input, $2.75/M output)
- Mark cerebras/zai-glm-4.6 with deprecation_date: 2026-01-20
- Both models support function calling, reasoning, and tool choice
* 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
John Greek