feat: upgrade MiniMax default model to M3#132
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- Add MiniMax-M3 to the public model list and set as default - Update MINIMAX_MODEL default to MiniMax-M3 in settings and env.example - Set fallback_model to MiniMax-M2.7 (still available alongside M2.7-highspeed) - Description now reflects M3 / M2.7 / M2.7-highspeed options Signed-off-by: octo-patch <octo-patch@users.noreply.github.com>
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Summary
Upgrade the MiniMax LLM provider configuration to use the latest MiniMax-M3 as the default model, while keeping M2.7 and M2.7-highspeed as available options.
Changes
backend_api_python/app/services/llm.py- Setdefault_modeltoMiniMax-M3; keepMiniMax-M2.7as fallback.backend_api_python/app/routes/settings.py- UpdateMINIMAX_MODELdefault toMiniMax-M3; description now lists M3, M2.7 and M2.7-highspeed.backend_api_python/app/routes/market.py- Replaceminimax/minimax-m2entry in the public model list withminimax/minimax-m3/MiniMax: MiniMax M3.backend_api_python/env.example- BumpMINIMAX_MODELexample toMiniMax-M3.Why
MiniMax-M3 is the new flagship model with a 512K context window, 128K max output, and image input support across both OpenAI-compatible and Anthropic-compatible interfaces. M2.7 and the low-latency M2.7-highspeed variant remain available for users who prefer the previous generation.
How to test
LLM_PROVIDER=minimaxandMINIMAX_API_KEY=...in.env(noMINIMAX_MODELoverride).python run.py) and trigger any AI analysis path; verify the request hitshttps://api.minimax.io/v1withMiniMax-M3.MINIMAX_MODEL=MiniMax-M2.7(orMiniMax-M2.7-highspeed) and confirm the prior generation still works.Backward compatibility
Existing users with an explicit
MINIMAX_MODELoverride (e.g.MiniMax-M2.7orMiniMax-M2.7-highspeed) are unaffected. Only the default for users who never setMINIMAX_MODELchanges from M2.7 to M3.