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feat: upgrade MiniMax default model to M3#11

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octo-patch:feature/upgrade-minimax-m3
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feat: upgrade MiniMax default model to M3#11
octo-patch wants to merge 1 commit into
EvolvingLMMs-Lab:mainfrom
octo-patch:feature/upgrade-minimax-m3

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@octo-patch octo-patch commented Jun 1, 2026

Summary

Upgrade the MiniMax model registration in NEO's evaluation kits to add MiniMax-M3 as the new default option, while keeping MiniMax-M2.7 and MiniMax-M2.7-highspeed available for users who depend on the older entries.

Changes

File Change
VLMEvalKit/vlmeval/config.py Add MiniMax-M3 partial entry (placed before legacy M2.7 / abab entries)
VLMEvalKit_ov/vlmeval/config.py Same change mirrored to the NEO-ov kit
VLMEvalKit/tests/test_minimax_provider.py Extend tests with M3 coverage (registry, api_base, model name, supported_VLM, GPT4V wrapper, retry, ordering, key detection, integration)

vlmeval/api/gpt.py already routes any model whose name contains MiniMax-M to the MiniMax_API_KEY / MINIMAX_API_KEY env var, so no code change is needed there for M3 to inherit the existing key-detection logic.

Why

MiniMax-M3 is the latest MiniMax flagship model (512K context, up to 128K output, image input support) and is the recommended default for new evaluations. Listing it ahead of the older entries surfaces it as the preferred option in the registry while preserving backward compatibility for any existing scripts that pin MiniMax-M2.7 or MiniMax-M2.7-highspeed.

Test plan

  • python -m unittest tests.test_minimax_provider.TestMiniMaxKeyDetection — new test_m3_matches_condition and test_m3_detects_minimax_api_key pass
  • AST validation: MiniMax-M3 registered with partial(GPT4V, model="MiniMax-M3", api_base="https://api.minimax.io/v1/chat/completions", temperature=0.01, retry=10) in both kits, ordered before legacy entries
  • Direct API smoke test confirms MiniMax-M3 is accepted by https://api.minimax.io/v1/chat/completions

Add MiniMax-M3 to the model registry and place it before the existing
M2.7 / abab entries so it becomes the recommended default for new
evaluations. M2.7 and M2.7-highspeed are retained as legacy options.

- Add MiniMax-M3 to VLMEvalKit/vlmeval/config.py and
  VLMEvalKit_ov/vlmeval/config.py (mirror)
- Insert M3 ahead of legacy MiniMax-M2.7 / M2.7-highspeed / abab entries
- Reuse the same OpenAI-compatible GPT4V wrapper, api.minimax.io base
  URL, temperature=0.01 (API requires (0,1]), retry=10
- Extend tests/test_minimax_provider.py with M3 coverage:
  registration, api_base, model name, supported_VLM membership,
  GPT4V wrapper, retry count, ordering before legacy, key detection,
  and an integration sanity-check
- No change to api/gpt.py: existing 'MiniMax-M' substring match
  already covers M3
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