MiniMax-M3 scores 55 on the Artificial Analysis Intelligence Index. Once the weights are released, it will be the leading open weights model

M3 is @MiniMax_AI's first multimodal M-series model, adding image and video input and a 1M token context window over the text-only MiniMax-M2.7 (50). At 55 on the Intelligence Index it sits just ahead of open weights peers Kimi K2.6 (54) and MiMo-V2.5-Pro (54). MiniMax has noted they plan to release the weights within ~10 days. When MiniMax released the weights for M2.7, it was under a commercially restricted license.

Key takeaways:

➤ MiniMax-M3 improves on MiniMax-M2.7 across most evaluations. HLE +9 points (28% to 37%), GPQA Diamond +6 (87% to 93%), AA-LCR +5 (69% to 74%), IFBench +7 (76% to 83%), and CritPt +3 (1% to 4%), with a small regression on SciCode (47% to 45%)

➤ M3 scores ~1670 on GDPval-AA, behind Claude Opus 4.8 (max, 1890) and GPT-5.5 (xhigh, 1769), and level with Claude Sonnet 4.6 (max, 1676). GDPval-AA measures real-world tasks across 44 occupations and 9 industries

➤ Native multimodality, scoring ~80% on MMMU-Pro. Level with GPT-5.5 (xhigh, 79.9%) and Kimi K2.6 (79.4%), behind Gemini 3.5 Flash (high, 84.3%). Not all open weights models support native vision input

➤ On AA-Omniscience, heavy abstention drives both low hallucination and low accuracy. M3 attempts only 30.9% of questions, the lowest among current peers, yielding a low hallucination rate (16.1%) and low accuracy (15.0%)

➤ MiniMax-M3's token usage is close to M2.7's, using ~91M output tokens to run the Intelligence Index (~81M reasoning) versus ~87M (~79M reasoning), while scoring 5 points higher

Key model details:

➤ Context window: 1M tokens, up from MiniMax-M2.7's 200K

➤ Pricing: $0.30/$1.20 per 1M input/output tokens up to 512K context, rising to $0.60/$2.40 for 512K to 1M context

➤ Weights: Not yet released. MiniMax has stated the weights will follow

➤ Availability: MiniMax first-party API, @SiliconFlowAI, @gmi_cloud, and @novita_labs