Our kernel team has been deep in MiniMax M3 all week. The 1M-token context and native multimodality make it a hard model to serve well, which is exactly the kind of problem we like!
When the open weights drop in the next few days, you'll be able to run it on Modular right away.
Maison Solutions Inc. (Nasdaq: MSS) has announced a strategic collaboration with @Supplyaiusa and @MiniMax_AI to explore AI-native food supply chain solutions.
Together, the parties aim to bring AI closer to real food retail and supply chain operations.
Read more:
Excited to see this take shape.
Where digital intelligence meets physical operations — that's where the next wave of AI lives.
Proud to power this with @Supplyaiusa and @HKGoodFortune.
AI-native food supply chain → coming soon. 🚀
Big step for SupplyAi.
We’re excited to be part of the strategic collaboration announced by @HKGoodFortune (Nasdaq: MSS) with @MiniMax_AI to explore AI-native food supply chain solutions.
We’re building toward a future where business data, AI agents, and physical execution are
MiniMax is headed to Cupertino for AiOS Meetup — WWDC ’26 Edition on June 11.
Apple platforms. On-device AI. AI-native apps. A room full of builders fresh off WWDC.
Speakers include @awnihannun, co-creator of MLX, and @peterfriese, sharing more on Gemini models via Firebase and Apple Foundation Models.
Every attendee gets $50 in credits to try MiniMax M3.
Hosted by @rudrank, @ronaldmannak, @rayfernando, and @CommunityKit.
See you there.
Details in the comments.
#WWDC26#MLX#Firebase#AppleAI#OnDeviceAI
After using @MiniMax_AI M3 quite extensively for 2 days, I would say I can use it as the main model for coding. I have been testing mostly with medium-sized task (up to 1250 lines of code). Not sure how much Roo Code harness is affecting it, but the combo seems to work really well. I basically run Roo Code within Windsurf.
With $20/month plan, it seems that I can potentially write around 400k lines of code before I run out of the quota.
A quick review on @MiniMax_AI M3:
Harness: Roo Code within Windsurf Environment
- A very capable model for coding. I tested using Code mode with a medium size task that goes across multiple components (complex graph on canvas + inspector for scenario mode). It was able to
Today, we are launching GMI Agent Box.
A complete infrastructure stack for production-ready AI agents:
native Docker, flexible deployment, 200+ models under one API key, dedicated compute across regions, and a marketplace for distribution.
Available now.
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
truly impressed by @MiniMax_AI m3's visual coding
@NousResearch Hermes Desktop Agent + Minimax M3 built the whole thing 🌺
I didn't open a single TouchDesigner node for this
Way too funny, @fromsinaimportx.
Using @MiniMax_AI M3 to control an iPhone through a voice agent powered by tung tung tung sahur 🤣
And with Speech 2.8 voice cloning, you can create multiple personas to control your phone, including MrBeast.
More from the night coming soon, including our winners 👑
Super excited to see what people build with @MiniMax_AI M3 and Speech-2.8 at the @ycombinator conversational voice hackathon.
Thank you @usemoss for putting this together.
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