MiniMax (official)

MiniMax M3.1 Flash: Space Performance + Bunny Speed

🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash.

💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed.

API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens.

Model weights will arrive on Oct 23.

API: platform.minimax.io
M Plan: minimax.io/m-plan
MiniMax Code for desktop: agent.minimax.io/download

M3.1 Flash is live on @FireworksAI_HQ . Let’s talk about how it was built—over cocktails.

Join MiniMax and @FireworksAI_HQ for Code & Cocktails in SF, where @Prof_OZ from Fireworks and @VictorSuOrtiz from MiniMax will discuss:
→ What went into building M3.1 Flash for long-running agents
→ How coding, tool use, and visual reasoning work together
→ What cheaper cached tokens mean for the cost of agent loops

Then drinks, bites, and time to compare notes with other builders.
📅 Tuesday, November 10 · Doors at 6 PM, fireside at 6:45 PM
📍 San Francisco · Venue shared after registration approval

Bring your questions. Registration below.
#MiniMax #FireworksAI #OpenModels #AgenticAI #AIEngineering

MiniMax M3.1 Flash, on more of your hardware.

Quantizing a 428B model opens up more ways to run it locally. Lower-bit weights reduce the memory needed to load the model and the weight data moved during decoding. Making those savings useful takes careful calibration, efficient low-bit kernels, and hardware-aware placement of experts across GPU and system RAM.

Thank you to the builders doing that work:

• @UnslothAI @danielhanchen — 2-bit Dynamic GGUF weights at 143.9 GB, with calibration and fidelity testing across coding, tool use and other workloads.
• @ViC305 — SAGE-EXL3, token-level fidelity measurements and a single-DGX-Spark recipe.
• @PeasantSmith — Project Maya quants and expert offloading for local coding and tool use.
• @MiaAI_lab — EXL3-EAQ quantization work.
• @jundotkim + mlx-community — oQ quants and DSpark-powered decoding through oMLX on Apple Silicon.
• @AIatAMD — Quark MXFP4 adapted for AMD Instinct.
• @atomic_chat_hq — GGUFs for local desktop workflows.
• @superalesha — custom inference work combining quantized weights, expert caching and system RAM on 4× RTX 3090.
Every shared quant, kernel, measurement and recipe gives the next builder a better starting point. Thank you.

💬 Live tomorrow with Fireworks AI: MiniMax M3.1 Flash.

Join @VictorSuOrtiz from MiniMax and @sophiamyang from @FireworksAI_HQ with your agent ideas, integration questions, or that one thing you’ve been meaning to ask.

Oct 28 · 10 AM PT

👉 RSVP: x.com/i/broadcasts/1…

MiniMax × @Pickford_AI bring Whispers to the Sydney Opera House during #NeurIPS2026.

Cinema that rewrites and reanimates itself in response to its audience.

December 7 · Private screening