MiniMax M3
A Tier · 8.4/10
MiniMax's coding/agent flagship -- M3 (June 1 2026): 1M-token context, MSA sparse attention (>15x decoding speedup at long context), SWE-Bench Pro 59.0%, Terminal-Bench 66.0%. OPEN WEIGHTS LIVE on HuggingFace since June 12 (~428B total / ~23B active, native multimodal, minimax-community license)
Score Breakdown
Benchmark Scores
Benchmarks for MiniMax-M2.7 (229B total, ~10B active MoE) -- self-evolving agent positioning per vendor
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-Bench Pro | 56.22% | ||
| Terminal Bench 2 | 57% | ||
| SWE Multilingual | 76.5% | ||
| Multi SWE Bench | 52.7% | ||
| VIBE-Pro | 55.6% |
Last updated: 2026-04-27
Personality & Tone
The Chinese multimodal generalist
Tone: Expressive and media-rich. MiniMax's chat models lean into long, formatted responses and handle voice and image prompts more naturally than most pure-text peers.
Quirks: Strong multimodal story; text-only quality is good but not class-leading versus DeepSeek or Qwen. Like other Chinese models, careful on domestic political topics.
The Good and the Bad
What we like
- +229B/10B-active MoE delivers Tier-1 agentic performance with the smallest active footprint in its class -- M2.7 hits 56.22% on SWE-Bench Pro and 57.0% on Terminal Bench 2
- +Sparse MoE design: ~10B active params during inference means fast and cheap to run despite 229B total
- +M2 / M2.5 (predecessors) carry true MIT licensing with zero commercial restrictions; M2.7 license needs verification (HF lists 'Other')
- +Native agentic and tool-use training -- positioned as a 'self-evolving agent model'
- +Per-layer QK-Norm + full-attention blocks make long-context stable
- +Strong cost-to-performance ratio for agentic workloads vs closed frontier models
What could be better
- −Smaller Western community than Qwen/DeepSeek -- tutorials sparse
- −Ollama support arrived late -- community relied on vLLM for months
- −English writing tone is noticeably less polished than Claude or Mistral
- −PRC content filters apply
- −MiniMax as a lab is less well-known than Alibaba or DeepSeek -- some enterprise buyers hesitate
Pricing
Self-hosted (Free)
- ✓MIT license on M2 / M2.5
- ✓M2.7 license listed as 'Other' on HuggingFace -- verify before commercial use
- ✓Weights on Hugging Face (MiniMaxAI/MiniMax-M2.7)
API (M2 / M2.5 reference, MiniMax / OpenRouter)
- ✓M2: $0.30 in / $1.20 out
- ✓192K+ context
- ✓Native agentic + tool-use
API (M2.7)
- ✓M2.7 per-token pricing not posted on platform.minimax.io/subscribe/token-plan as of 2026-04-27 -- verify before quoting in production planning
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| MiniMax M2 / M2.5 (230B total, ~10B active MoE)Sparse MoE activates only ~10B params during inference -- fast tok/s on moderate hardware | 96 GB unified RAM Q3 (Mac M3 Ultra) | 4× A100 80 GB FP8 |
| MiniMax M1 (hybrid-attention reasoning predecessor) | 96 GB unified RAM Q3 | 4× A100 80 GB FP8 |
Known Issues
- INDEPENDENT BENCHMARK PUBLISHED (verified 2026-07-04): Artificial Analysis now has a citable M3 page (artificialanalysis.ai/models/minimax-m3, 'Released June 2026') showing an **Intelligence Index of 44**. This is lower than early social-media chatter had suggested (~55 circulated on X/LinkedIn but does not match the authoritative page) -- cite 44 as the independently-measured number. It keeps M3 solidly mid-pack among open-weights (below GLM-5.2's 51) despite the strong vendor SWE-Bench Pro 59% claim; treat vendor coding numbers and the AA general-intelligence number as measuring different things.Source: Artificial Analysis (artificialanalysis.ai/models/minimax-m3) · 2026-07-04
- WEIGHTS SHIPPED (2026-06-12): **MiniMax-M3 open weights landed on HuggingFace** (MiniMaxAI/MiniMax-M3 + an MXFP8 quant) -- hitting the 'within 10 days of June 1' promise on day 10-11. Model-card specs: **~428B total / ~23B active params**, native multimodal (text/image/video input), MiniMax Sparse Attention, 1M-token context, 9x prefill / 15x decode speedup vs M2 at 1M ctx; tech report arXiv:2606.13392. LICENSE CAVEAT: **minimax-community license**, not MIT like the M2 line -- review terms before commercial self-hosting. Self-reported SWE-Bench Pro 59% now open to independent verification (Artificial Analysis pre-drop analysis pending update)Source: HuggingFace (huggingface.co/MiniMaxAI/MiniMax-M3), Artificial Analysis (artificialanalysis.ai/articles/minimax-m3), The Decoder · 2026-06-12
- MODEL LAUNCH (2026-06-01): **MiniMax M3** -- coding/agent flagship with **1M-token context** (product page guarantees minimum 512K) and MSA sparse attention delivering vendor-claimed **>9x prefilling / >15x decoding speedups** at long context (per-token compute at 1M context = 1/20th of prior gen). Vendor-published benchmarks: SWE-Bench Pro 59.0%, Terminal-Bench 2.1 66.0%, BrowseComp 83.5 (above Opus 4.7's 79.3), PostTrainBench 37.1 (#3 behind Opus 4.7 and GPT-5.5), MCP Atlas 74.2%. WEIGHTS CAVEAT: announced as open-weight with release 'over the next 10 days,' but as of 6/10 there is NO M3 repo on HuggingFace (latest = M2.7) -- label it 'open-weight, release pending,' not available. Token Plan subscriptions: Plus $20/mo (~1.7B tokens), Max $50/mo (~5.1B), Ultra $120/mo (~9.8B)Source: MiniMax blog (minimax.io/blog/minimax-m3), minimax.io/models/text/m3, HuggingFace MiniMaxAI (checked 2026-06-10) · 2026-06-01
- MiniMax-M2.7 (released 2026-03-18) supersedes M2.5 -- 229B total params, sparse MoE positioned by MiniMax as a 'self-evolving' agent model. Verified third-party benchmarks: SWE-Bench Pro 56.22%, Terminal Bench 2 57.0%, SWE Multilingual 76.5%, Multi SWE Bench 52.7%, VIBE-Pro 55.6%, GDPval-AA Elo 1495. The old 'first open-weight to hit 80.2% SWE-Bench Verified matching Opus 4.6' framing was an M2.5-on-different-bench claim and does NOT carry forward to M2.7 directly -- SWE-Bench Pro and SWE-Bench Verified are not comparable. License labeled 'Other' on HuggingFace (not MIT like M2 / M2.5) -- verify before commercial useSource: MiniMax blog (minimax.io/news/minimax-m27-en), HuggingFace MiniMaxAI/MiniMax-M2.7, MarkTechPost · 2026-03-18
- M2 initial release required custom vLLM build -- community quants took 2-3 weeks to stabilize. M2.7 inherits the same architecture so the vLLM compatibility story carries forwardSource: GitHub MiniMax-AI/MiniMax-M2, Hugging Face discussions · 2026-02
- Per-layer QK-Norm is non-standard -- some inference backends had subtle bugs at long contextSource: Reddit r/LocalLLaMA · 2026-03
Best for
Agentic coding and tool-use workflows on a budget. Best price-to-SWE-Bench ratio of any open-weights model in 2026.
Not for
Teams that prioritize polished English writing (Mistral Large 3 or Claude are better), or anyone who needs the deepest ecosystem support (Llama is still that).
Our Verdict
MiniMax M2/M2.5 is the most cost-efficient frontier-tier open model in 2026. The 80.2% SWE-Bench Verified score is a genuine breakthrough -- matching Claude Opus 4.6 on real coding tasks at a tenth of the price. The sparse 10B-active MoE runs fast on moderate hardware. The main drawback is ecosystem: MiniMax has less Western infrastructure support than Alibaba or DeepSeek. If you're building an agentic product and want maximum value per token, M2.5 is an A-tier pick.
Sources
- Artificial Analysis: MiniMax M3 (Intelligence Index 44, released June 2026) (accessed 2026-07-04)
- MiniMax: M2.7 release blog (2026-03-18) (accessed 2026-04-27)
- HuggingFace MiniMaxAI/MiniMax-M2.7 (accessed 2026-04-27)
- MarkTechPost: MiniMax M2.7 release coverage (accessed 2026-04-27)
- Artificial Analysis MiniMax M2 benchmarks (accessed 2026-04-13)
- Bytebot MiniMax M2.5 analysis (accessed 2026-04-13)
- Hugging Face MiniMaxAI collection (accessed 2026-04-13)
- GitHub MiniMax-AI/MiniMax-M2 (accessed 2026-04-13)
- OpenRouter pricing (accessed 2026-04-13)
Explore more MiniMax M3 rankings
Deeper leaderboards, benchmarks, task-specific tier lists, and status/pricing pages for MiniMax M3.
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Alternatives to MiniMax M3
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DeepSeek
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