Quick answer: MiniMax M3.1 Flash is MiniMax's October 2026 refresh of M3 — a native multimodal mixture-of-experts model (~428B total, 23B active) for coding, agentic workflows, computer use, and cybersecurity, with a 1M-token context window. It scores 76.2% on BankerToolBench, 73.7% on DeepSWE v1.1, and 61.7% on OSWorld 2.0.
Where MiniMax M3.1 Flash leads
Where it lags
Best for: Agentic coding and computer-use workloads needing long context and efficient inference over M3.
MiniMax M3.1 Flash builds on M3's mixture-of-experts architecture (~428B total parameters, ~23B active per token) with three changes: sparse attention replacing the first three full-attention layers, quantization-aware training of routed experts and attention paths, and DSpark speculative decoding replacing the earlier EAGLE-like draft approach. It accepts text, image, and video input and produces text output, with a context window of up to 1,000,000 tokens.
The model targets agentic use cases — coding, computer-use (OSWorld), tool-calling, and cybersecurity research — rather than pure knowledge/reasoning benchmarks, continuing MiniMax's enterprise-agentic positioning from M3.
| Field | Value |
|---|---|
| Organization | MiniMax |
| Parameters | ~428B total; ~23B active per token (MoE) |
| Context window | 1,000,000 tokens |
| Architecture | Native multimodal mixture-of-experts with MiniMax Sparse Attention (MSA) + DSpark speculative decoding |
| License | MiniMax Community License |
| Release date | October 2026 |
| Modality | Text, image, video (input); text (output) |
Pricing not yet published by MiniMax for M3.1 Flash at time of writing; available via the MiniMax API and MiniMax Agent.
MiniMax M3.1 Flash has a 1,000,000-token context window — enough for large multi-file codebases or long agent trajectories in a single request. Sparse attention and DSpark speculative decoding are designed to keep this long-context setting efficient at inference time.
| Benchmark | Score | Source | Date |
|---|---|---|---|
| BankerToolBench | 76.2 | MiniMax M3.1 Flash model card | 2026-10 |
| Terminal-Bench 4.0 | 38.1% | MiniMax M3.1 Flash model card | 2026-10 |
| Agents' Last Exam | 29.6% | MiniMax M3.1 Flash model card | 2026-10 |
| OSWorld 2.0 | 61.7% | MiniMax M3.1 Flash model card | 2026-10 |
| PostTrainBench | 39.3% | MiniMax M3.1 Flash model card | 2026-10 |
| DeepSWE | 73.7% | MiniMax M3.1 Flash model card | 2026-10 |
| NL2Repo-Bench | 61.2% | MiniMax M3.1 Flash model card | 2026-10 |
| CyberGym | 82.2% | MiniMax M3.1 Flash model card | 2026-10 |
| BabyVision | 91.0% (with tools) | MiniMax M3.1 Flash model card | 2026-10 |
| Humanity's Last Exam | 43.4% (text-only, without tools) | MiniMax M3.1 Flash model card | 2026-10 |
| CharXiv Reasoning | 82.5% | MiniMax M3.1 Flash model card | 2026-10 |
Scores above are reported by MiniMax and shown for context. They are not Benchgen measurements. Figures depend on harness, tools, and effort settings — see the source for methodology.
Excluded vendor scores (not mapped to any local benchmark entity): Automation-Bench v1.0.6 (62.8%) — distinct from the locally-tracked Zapier AutomationBench despite the similar name, no confirmed shared methodology; GDP.pdf AA (20.8) — ambiguous metric scale/label in the vendor table, not confidently mappable to GDPVal-AA v2's Elo scale; LOCA-Bench 256K (60.3%) — no matching local entity; SWE Refactor Bench (23.0%) — distinct from the locally-tracked Scale AI SWE Atlas – Refactoring benchmark, no confirmed shared methodology. Each excluded per the scope-assumption rule rather than guessed.
| Model | Context | DeepSWE | OSWorld 2.0 | License |
|---|---|---|---|---|
| MiniMax M3.1 Flash | 1M | 73.7% | 61.7% | MiniMax Community |
| MiniMax M3 | — | — | — | MiniMax Commercial |
| DeepSeek V4.1 Flash-1017 | — | 77.0% | — | — |
MiniMax M3.1 Flash trades some raw terminal-use performance (Terminal-Bench 4.0) for stronger cybersecurity and coding-agent results versus its DeepSeek Flash-class rivals.
The public numbers span enterprise tool-use (BankerToolBench), computer-use (OSWorld 2.0), and long-horizon coding (DeepSWE, NL2Repo-Bench) — a broader agentic footprint than M3's narrower CLAW Bench-only disclosure. A leaderboard score establishes general capability on a fixed task distribution; builders evaluating M3.1 Flash for a specific domain (e.g. banking workflows or vulnerability research) should still validate against their own task set, since harness and tool-access settings materially affect reported scores.
from openai import OpenAI # MiniMax exposes an OpenAI-compatible API
client = OpenAI(base_url="https://api.minimax.io/v1", api_key="YOUR_API_KEY")
response = client.chat.completions.create(
model="MiniMax-M3.1-Flash",
messages=[{"role": "user", "content": "Summarize this contract clause..."}],
)
print(response.choices[0].message.content)Specs and scores sourced from MiniMax's official M3.1 Flash model card (October 2026). Pricing cited to the provider where available. Last updated 2026-10-24.