MiniMax M3.1 Flash
m3-1-flashminimax-m3-1-flashminimax-m3.1-flashMiniMax-M3.1-Flashminimax/minimax-m3.1-flash- 📏 1M-token context for repositories, long documents, multi-step agent runs
- 👁️ Accepts text, image and video input; returns text (catalog)
- 🧠 Always-on thinking; reasoning returned separately from the final answer
- 🔧 Five effort levels — low through max, with max as default
- 🎯 Built for agentic reasoning, tool use, coding, structured task execution
- 💬 Supports function calling and web search in the catalog listing (catalog)
- 🏢 Debuted inside MiniMax Code and the company's subscription plans
- 🆕 Newest text model in MiniMax's M-series lineup (catalog)
MiniMax is an AI company building generative models across multiple modalities, with a focus that spans both language understanding and audio creation. Their rapid release cadence in early 2026—delivering several new models within just a few months—reflects an ambitious and…
Explore 10 more models by Minimax →MiniMax M3.1 Flash is the newest entry in MiniMax's M-series of text models, positioned as a fast, multimodal option for coding and agentic work. MiniMax's own documentation describes it as the latest M-series language model for agentic reasoning, tool use, coding and long-context tasks, with a 1,000,000-token context window intended for long documents, whole codebases and multi-step agent sessions. The catalog listing records text, image and video input with text output, along with function calling and web search.
Within the family, it follows the M2 generation — including MiniMax M2.5 and MiniMax M2.7 — and the million-token MiniMax M3 Preview, sitting alongside MiniMax's separate media lines such as the MiniMax H3 Max video models and MiniMax Music 2.6.
The distinguishing control in M3.1 Flash is tunable thinking depth. The effort setting accepts low, medium, high, xhigh and max, with max applied when the field is omitted; higher settings produce more thorough reasoning at the cost of extra output tokens and latency. Thinking cannot be disabled — MiniMax states that reasoning before answering measurably improves accuracy on agentic reasoning, tool use, coding and maths.
Reasoning traces are returned in a separate field from the answer, so applications can display results without stripping markers out of the text. The model first shipped inside MiniMax's own coding agent and subscription plans.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · MiniMax M3.1 Flash · Organic sellers
Top sellers · MiniMax M3.1 Flash · Organic
- ZLKPro-Api31.0%
- attacompute.ai27.3%
- Best27.0%
- Venice.ai Proxy11.0%
- zro2.4%
- Hana Gateway ✅0.6%
- Others0.7%
| Seller | Health | Rep↓ | Sales | Tokens | Routing | Input $/M | Cached $/M | Output $/M | Save | 1st token | tok/s |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | healthy | 81.71 | $0.01 | 119.81k | #1 | $0.09 | $0.009 | $0.375 | — | — | — |
| attacompute.ai 0xd5e7…7be0 | healthy | 51.22 | $0.84 | 84.21M | gated | $0.01 | $0.01 | $0.01 | −87% | 6.1 s | 84 |
| SCloud 0x0da6…cd0c | healthy | 25.00 | $0.00 | 15.57k | gated | $0.015 | $0.0015 | $0.06 | −80% | — | — |
| ZLKPro-Api 0x0b0b…f446 | healthy | 20.91 | $0.95 | 111.22M | gated | $0.033 | $0.015 | $0.13 | −56% | — | — |
| OpenCheap 0x0ee1…6c41 | — | 12.43 | $0.00 | 6.03k | gated | $0.0804 | $0.008 | $0.3216 | — | — | — |
| Sapiens 0x1fae…8318 | healthy | 8.21 | $0.00 | 2.41k | gated | $0.1134 | $0.0113 | $0.4792 | — | — | — |
| dealer 0xc46e…8ce6 | — | 2.57 | $0.00 | 16.08k | gated | $0.089 | $0.0089 | $0.3708 | — | — | — |
| surplus-hive 0x6145…5c58 | — | 0.00 | — | — | gated | $0.4557 | $0.4557 | $1.8229 | — | — | — |
| LLMart 0x948a…a000 | — | 0.00 | $0.00 | 1.62k | gated | $0.1688 | $0.0149 | $0.675 | — | — | — |
| Fire Ant 🔥🐜 0xbe05…bc5d | — | 0.00 | $0.00 | 18.45k | gated | $0.1688 | $0.0118 | $0.4725 | — | — | — |
| Edith AI 0xb269…b1a6 | healthy | 0.00 | —excluded | — | gated | $0.1031 | $0.0103 | $0.4125 | — | — | — |
| Venice.ai Proxy 0x1f22…18c9 | failingupstream error · 3.7 h | 86.84 | $0.34 | 6.42M | #2 | $0.1031 | $0.0103 | $0.4125 | — | — | — |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.