# MiniMax M3.1 Flash

URL: https://interfaze.ai/models/minimaxaiminimax-m31-flash

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MiniMax M3.1 Flash by MiniMaxAI, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | MiniMax M3.1 Flash | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | MiniMax M3.1 Flash | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash)

**MiniMax-M3.1-Flash** is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to **1M tokens**.

Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding.

**Highlights:**

-   **Agentic performance:** A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym.
-   **Efficient by design:** Sparse attention throughout, quantization-aware training, and DSpark speculative decoding improve inference efficiency.

## Model at a Glance

-   **Architecture:** Native multimodal mixture-of-experts (MoE)
-   **Parameters:** Approximately 428B total; 23B activated per token
-   **Inputs / output:** Text, image, and video inputs; text output
-   **Context window:** Up to 1,000,000 tokens
-   **Attention:** [MiniMax Sparse Attention](https://arxiv.org/abs/2606.13392) (MSA) throughout the model
-   **Speculative decoding:** DSpark draft head included

## Benchmarks

-   **Automation-Bench v1.0.6:** 600 publicly released tasks.
-   **BankerToolBench:** OpenCode 1.17.18; MiniMax-M2.7 judge.
-   **GDP.pdf AA:** Taken directly from Artificial Analysis.
-   **Terminal Bench 4.0:** Codex 0.149.1.
-   **LOCA-Bench 256K:** Official implementation.
-   **Agents’ Last Exam:** Official Overall setting; full-set results from the official ALE leaderboard only. Blank = no official result (CLI-only excluded), not zero.
-   **OSWorld 2.0:** 0808 Full release; highest reported per model at release, except DeepSeek V4.1 (in-house reproduction).
-   **PostTrain Bench v1.1:** Claude Code 2.1.251, 512K context; otherwise official setup.
-   **DeepSWE v1.1:** mini-SWE-agent 2.4.5, 512K context.
-   **NL2Repo-Bench:** Claude Code 2.1.233, 512K context; reward hacking = 0; two inconsistent tasks corrected (scores use the corrected set).
-   **SWE Refactor Bench:** Claude Code 2.1.220, 200K context; otherwise official setup.
-   **CyberGym:** Claude Code 2.1.263, 1M context.
-   **BabyVision (with tools):** Claude Code 2.1.285; min image side 1,500 px.
-   **HLE (without tools):** Text-only subset (2,158 questions).
-   **CharXiv RQ:** GPT-5.5 judge.

Results are for the official weights; third-party quantizations may differ.

## What’s New in M3.1-Flash

M3.1-Flash retains M3’s core mixture-of-experts architecture, with three main changes:

-   **Sparse attention throughout:** the first three full-attention layers are replaced with sparse attention.
-   **Quantization-aware training:** the routed MoE experts and the attention paths are quantized during training, reducing memory footprint and improving serving efficiency.
-   **DSpark speculative decoding:** DSpark replaces the earlier EAGLE-like MTP approach. The release includes the draft head for use with compatible serving implementations.

## How to Use

-   [MiniMax Agent](https://agent.minimax.io/)
-   [MiniMax API](https://platform.minimax.io/)

### Local Deployment

Download the model:

```
hf download MiniMaxAI/MiniMax-M3.1-Flash --local-dir MiniMax-M3.1-Flash
```

We recommend the following inference frameworks to serve the model:

-   [SGLang](https://docs.sglang.io/) — see [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M3.1).
-   [vLLM](https://github.com/vllm-project/vllm) — see [vLLM recipe](https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3.1-Flash).
-   [TokenSpeed](https://github.com/lightseekorg/tokenspeed) — see [TokenSpeed recipe](https://lightseek.org/tokenspeed/recipes/models#minimax-m3-1).

Additional integrations in progress: Transformers, KTransformers, unsloth, and ATOM.

### Multi-Chip Deployment

[FlagOS](https://github.com/flagos-ai) — see [Multi-Chip Support and Downloads](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash/blob/main/docs/FlagOS-Multi-Chip-Support-and-Downloads.md).

## Inference Settings

Recommended sampling settings: `temperature=1.0` and `top_p=0.95`.

For reasoning, set `reasoning_effort` in the chat template: `low`, `medium`, `high`, `xhigh`, or `max`.

The model supports a context window of up to 1M tokens. Usable context length and throughput depend on GPU memory and the serving configuration.

## Contact Us

Contact us at [model@minimax.io](mailto:model@minimax.io).

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)