MiniMax M3 Now on SiliconFlow: Frontier Coding, 1M Context, and Native Multimodality

TL; DR: Built on the new MiniMax Sparse Attention (MSA) architecture, MiniMax M3 unites frontier-grade coding and agentic capability, a 1M-token context window, and native multimodality in a single open-weight model. It scores 83.5 on BrowseComp — ahead of Opus 4.7 (79.3) — and tops GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro, while delivering up to 9.7× prefilling and 15.6× faster decoding at full context. Start building with SiliconFlow's API right now.

Try MiniMax M3 on SiliconFlow

• Cost-effective Pricing: $0.12 / $0.60 / $2.40 per 1M tokens (Cached Input / Input / Output); and 50% off for 7 days through June 7th: $0.06 / $0.30 / $1.20 per 1M tokens (Cached Input / Input / Output).

• Seamless Integration: Instant compatibility with your existing development ecosystem, deploy via SiliconFlow's OpenAI-Compatible API through Cline, Gen-CLI, Kilo Code, Roo Code ; Anthropic-Compatible API with Claude Code; plug into agents like OpenClaw, Hermes Agent; ready-to-use in Dify, Janitor AI, Chub AI, ChatHub, Chatbox, Sider; and also available through OpenRouter.

Key Features & Benchmark Performance

Overview

MiniMax M3, the first open-weight model with three frontier capabilities, pairs frontier-level software engineering and agentic performance with a 1M-token context window and multimodal understanding trained in from step zero, enabling developers to run long-horizon coding agents, whole-codebase analysis, and image & video grounded workflows with greater efficiency and reliability.

As a next-generation agentic foundation model, MiniMax M3 sets a new benchmark for what an open model can do at extreme context lengths — closing the gap with the small set of closed frontier systems that, until now, were the only ones to own all three capabilities at once.

Highlights

• Frontier coding and agentic capability, with autonomous task decomposition, tool invocation, and multi-step reasoning.

• A 1M-token context window powered by MSA, with a guaranteed minimum of 512K tokens.

• Native multimodality across text, image, and video inputs — plus computer-use, baked into the model rather than bolted on.

• The first open-weight model to combine all three frontier capabilities in one system.

Benchmark Performance

Coding and agentic capabilities are key areas of improvement for MiniMax M3.

• SWE-Bench Pro: Outperforms GPT-5.5 and Gemini 3.1 Pro on software-engineering tasks, approaching Opus 4.7.

• BrowseComp: 83.5, surpassing Opus 4.7 (79.3) on autonomous browsing and information retrieval.

• SVG-Bench: 63.7, edges out Opus 4.7 (62.3) , GPT-5.5 (58.2) and Gemini 3.1 Pro (59.2) on SVG generation performance.

Architectural Innovations

MiniMax M3 introduces MiniMax Sparse Attention (MSA), a clean and scalable architecture that makes context a dimension can actually scale. Its enhancements include:

• Precise KV-block selection: MSA adds a lightweight screening stage that chunks the KV cache more accurately than schemes like DSA and MoBA, giving higher effective context coverage instead of just cheaper attention.

• Operator-level optimization: A KV-outer-gather-Q kernel reads each block once with contiguous memory access, running more than 4× faster than open implementations such as Flash-Sparse-Attention and flash-moba.

• Order-of-magnitude efficiency at scale: At 1M tokens, M3 spends about 1/20 the per-token compute of the prior generation, with prefill accelerated more than 9× and decoding more than 15×.

Real-World Applications

MiniMax M3 empowers developers and businesses to create cutting-edge solutions for:

• Autonomous research and paper reproduction: Handed an ICLR 2025 Outstanding Paper, M3 ran for nearly 12 hours on its own, producing 18 commits and 23 experimental figures and successfully replicating the core results — reading the paper's charts and formulas, holding paper, code, and logs in a single context window, and driving the whole long-horizon job to completion.

• Long-horizon systems engineering: Starting from only a task description and a non-runnable Triton skeleton, M3 optimized an FP8 GEMM kernel on NVIDIA Hopper GPUs across 147 benchmark submissions and 1,959 tool calls, pushing hardware peak utilization from 7.6% to 71.3% — a 9.4× speedup with zero human intervention.

• Self-improving ML pipelines: Given four pretrain-only base models, M3 autonomously ran data synthesis, training, evaluation, and iteration within 12 hours to make them usable for math, code, and knowledge tasks — no human in the loop.

From AI-native software development to scientific research, the model helps teams accelerate development and unlock new AI-driven possibilities.

Get Started Immediately

1. Bulid: Try MiniMax M3 in the SiliconFlow playground.

2. Explore: More models from MiniMax such as MiniMax-M2.5 on SiliconFlow.

3. Integrate: Use our OpenAI-compatible API. Explore the full API specifications in the SiliconFlow API documentation.

import requests

url = "https://api.siliconflow.com/v1/chat/completions"

payload = {
    "model": "MiniMaxAI/MiniMax-M3",
    "messages": [
        {
            "role": "user",
            "content": "Tell me a 1000 word bedtime story"
        }
    ],
    "stream": True,
    "max_tokens": 4095,
    "enable_thinking": True,
    "min_p": 0.95,
    "temperature": 1,
    "top_k": 40,
    "frequency_penalty": 0
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

import requests

url = "https://api.siliconflow.com/v1/chat/completions"

payload = {
    "model": "MiniMaxAI/MiniMax-M3",
    "messages": [
        {
            "role": "user",
            "content": "Tell me a 1000 word bedtime story"
        }
    ],
    "stream": True,
    "max_tokens": 4095,
    "enable_thinking": True,
    "min_p": 0.95,
    "temperature": 1,
    "top_k": 40,
    "frequency_penalty": 0
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

import requests

url = "https://api.siliconflow.com/v1/chat/completions"

payload = {
    "model": "MiniMaxAI/MiniMax-M3",
    "messages": [
        {
            "role": "user",
            "content": "Tell me a 1000 word bedtime story"
        }
    ],
    "stream": True,
    "max_tokens": 4095,
    "enable_thinking": True,
    "min_p": 0.95,
    "temperature": 1,
    "top_k": 40,
    "frequency_penalty": 0
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

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