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MiniMax

MiniMax-M2

MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency.

🔧Tool calling
Try MiniMax-M2CompareDocs

Specifications

Context window200K tokens
Max output128K tokens
API typechat
AddedOct 28, 2025
Model IDminimaxi/MiniMax-M2
Data retentionYes
Used for trainingUnknown
Provider location🇸🇬 Singapore
Privacy policyMiniMax Privacy Policy →

Benchmarks

Released 2025-10

Strong real-world coding via agentic training.

SWE-Bench Verifiedcoding
69.3%

Resolving real GitHub issues from 12 popular Python repositories.

GPQA Diamondreasoning
62.5%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

MMLU Proknowledge
79.2%

Massive Multitask Language Understanding across 57 academic subjects.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality — always test on your own workload.

Pricing

Input / 1M
$0.30
Output / 1M
$1.20
Cache write
—
Cache read
—
Estimated cost
100K input + 10K output$0.0420
1M input + 100K output$0.42
10M input + 1M output$4.20

Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to minimaxi/MiniMax-M2.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="minimaxi/MiniMax-M2",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Other MiniMax models

MiniMax-M2.7
200K ctx$0.30/1M
MiniMax-M2.7-highspeed
200K ctx$0.60/1M
MiniMax-M2.5-highspeed
200K ctx$0.60/1M
MiniMax-M2.5
200K ctx$0.30/1M

Frequently asked questions

How much does MiniMax-M2 cost?
MiniMax-M2 is priced at $0.30 per million input tokens and $1.20 per million output tokens when accessed via Requesty. Requesty charges exactly what the upstream provider charges — we don't add markup.
What is the context window of MiniMax-M2?
MiniMax-M2 has a context window of 200K tokens, with a maximum output of 128K tokens per response. That's roughly 267 words of input you can fit in a single prompt.
How does MiniMax-M2 perform on benchmarks?
MiniMax-M2 scores 88.1% on HumanEval, 82.5% on MATH, 79.2% on MMLU Pro. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can MiniMax-M2 do?
MiniMax-M2 supports tool calling. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use MiniMax-M2 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "minimaxi/MiniMax-M2". The Quickstart above shows Python, JavaScript and cURL snippets.

Access MiniMax-M2 through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

Get started freeAll MiniMax models