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MiniMax M2.1

Inndata:$0.24/M
Utdata:$0.96/M
Kontekst:204,800(total input + output per request)
Maks utdata:131.1K
Utgitt:Oct 1, 2025

MiniMax M2.1: Significantly Enhanced Multi-Language Programming, Built for Real-World Complex Tasks

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Playground for MiniMax M2.1

Utforsk MiniMax M2.1's Playground โ€” et interaktivt miljรธ for รฅ teste modeller og kjรธre spรธrringer i sanntid. Prรธv prompts, juster parametere og iterer umiddelbart for รฅ akselerere utvikling og validere brukstilfeller.

What is MiniMax M2.1

MiniMax M2.1 is the follow-up release to the MiniMax M2 family, published by MiniMax on Dec 23, 2025. It is positioned as an open-source, production-oriented model designed specifically for coding, agentic multi-step workflows (tool use, multi-turn planning), and full-stack app generation (web, mobile, backend). The release emphasizes improved multi-language programming, better mobile / native app capabilities, concise responses, and improved tool/agent generalization.

Main features

  • MoE efficiency: Large total parameter count but only a small active subset per token (architecture designed to trade peak capacity for inference efficiency).
  • Coding-first optimizations: Strong polyglot code comprehension and generation across many languages (Python, TypeScript, Rust, Go, C++, Java, Swift, mobile native languages).
  • Agentic & tool workflows: Designed for tool calls, multi-step plans, and โ€œinterleaved thinkingโ€ / chained execution of composite instruction constraints.
  • Large context support & long outputs: Targeted at long-context developer workflows and agent trace/history.
  • Low-latency / high-throughput: Practical for interactive coding assistants and scaled agent loops due to the selective-activation design and deployment optimizations.

Technical capabilities and specifications

  • Architecture: Mixture-of-Experts (MoE) design.
  • Parameters: Reported design: ~230 billion total parameters with ~10 billion active parameters used per inference (MoE active subset). This is the same active-parameter efficiency model used in the M2 family.
  • Inference characteristics: Designed for low-latency interactive use, high-throughput batched inference, and agentic loops with frequent tool calls.
  • Streaming / function calling: supports streaming token output and advanced function-call/tool interfaces for structured I/O.

Benchmark performance

MiniMax released comparative benchmark claims and third-party aggregators reported scores at launch; representative published figures include:

Multi-SWE Bench / SWE-Bench (coding/agentic suites): provider and aggregator listings cite 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual for M2.1 (these are scoring aggregates for code-generation and code reasoning tasks).

M2.1 shows comprehensive improvements over M2 in test case generation, code optimization, code review and instruction following and M2.1 outperforms M2 and often matches or surpasses Claude Sonnet 4.5 on several coding subtasks.

MiniMax M2.1

Representative production use cases

  1. IDE code assistant & refactoring: Multi-file refactors, code review suggestions, automated test generation and patch generation across multiple languages.
  2. Agentic โ€œDigital Employeeโ€: Automating repetitive office workflows (searching ticket systems, summarizing documents, interacting with web apps through text-based commands) using tool integration and interleaved thinking.
  3. Multi-language engineering support: Teams that maintain polyglot codebases (Rust, Go, Java, C++, TypeScript) can use M2.1 for cross-language code synthesis and conversions.
  4. Automated code evaluation & test generation: Generating testcases, running code analysis and producing suggested fixes or optimizations as part of CI tooling.
  5. Local/on-prem research and customization: Organizations that require on-prem control can fine-tune or run M2.1 locally using published weights and recommended inference stacks.

How to access and use MiniMax M2.1 API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click โ€œAdd Tokenโ€ at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to MiniMax M2.1 API

Select the โ€œminimax-m2.1โ€ endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Chat-style APIs.

Insert your question or request into the content fieldโ€”this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

FAQ

Priser for MiniMax M2.1

Utforsk konkurransedyktige priser for MiniMax M2.1, designet for รฅ passe ulike budsjetter og bruksbehov. Vรฅre fleksible planer sikrer at du bare betaler for det du bruker, noe som gjรธr det enkelt รฅ skalere etter hvert som kravene dine vokser. Oppdag hvordan MiniMax M2.1 kan forbedre prosjektene dine samtidig som kostnadene holdes hรฅndterbare.

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Inndata:$0.24/M
Utdata:$0.96/M
Inndata:$0.3/M
Utdata:$1.2/M
-20%

Eksempelkode og API for MiniMax M2.1

Fรฅ tilgang til omfattende eksempelkode og API-ressurser for MiniMax M2.1 for รฅ effektivisere integreringsprosessen din. Vรฅr detaljerte dokumentasjon gir trinn-for-trinn-veiledning som hjelper deg med รฅ utnytte det fulle potensialet til MiniMax M2.1 i prosjektene dine.

#!/bin/bash

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY="${COMETAPI_KEY:-<YOUR_COMETAPI_KEY>}"

# minimax-m2.1: MiniMax M2.1 model via chat/completions
curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "minimax-m2.1",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }'

cURL Code Example

#!/bin/bash

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY="${COMETAPI_KEY:-<YOUR_COMETAPI_KEY>}"

# minimax-m2.1: MiniMax M2.1 model via chat/completions
curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "minimax-m2.1",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }'

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

# minimax-m2.1: MiniMax M2.1 model via chat/completions
completion = client.chat.completions.create(
    model="minimax-m2.1",
    messages=[
        {"role": "user", "content": "Hello! Tell me a short joke."}
    ]
)

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

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token
const COMETAPI_KEY = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const BASE_URL = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey: COMETAPI_KEY,
  baseURL: BASE_URL,
});

// minimax-m2.1: MiniMax M2.1 model via chat/completions
async function main() {
  const completion = await client.chat.completions.create({
    model: "minimax-m2.1",
    messages: [{ role: "user", content: "Hello! Tell me a short joke." }],
  });

  console.log(completion.choices[0].message.content);
}

main().catch(console.error);