MiniMax-M2

MiniMax-M2

MiniMaxllm

Grande modelo de linguagem (llm) desenvolvido pela MiniMax — Intelligence Index 36/100 no Artificial Analysis; US$ 0.300/1M tokens de entrada; 83 tokens/s de velocidade.

API AvailableTool Calling

Specifications

Context Window

205K tokens

Input Price/1M

$0.30

Output Price/1M

$1.20

Parameters

Speed

91 tok/s

Latency (TTFT)

1.1s

Max Output

197K tokens

Benchmarks

MiniMax-M2 results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Coding

BenchmarkScoreMaximumMethodology
LiveCodeBench81.0100.0Artificial Analysis official API
AA Coding Index32.8100.0Artificial Analysis official API

Knowledge

BenchmarkScoreMaximumMethodology
MMLU-Pro82.0100.0

Math

BenchmarkScoreMaximumMethodology
AA Math Index82.7100.0Artificial Analysis official API
AIME 202582.7100.0Artificial Analysis official API

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index39.4100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
MMLU Pro87.5100.0Artificial Analysis official API
GPQA Diamond83.0100.0Artificial Analysis official API
HLE13.0100.0

Information

Release date
October 26, 2025
Tool Calling
✅ Supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: MiniMax-M2

What is MiniMax-M2?

MiniMax-M2 is an AI model developed by MiniMax, classified as a llm model. It focuses on text processing and natural language generation. As a proprietary model, it is available via MiniMax's cloud API. With a context window of 205K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.

Pricing & Costs in 2026

MiniMax-M2 is usage-based, priced at $0.3/1M input tokens and $1.2/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

MiniMax-M2 was evaluated on 9 different benchmarks, covering categories like Coding, Knowledge, Math, overall, Reasoning. Results show solid performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

MiniMax-M2 is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.

Comparison with Alternatives

In the 2026 AI model ecosystem, MiniMax-M2 competes directly with similarly capable models. MiniMax competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is MiniMax-M2?

MiniMax-M2 is an AI model developed by MiniMax. It is a llm model.

How much does MiniMax-M2 cost?

MiniMax-M2 costs $0.3/1M input tokens and $1.2/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does MiniMax-M2 compare with other models?

In available benchmarks, MiniMax-M2 scored: LiveCodeBench: 81/100, AA Coding Index: 32.8/100, MMLU-Pro: 82/100. See the full table above for a detailed comparison.

Is MiniMax-M2 open source?

No, MiniMax-M2 is a proprietary model from MiniMax. It is available via cloud API. For open source alternatives, check our open source model ranking.

What is MiniMax-M2 best for?

MiniMax-M2 excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.

Last updated: May 24, 2026 View methodology →