MiniMax-M2
MiniMax • llm
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.
Specifications
Context Window
205K tokens
Input Price/1M
$0.30
Output Price/1M
$1.20
Parameters
—
Speed
73 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.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 26.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 83.0 | 100.0 | Artificial Analysis official API |
| LiveBench Coding | 70.7 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
| AA Coding Index | 52.6 | 100.0 | Artificial Analysis official API |
| SciCode | 36.0 | 100.0 | — |
Data Analysis
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveBench Data Analysis | 49.6 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 82.0 | 100.0 | — |
Language
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveBench Language | 55.1 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 61.0 | 100.0 | — |
Math
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Math Index | 78.3 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 78.0 | 100.0 | Artificial Analysis official API |
| LiveBench Math | 77.4 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LMArena Elo | 1346.0 | 2000.0 | Crowdsourced blind pairwise comparisons |
| LiveBench Global | 60.1 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
| AA Intelligence Index | 28.3 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 82.0 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 78.0 | 100.0 | Artificial Analysis official API |
| IFBench | 72.0 | 100.0 | — |
| LiveBench Reasoning | 59.3 | 100.0 | Contamination-free benchmark with objective ground-truth answers |
| HLE | 13.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 87.0 | 100.0 | — |
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 21 different benchmarks, covering categories like Agentic, Coding, Data Analysis, Knowledge, Language, Long Context, Math, overall, Reasoning, Tool Use. Results show exceptional 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: Terminal-Bench Hard: 26/100, LiveCodeBench: 83/100, LiveBench Coding: 70.7/100. 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: July 17, 2026 • View methodology →