Model Comparison
MiniMax-M2.1
vs. MiMo-V2-Flash (Reasoning)
Comparing 2 AI models · 5 benchmarks · MiniMax, Xiaomi
Chat with MiniMax-M2.1 & MiMo-V2-FlashMost Affordable
$0.10/1M
Highest Intelligence
84.6% GPQA
Best for Coding
32.8 Coding Index
Price Difference
3.0x
input cost range
Composite Indices
Intelligence, Coding, Math
Standard Benchmarks
Academic and industry benchmarks
Benchmark Winners
5 tests MiniMax-M2.1
- MMLU Pro
- HLE
MiMo-V2-Flash (Reasoning)
- GPQA
- LiveCodeBench
- AIME 2025
| Metric | Mi MiniMax-M2.1 | Xi MiMo-V2-Flash (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.30/1M | $0.10/1M |
| Output Cost | $1.20/1M | $0.30/1M |
| Blended (3:1) | $0.52/1M | $0.15/1M |
| Specifications | ||
| Organization | MiniMax | Xiaomi |
| Release Date | Dec 23, 2025 | Dec 16, 2025 |
| Performance & Speed | ||
| Throughput | 61.8 tok/s | 126.5 tok/s |
| TTFT | 1561ms | 1679ms |
| Latency | 33925ms | 17492ms |
| Composite Indices | ||
| Intelligence | 39.4 | 39.2 |
| Coding | 32.8 | 31.8 |
| Math | 82.7 | 96.3 |
| Standard Benchmarks | ||
| GPQA | 83.0% | 84.6% |
| MMLU Pro | 87.5% | 84.3% |
| HLE | 22.2% | 21.1% |
| LiveCodeBench | 81.0% | 86.8% |
| MATH 500 | — | — |
| AIME 2025 | 82.7% | 96.3% |
| AIME (Original) | — | — |
| SciCode | 40.7% | 39.4% |
| LCR | 59.0% | 63.0% |
| IFBench | 69.9% | 64.2% |
| TAU-bench v2 | 85.4% | 95.0% |
| TerminalBench Hard | 28.8% | 28.0% |
Key Takeaways
MiMo-V2-Flash (Reasoning) offers the best value at $0.10/1M, making it ideal for high-volume applications and cost-conscious projects.
MiMo-V2-Flash (Reasoning) leads in reasoning capabilities with a 84.6% GPQA, excelling at complex analytical tasks and problem-solving.
MiniMax-M2.1 reaches a 32.8 coding index, making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
MiniMax-M2.1
- Code generation
- Software development
MiMo-V2-Flash (Reasoning)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
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Understanding Composite Indices
- Intelligence Index: Aggregated score combining MMLU-Pro, GPQA, and HLE benchmarks - measures overall reasoning and knowledge capabilities
- Coding Index: Composite metric from LiveCodeBench, SciCode, and LiveCodeBench Review - evaluates programming proficiency across multiple languages
- Math Index: Combined score from AIME, AIME 2025, and MATH-500 benchmarks - assesses mathematical reasoning from high school to competition level
Key Comparison Metrics
- Benchmark Scores: Standardized tests measuring intelligence, coding, math, and specialized capabilities - higher percentages indicate better performance
- Pricing Analysis: Compare input and output token costs across models - critical for budgeting API usage and scaling applications
- Performance Metrics: Throughput (tokens/second) and latency measurements for real-time application planning
- Context Windows: Maximum token capacity for processing documents and maintaining conversation history
How to Compare AI Models Effectively
Performance vs Cost
Balance benchmark scores against token pricing - flagship models offer 10-15% better performance but cost 5-10x more than smaller alternatives
Task-Specific Selection
Prioritize relevant indices: coding index for development tasks, math index for STEM applications, intelligence index for general reasoning
Real-World Testing
Use our free AI chat interface to test models with your specific prompts before committing to API integration
All benchmark scores, pricing data, and performance metrics are sourced from Artificial Analysis and updated daily. Compare models by intelligence, coding ability, math performance, speed, cost, or release date using our comprehensive AI model leaderboard.