MiniMax-M2 vs Qwen3: which model should you choose?
We compared MiniMax-M2 and Qwen3 on price, context window and 12 benchmarks. MiniMax-M2 has the stronger average benchmark score; Qwen3 is cheaper on a blended 3:1 basis.
MiniMax-M2
MiniMax-M2 is a multimodal model from MiniMax. It offers a 200,000-token context window, costs $5.19 per million input tokens and $22.73 per million output tokens, and averages 80.6 across the index's benchmark suite.
- Input
- $5.19 / 1M
- Output
- $22.73 / 1M
- Context
- 200,000
- Avg score
- 80.6
- Open weights
- Yes
Qwen3
Qwen3 is a multimodal model from Alibaba Cloud. It offers a 128,000-token context window, costs $5.04 per million input tokens and $22.52 per million output tokens, and averages 80.3 across the index's benchmark suite.
- Input
- $5.04 / 1M
- Output
- $22.52 / 1M
- Context
- 128,000
- Avg score
- 80.3
- Open weights
- Yes
Specification comparison
| Spec | MiniMax-M2 | Qwen3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.19 | $5.04 | B |
| Output price / 1M | $22.73 | $22.52 | B |
| Context window | 200,000 | 128,000 | A |
| Max output | 65,536 | 8,192 | A |
| Average score | 80.6 | 80.3 | A |
| Arena Elo | 1423.8 | 1420.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
MiniMax-M2 wins 8, Qwen3 wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | MiniMax-M2 | Qwen3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.8 | 81.2 | 0.6 | MiniMax-M2 |
| GPQA Diamond | 75.4 | 77.7 | 2.3 | Qwen3 |
| SWE-bench Verified | 69.9 | 71.6 | 1.7 | Qwen3 |
| HumanEval | 88.0 | 87.0 | 1.0 | MiniMax-M2 |
| MATH-500 | 82.3 | 86.5 | 4.2 | Qwen3 |
| AIME 2025 | 78.1 | 74.5 | 3.6 | MiniMax-M2 |
| LiveCodeBench | 73.4 | 73.8 | 0.4 | Qwen3 |
| IFEval | 86.1 | 85.6 | 0.5 | MiniMax-M2 |
| TruthfulQA | 83.5 | 80.4 | 3.1 | MiniMax-M2 |
| HellaSwag | 90.4 | 88.1 | 2.3 | MiniMax-M2 |
| MMMU | 78.0 | 76.7 | 1.3 | MiniMax-M2 |
| Arena Elo | 1423.8 | 1420.9 | 2.9 | MiniMax-M2 |
Price analysis
On a blended 3:1 input-to-output basis, MiniMax-M2 costs about $9.57 per million tokens and Qwen3 costs about $9.41. Qwen3 is roughly 2% cheaper than MiniMax-M2. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
MiniMax-M2 has the larger context window at 200,000 tokens, compared with 128,000 for Qwen3. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, MiniMax-M2 wins 8, Qwen3 wins 4, and 0 are tied. On the index average, MiniMax-M2 leads Qwen3 by 0.3 points (80.6 versus 80.3).
The price picture favours Qwen3. The context picture favours MiniMax-M2. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with MiniMax-M2 for quality-sensitive work and Qwen3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose MiniMax-M2 if
- You want the higher average benchmark score in this index.
- You need the larger 200,000-token context window.
- You need the larger 65,536-token output limit.
Choose Qwen3 if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is MiniMax-M2 better than Qwen3?
On the index average, MiniMax-M2 leads Qwen3 by 0.3 points (80.6 versus 80.3). The better choice depends on price, context and your task. MiniMax-M2 wins 8 benchmarks, Qwen3 wins 4.
Which is cheaper, MiniMax-M2 or Qwen3?
Qwen3 is cheaper on a blended 3:1 basis at about $9.41 per million tokens, versus $9.57 for MiniMax-M2.
Which has the larger context window?
MiniMax-M2 has the larger context window at 200,000 tokens. Qwen3 has 128,000 tokens.
Are these models open weights?
MiniMax-M2 is open weights; Qwen3 is open weights.
Which should I choose for production?
If you need a single default, start with MiniMax-M2 for quality-sensitive work and Qwen3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.