MiniMax-M2 Chat vs Qwen3: which model should you choose?
We compared MiniMax-M2 Chat and Qwen3 on price, context window and 12 benchmarks. Qwen3 has the stronger average benchmark score; MiniMax-M2 Chat is cheaper on a blended 3:1 basis.
MiniMax-M2 Chat
MiniMax-M2 Chat is a multimodal model from MiniMax. It offers a 200,000-token context window, costs $5.02 per million input tokens and $18.95 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.02 / 1M
- Output
- $18.95 / 1M
- Context
- 200,000
- Avg score
- 80.2
- 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 Chat | Qwen3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.02 | $5.04 | A |
| Output price / 1M | $18.95 | $22.52 | A |
| Context window | 200,000 | 128,000 | A |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 80.2 | 80.3 | B |
| Arena Elo | 1443.0 | 1420.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
MiniMax-M2 Chat wins 6, Qwen3 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | MiniMax-M2 Chat | Qwen3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 81.2 | 3.1 | MiniMax-M2 Chat |
| GPQA Diamond | 77.8 | 77.7 | 0.1 | MiniMax-M2 Chat |
| SWE-bench Verified | 66.4 | 71.6 | 5.2 | Qwen3 |
| HumanEval | 84.2 | 87.0 | 2.8 | Qwen3 |
| MATH-500 | 86.0 | 86.5 | 0.5 | Qwen3 |
| AIME 2025 | 78.3 | 74.5 | 3.8 | MiniMax-M2 Chat |
| LiveCodeBench | 70.4 | 73.8 | 3.4 | Qwen3 |
| IFEval | 88.0 | 85.6 | 2.4 | MiniMax-M2 Chat |
| TruthfulQA | 80.3 | 80.4 | 0.1 | Qwen3 |
| HellaSwag | 87.8 | 88.1 | 0.3 | Qwen3 |
| MMMU | 79.2 | 76.7 | 2.5 | MiniMax-M2 Chat |
| Arena Elo | 1443.0 | 1420.9 | 22.1 | MiniMax-M2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, MiniMax-M2 Chat costs about $8.50 per million tokens and Qwen3 costs about $9.41. MiniMax-M2 Chat is roughly 10% cheaper than Qwen3. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
MiniMax-M2 Chat 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 Chat wins 6, Qwen3 wins 6, and 0 are tied. On the index average, Qwen3 leads MiniMax-M2 Chat by 0.1 points (80.3 versus 80.2).
The price picture favours MiniMax-M2 Chat. The context picture favours MiniMax-M2 Chat. 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 Qwen3 for quality-sensitive work and MiniMax-M2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose MiniMax-M2 Chat if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 200,000-token context window.
Choose Qwen3 if
- You want the higher average benchmark score in this index.
FAQ
Is MiniMax-M2 Chat better than Qwen3?
On the index average, Qwen3 leads MiniMax-M2 Chat by 0.1 points (80.3 versus 80.2). The better choice depends on price, context and your task. MiniMax-M2 Chat wins 6 benchmarks, Qwen3 wins 6.
Which is cheaper, MiniMax-M2 Chat or Qwen3?
MiniMax-M2 Chat is cheaper on a blended 3:1 basis at about $8.50 per million tokens, versus $9.41 for Qwen3.
Which has the larger context window?
MiniMax-M2 Chat has the larger context window at 200,000 tokens. Qwen3 has 128,000 tokens.
Are these models open weights?
MiniMax-M2 Chat is open weights; Qwen3 is open weights.
Which should I choose for production?
If you need a single default, start with Qwen3 for quality-sensitive work and MiniMax-M2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.