GLM-4.6 vs MiniMax-M2: which model should you choose?
We compared GLM-4.6 and MiniMax-M2 on price, context window and 12 benchmarks. MiniMax-M2 has the stronger average benchmark score; GLM-4.6 is cheaper on a blended 3:1 basis.
GLM-4.6
GLM-4.6 is a multimodal model from Zhipu AI. It offers a 128,000-token context window, costs $5.10 per million input tokens and $18.45 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.10 / 1M
- Output
- $18.45 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
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
Specification comparison
| Spec | GLM-4.6 | MiniMax-M2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.19 | A |
| Output price / 1M | $18.45 | $22.73 | A |
| Context window | 128,000 | 200,000 | B |
| Max output | 8,192 | 65,536 | B |
| Average score | 80.2 | 80.6 | B |
| Arena Elo | 1440.7 | 1423.8 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 6, MiniMax-M2 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | MiniMax-M2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 81.8 | 2.2 | GLM-4.6 |
| GPQA Diamond | 78.3 | 75.4 | 2.9 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 69.9 | 2.5 | MiniMax-M2 |
| HumanEval | 83.8 | 88.0 | 4.2 | MiniMax-M2 |
| MATH-500 | 83.4 | 82.3 | 1.1 | GLM-4.6 |
| AIME 2025 | 80.2 | 78.1 | 2.1 | GLM-4.6 |
| LiveCodeBench | 71.6 | 73.4 | 1.8 | MiniMax-M2 |
| IFEval | 86.4 | 86.1 | 0.3 | GLM-4.6 |
| TruthfulQA | 82.5 | 83.5 | 1.0 | MiniMax-M2 |
| HellaSwag | 89.4 | 90.4 | 1.0 | MiniMax-M2 |
| MMMU | 75.4 | 78.0 | 2.6 | MiniMax-M2 |
| Arena Elo | 1440.7 | 1423.8 | 16.9 | GLM-4.6 |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 costs about $8.44 per million tokens and MiniMax-M2 costs about $9.57. GLM-4.6 is roughly 12% 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 GLM-4.6. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 6, MiniMax-M2 wins 6, and 0 are tied. On the index average, MiniMax-M2 leads GLM-4.6 by 0.4 points (80.6 versus 80.2).
The price picture favours GLM-4.6. 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 GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GLM-4.6 if
- Unit cost matters and you want the cheaper blended price.
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.
FAQ
Is GLM-4.6 better than MiniMax-M2?
On the index average, MiniMax-M2 leads GLM-4.6 by 0.4 points (80.6 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 6 benchmarks, MiniMax-M2 wins 6.
Which is cheaper, GLM-4.6 or MiniMax-M2?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 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. GLM-4.6 has 128,000 tokens.
Are these models open weights?
GLM-4.6 is open weights; MiniMax-M2 is open weights.
Which should I choose for production?
If you need a single default, start with MiniMax-M2 for quality-sensitive work and GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.