GLM-4.6 vs MiniMax-M2 Instruct: which model should you choose?
We compared GLM-4.6 and MiniMax-M2 Instruct on price, context window and 12 benchmarks. GLM-4.6 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 Instruct
MiniMax-M2 Instruct is a multimodal model from MiniMax. It offers a 128,000-token context window, costs $5.11 per million input tokens and $25.04 per million output tokens, and averages 79.4 across the index's benchmark suite.
- Input
- $5.11 / 1M
- Output
- $25.04 / 1M
- Context
- 128,000
- Avg score
- 79.4
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | MiniMax-M2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.11 | A |
| Output price / 1M | $18.45 | $25.04 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 8,192 | 4,096 | A |
| Average score | 80.2 | 79.4 | A |
| Arena Elo | 1440.7 | 1442.2 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 8, MiniMax-M2 Instruct wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | MiniMax-M2 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 82.1 | 1.9 | GLM-4.6 |
| GPQA Diamond | 78.3 | 73.7 | 4.6 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 69.9 | 2.5 | MiniMax-M2 Instruct |
| HumanEval | 83.8 | 86.7 | 2.9 | MiniMax-M2 Instruct |
| MATH-500 | 83.4 | 82.1 | 1.3 | GLM-4.6 |
| AIME 2025 | 80.2 | 77.8 | 2.4 | GLM-4.6 |
| LiveCodeBench | 71.6 | 71.1 | 0.5 | GLM-4.6 |
| IFEval | 86.4 | 87.3 | 0.9 | MiniMax-M2 Instruct |
| TruthfulQA | 82.5 | 80.0 | 2.5 | GLM-4.6 |
| HellaSwag | 89.4 | 88.5 | 0.9 | GLM-4.6 |
| MMMU | 75.4 | 74.1 | 1.3 | GLM-4.6 |
| Arena Elo | 1440.7 | 1442.2 | 1.5 | MiniMax-M2 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 costs about $8.44 per million tokens and MiniMax-M2 Instruct costs about $10.09. GLM-4.6 is roughly 16% cheaper than MiniMax-M2 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 8, MiniMax-M2 Instruct wins 4, and 0 are tied. On the index average, GLM-4.6 leads MiniMax-M2 Instruct by 0.8 points (80.2 versus 79.4).
The price picture favours GLM-4.6. The context picture is effectively a tie. 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 GLM-4.6 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose GLM-4.6 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need the larger 8,192-token output limit.
Choose MiniMax-M2 Instruct if
- You prefer MiniMax's tooling or already use it.
FAQ
Is GLM-4.6 better than MiniMax-M2 Instruct?
On the index average, GLM-4.6 leads MiniMax-M2 Instruct by 0.8 points (80.2 versus 79.4). The better choice depends on price, context and your task. GLM-4.6 wins 8 benchmarks, MiniMax-M2 Instruct wins 4.
Which is cheaper, GLM-4.6 or MiniMax-M2 Instruct?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $10.09 for MiniMax-M2 Instruct.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
GLM-4.6 is open weights; MiniMax-M2 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with GLM-4.6 for quality-sensitive work, and check whether its price holds at your expected volume before committing.