GLM-4.6 Instruct vs MiniMax-M2: which model should you choose?
We compared GLM-4.6 Instruct and MiniMax-M2 on price, context window and 12 benchmarks. MiniMax-M2 has the stronger average benchmark score; MiniMax-M2 is cheaper on a blended 3:1 basis.
GLM-4.6 Instruct
GLM-4.6 Instruct is a multimodal model from Zhipu AI. It offers a 1,000,000-token context window, costs $5.15 per million input tokens and $24.32 per million output tokens, and averages 80.4 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $24.32 / 1M
- Context
- 1,000,000
- Avg score
- 80.4
- 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 Instruct | MiniMax-M2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.15 | $5.19 | A |
| Output price / 1M | $24.32 | $22.73 | B |
| Context window | 1,000,000 | 200,000 | A |
| Max output | 128,000 | 65,536 | A |
| Average score | 80.4 | 80.6 | B |
| Arena Elo | 1426.6 | 1423.8 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 Instruct wins 8, MiniMax-M2 wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 Instruct | MiniMax-M2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 81.8 | 1.2 | MiniMax-M2 |
| GPQA Diamond | 75.8 | 75.4 | 0.4 | GLM-4.6 Instruct |
| SWE-bench Verified | 70.5 | 69.9 | 0.6 | GLM-4.6 Instruct |
| HumanEval | 85.3 | 88.0 | 2.7 | MiniMax-M2 |
| MATH-500 | 84.9 | 82.3 | 2.6 | GLM-4.6 Instruct |
| AIME 2025 | 74.5 | 78.1 | 3.6 | MiniMax-M2 |
| LiveCodeBench | 72.6 | 73.4 | 0.8 | MiniMax-M2 |
| IFEval | 87.4 | 86.1 | 1.3 | GLM-4.6 Instruct |
| TruthfulQA | 83.8 | 83.5 | 0.3 | GLM-4.6 Instruct |
| HellaSwag | 90.6 | 90.4 | 0.2 | GLM-4.6 Instruct |
| MMMU | 78.9 | 78.0 | 0.9 | GLM-4.6 Instruct |
| Arena Elo | 1426.6 | 1423.8 | 2.8 | GLM-4.6 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 Instruct costs about $9.94 per million tokens and MiniMax-M2 costs about $9.57. MiniMax-M2 is roughly 4% cheaper than GLM-4.6 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens, compared with 200,000 for MiniMax-M2. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 Instruct wins 8, MiniMax-M2 wins 4, and 0 are tied. On the index average, MiniMax-M2 leads GLM-4.6 Instruct by 0.2 points (80.6 versus 80.4).
The price picture favours MiniMax-M2. The context picture favours GLM-4.6 Instruct. 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 check whether its price holds at your expected volume before committing.
Choose GLM-4.6 Instruct if
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
Choose MiniMax-M2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
FAQ
Is GLM-4.6 Instruct better than MiniMax-M2?
On the index average, MiniMax-M2 leads GLM-4.6 Instruct by 0.2 points (80.6 versus 80.4). The better choice depends on price, context and your task. GLM-4.6 Instruct wins 8 benchmarks, MiniMax-M2 wins 4.
Which is cheaper, GLM-4.6 Instruct or MiniMax-M2?
MiniMax-M2 is cheaper on a blended 3:1 basis at about $9.57 per million tokens, versus $9.94 for GLM-4.6 Instruct.
Which has the larger context window?
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens. MiniMax-M2 has 200,000 tokens.
Are these models open weights?
GLM-4.6 Instruct 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 check whether its price holds at your expected volume before committing.