Command A Max vs GLM-4.6: which model should you choose?
We compared Command A Max and GLM-4.6 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.
Command A Max
Command A Max is a multimodal model from Cohere. It offers a 400,000-token context window, costs $14.28 per million input tokens and $70.22 per million output tokens, and averages 79.9 across the index's benchmark suite.
- Input
- $14.28 / 1M
- Output
- $70.22 / 1M
- Context
- 400,000
- Avg score
- 79.9
- Open weights
- No
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
Specification comparison
| Spec | Command A Max | GLM-4.6 | Edge |
|---|---|---|---|
| Input price / 1M | $14.28 | $5.10 | B |
| Output price / 1M | $70.22 | $18.45 | B |
| Context window | 400,000 | 128,000 | A |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 79.9 | 80.2 | B |
| Arena Elo | 1443.7 | 1440.7 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Command A Max wins 6, GLM-4.6 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Command A Max | GLM-4.6 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 84.0 | 0.3 | Command A Max |
| GPQA Diamond | 74.5 | 78.3 | 3.8 | GLM-4.6 |
| SWE-bench Verified | 69.7 | 67.4 | 2.3 | Command A Max |
| HumanEval | 83.4 | 83.8 | 0.4 | GLM-4.6 |
| MATH-500 | 85.7 | 83.4 | 2.3 | Command A Max |
| AIME 2025 | 77.2 | 80.2 | 3.0 | GLM-4.6 |
| LiveCodeBench | 73.1 | 71.6 | 1.5 | Command A Max |
| IFEval | 86.7 | 86.4 | 0.3 | Command A Max |
| TruthfulQA | 82.2 | 82.5 | 0.3 | GLM-4.6 |
| HellaSwag | 87.1 | 89.4 | 2.3 | GLM-4.6 |
| MMMU | 74.6 | 75.4 | 0.8 | GLM-4.6 |
| Arena Elo | 1443.7 | 1440.7 | 3.0 | Command A Max |
Price analysis
On a blended 3:1 input-to-output basis, Command A Max costs about $28.27 per million tokens and GLM-4.6 costs about $8.44. GLM-4.6 is roughly 70% cheaper than Command A Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Command A Max has the larger context window at 400,000 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, Command A Max wins 6, GLM-4.6 wins 6, and 0 are tied. On the index average, GLM-4.6 leads Command A Max by 0.3 points (80.2 versus 79.9).
The price picture favours GLM-4.6. The context picture favours Command A Max. 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 Command A Max if
- You need the larger 400,000-token context window.
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 open weights for self-hosting or fine-tuning.
FAQ
Is Command A Max better than GLM-4.6?
On the index average, GLM-4.6 leads Command A Max by 0.3 points (80.2 versus 79.9). The better choice depends on price, context and your task. Command A Max wins 6 benchmarks, GLM-4.6 wins 6.
Which is cheaper, Command A Max or GLM-4.6?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $28.27 for Command A Max.
Which has the larger context window?
Command A Max has the larger context window at 400,000 tokens. GLM-4.6 has 128,000 tokens.
Are these models open weights?
Command A Max is proprietary; GLM-4.6 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.