Command A Pro vs GLM-4.6 Instruct: which model should you choose?
We compared Command A Pro and GLM-4.6 Instruct on price, context window and 12 benchmarks. Command A Pro has the stronger average benchmark score; GLM-4.6 Instruct is cheaper on a blended 3:1 basis.
Command A Pro
Command A Pro is a multimodal model from Cohere. It offers a 200,000-token context window, costs $14.28 per million input tokens and $68.05 per million output tokens, and averages 81.3 across the index's benchmark suite.
- Input
- $14.28 / 1M
- Output
- $68.05 / 1M
- Context
- 200,000
- Avg score
- 81.3
- Open weights
- No
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
Specification comparison
| Spec | Command A Pro | GLM-4.6 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.28 | $5.15 | B |
| Output price / 1M | $68.05 | $24.32 | B |
| Context window | 200,000 | 1,000,000 | B |
| Max output | 16,384 | 128,000 | B |
| Average score | 81.3 | 80.4 | A |
| Arena Elo | 1434.6 | 1426.6 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Command A Pro wins 8, GLM-4.6 Instruct wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Command A Pro | GLM-4.6 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.3 | 80.6 | 0.7 | Command A Pro |
| GPQA Diamond | 79.1 | 75.8 | 3.3 | Command A Pro |
| SWE-bench Verified | 71.2 | 70.5 | 0.7 | Command A Pro |
| HumanEval | 87.5 | 85.3 | 2.2 | Command A Pro |
| MATH-500 | 83.1 | 84.9 | 1.8 | GLM-4.6 Instruct |
| AIME 2025 | 78.2 | 74.5 | 3.7 | Command A Pro |
| LiveCodeBench | 74.0 | 72.6 | 1.4 | Command A Pro |
| IFEval | 89.0 | 87.4 | 1.6 | Command A Pro |
| TruthfulQA | 83.3 | 83.8 | 0.5 | GLM-4.6 Instruct |
| HellaSwag | 90.1 | 90.6 | 0.5 | GLM-4.6 Instruct |
| MMMU | 77.2 | 78.9 | 1.7 | GLM-4.6 Instruct |
| Arena Elo | 1434.6 | 1426.6 | 8.0 | Command A Pro |
Price analysis
On a blended 3:1 input-to-output basis, Command A Pro costs about $27.72 per million tokens and GLM-4.6 Instruct costs about $9.94. GLM-4.6 Instruct is roughly 64% cheaper than Command A Pro. 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 Command A Pro. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Command A Pro wins 8, GLM-4.6 Instruct wins 4, and 0 are tied. On the index average, Command A Pro leads GLM-4.6 Instruct by 0.9 points (81.3 versus 80.4).
The price picture favours GLM-4.6 Instruct. 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 Command A Pro for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Command A Pro if
- You want the higher average benchmark score in this index.
Choose GLM-4.6 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is Command A Pro better than GLM-4.6 Instruct?
On the index average, Command A Pro leads GLM-4.6 Instruct by 0.9 points (81.3 versus 80.4). The better choice depends on price, context and your task. Command A Pro wins 8 benchmarks, GLM-4.6 Instruct wins 4.
Which is cheaper, Command A Pro or GLM-4.6 Instruct?
GLM-4.6 Instruct is cheaper on a blended 3:1 basis at about $9.94 per million tokens, versus $27.72 for Command A Pro.
Which has the larger context window?
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens. Command A Pro has 200,000 tokens.
Are these models open weights?
Command A Pro is proprietary; GLM-4.6 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Command A Pro for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.