GLM-4.6 vs Nova Pro Max: which model should you choose?
We compared GLM-4.6 and Nova Pro Max on price, context window and 12 benchmarks. Nova Pro Max 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
Nova Pro Max
Nova Pro Max is a multimodal model from Amazon. It offers a 1,000,000-token context window, costs $14.05 per million input tokens and $71.59 per million output tokens, and averages 81.2 across the index's benchmark suite.
- Input
- $14.05 / 1M
- Output
- $71.59 / 1M
- Context
- 1,000,000
- Avg score
- 81.2
- Open weights
- No
Specification comparison
| Spec | GLM-4.6 | Nova Pro Max | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $14.05 | A |
| Output price / 1M | $18.45 | $71.59 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 8,192 | 16,384 | B |
| Average score | 80.2 | 81.2 | B |
| Arena Elo | 1440.7 | 1423.2 | A |
| Open weights | Yes | No | A |
Benchmark comparison
GLM-4.6 wins 3, Nova Pro Max wins 6, and 3 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Nova Pro Max | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 84.2 | 0.2 | Nova Pro Max |
| GPQA Diamond | 78.3 | 78.3 | 0.0 | Tie |
| SWE-bench Verified | 67.4 | 67.4 | 0.0 | Tie |
| HumanEval | 83.8 | 87.5 | 3.7 | Nova Pro Max |
| MATH-500 | 83.4 | 83.8 | 0.4 | Nova Pro Max |
| AIME 2025 | 80.2 | 79.3 | 0.9 | GLM-4.6 |
| LiveCodeBench | 71.6 | 77.0 | 5.4 | Nova Pro Max |
| IFEval | 86.4 | 86.4 | 0.0 | Tie |
| TruthfulQA | 82.5 | 83.4 | 0.9 | Nova Pro Max |
| HellaSwag | 89.4 | 88.9 | 0.5 | GLM-4.6 |
| MMMU | 75.4 | 77.2 | 1.8 | Nova Pro Max |
| Arena Elo | 1440.7 | 1423.2 | 17.5 | 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 Nova Pro Max costs about $28.44. GLM-4.6 is roughly 70% cheaper than Nova Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Nova Pro Max has the larger context window at 1,000,000 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 3, Nova Pro Max wins 6, and 3 are tied. On the index average, Nova Pro Max leads GLM-4.6 by 1.0 points (81.2 versus 80.2).
The price picture favours GLM-4.6. The context picture favours Nova Pro 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 Nova Pro Max 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.
- You need open weights for self-hosting or fine-tuning.
Choose Nova Pro Max if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
- You need the larger 16,384-token output limit.
FAQ
Is GLM-4.6 better than Nova Pro Max?
On the index average, Nova Pro Max leads GLM-4.6 by 1.0 points (81.2 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 3 benchmarks, Nova Pro Max wins 6.
Which is cheaper, GLM-4.6 or Nova Pro Max?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $28.44 for Nova Pro Max.
Which has the larger context window?
Nova Pro Max has the larger context window at 1,000,000 tokens. GLM-4.6 has 128,000 tokens.
Are these models open weights?
GLM-4.6 is open weights; Nova Pro Max is proprietary.
Which should I choose for production?
If you need a single default, start with Nova Pro Max 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.