DeepSeek-R1 Base vs Nova Pro Max: which model should you choose?
We compared DeepSeek-R1 Base and Nova Pro Max on price, context window and 12 benchmarks. Nova Pro Max has the stronger average benchmark score; DeepSeek-R1 Base is cheaper on a blended 3:1 basis.
DeepSeek-R1 Base
DeepSeek-R1 Base is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.05 per million input tokens and $26.24 per million output tokens, and averages 80.1 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $26.24 / 1M
- Context
- 400,000
- Avg score
- 80.1
- 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 | DeepSeek-R1 Base | Nova Pro Max | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $14.05 | A |
| Output price / 1M | $26.24 | $71.59 | A |
| Context window | 400,000 | 1,000,000 | B |
| Max output | 65,536 | 16,384 | A |
| Average score | 80.1 | 81.2 | B |
| Arena Elo | 1442.9 | 1423.2 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-R1 Base wins 5, Nova Pro Max wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | Nova Pro Max | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 84.2 | 3.2 | Nova Pro Max |
| GPQA Diamond | 76.2 | 78.3 | 2.1 | Nova Pro Max |
| SWE-bench Verified | 71.2 | 67.4 | 3.8 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 87.5 | 4.1 | Nova Pro Max |
| MATH-500 | 84.9 | 83.8 | 1.1 | DeepSeek-R1 Base |
| AIME 2025 | 73.8 | 79.3 | 5.5 | Nova Pro Max |
| LiveCodeBench | 76.0 | 77.0 | 1.0 | Nova Pro Max |
| IFEval | 88.2 | 86.4 | 1.8 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 83.4 | 1.2 | Nova Pro Max |
| HellaSwag | 90.0 | 88.9 | 1.1 | DeepSeek-R1 Base |
| MMMU | 74.2 | 77.2 | 3.0 | Nova Pro Max |
| Arena Elo | 1442.9 | 1423.2 | 19.7 | DeepSeek-R1 Base |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and Nova Pro Max costs about $28.44. DeepSeek-R1 Base is roughly 64% 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 400,000 for DeepSeek-R1 Base. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 5, Nova Pro Max wins 7, and 0 are tied. On the index average, Nova Pro Max leads DeepSeek-R1 Base by 1.1 points (81.2 versus 80.1).
The price picture favours DeepSeek-R1 Base. 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 DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Base if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
- 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.
FAQ
Is DeepSeek-R1 Base better than Nova Pro Max?
On the index average, Nova Pro Max leads DeepSeek-R1 Base by 1.1 points (81.2 versus 80.1). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 5 benchmarks, Nova Pro Max wins 7.
Which is cheaper, DeepSeek-R1 Base or Nova Pro Max?
DeepSeek-R1 Base is cheaper on a blended 3:1 basis at about $10.35 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. DeepSeek-R1 Base has 400,000 tokens.
Are these models open weights?
DeepSeek-R1 Base 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 DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.