Nova Pro vs DeepSeek R1
Head-to-head specifications
| Metric | Nova Pro | DeepSeek R1 | Difference |
|---|---|---|---|
| MMLU (general capability) | 85.9% | 90.8% | -4.9% |
| Context window | 300K tokens | 128K tokens | — |
| Price (input / output per 1M) | $0.8 / $3.2 | Open weights | — |
| Access | Proprietary API | Open weights | — |
- DeepSeek R1 leads general capability (MMLU 90.8% vs 85.9%).
- Nova Pro offers the larger context window, useful for long documents and codebases.
Verdict: Nova Pro or DeepSeek R1?
Nova Pro advantages
- Context window (+57%)
DeepSeek R1 advantages
- General capability (+5%)
Which should you choose?
- Choose the Nova Pro if you work with long documents or large codebases.
- Choose the DeepSeek R1 if you need the strongest reasoning and accuracy.
Value for money
DeepSeek R1 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Nova Pro vs DeepSeek R1: which should you choose?
Nova Pro — Amazon large language model (2024) with a 300K-token context window and an MMLU score of 85.9%.
DeepSeek R1 — DeepSeek large language model (2025) with a 128K-token context window and an MMLU score of 90.8%, released with open weights.
Nova Pro vs DeepSeek R1: DeepSeek R1 scores higher on the MMLU benchmark. DeepSeek R1 leads general capability (MMLU 90.8% vs 85.9%). Nova Pro offers the larger context window, useful for long documents and codebases.
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the DeepSeek R1 scores 90.8% versus 85.9%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The Nova Pro handles up to 300K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
Nova Pro is proprietary api and DeepSeek R1 is open weights. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Nova Pro better than the DeepSeek R1?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. DeepSeek R1 leads general capability (MMLU 90.8% vs 85.9%).
What is the main difference between the Nova Pro and the DeepSeek R1?
DeepSeek R1 leads general capability (MMLU 90.8% vs 85.9%). Nova Pro offers the larger context window, useful for long documents and codebases.
Which is better value?
DeepSeek R1 is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Which should I choose?
Choose the Nova Pro if you work with long documents or large codebases. Choose the DeepSeek R1 if you need the strongest reasoning and accuracy.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.