Nova Pro vs Gemini 1.5 Pro
Head-to-head specifications
| Metric | Nova Pro | Gemini 1.5 Pro | Difference |
|---|---|---|---|
| MMLU (general capability) | 85.9% | 85.9% | +0.0% |
| Context window | 300K tokens | 2M tokens | — |
| Price (input / output per 1M) | $0.8 / $3.2 | $1.25 / $5 | — |
| Access | Proprietary API | Proprietary API | — |
- Nova Pro leads general capability (MMLU 85.9% vs 85.9%).
- Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Verdict: Nova Pro or Gemini 1.5 Pro?
Nova Pro advantages
- Input cost (+36%)
- Output cost (+36%)
Gemini 1.5 Pro advantages
- Context window (+85%)
Which should you choose?
- Choose the Nova Pro if you process large volumes of input and want the lowest cost.
- Choose the Gemini 1.5 Pro if you work with long documents or large codebases.
- Choose the Nova Pro if you generate a lot of output and want the lowest cost.
Value for money
Nova Pro offers more capability per dollar — a better value pick for high-volume use, delivering 1.56× the MMLU-per-cost of the alternative.
Nova Pro vs Gemini 1.5 Pro: which should you choose?
Nova Pro — Amazon large language model (2024) with a 300K-token context window and an MMLU score of 85.9%.
Gemini 1.5 Pro — Google large language model (2024) with a 2M-token context window and an MMLU score of 85.9%.
Nova Pro vs Gemini 1.5 Pro: Nova Pro scores higher on the MMLU benchmark. Nova Pro leads general capability (MMLU 85.9% vs 85.9%). Gemini 1.5 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 Nova Pro scores 85.9% 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 Gemini 1.5 Pro handles up to 2 million 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 Gemini 1.5 Pro is proprietary api. 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 Gemini 1.5 Pro?
Nova Pro takes the overall edge, though Gemini 1.5 Pro wins in specific areas worth weighing. Nova Pro leads general capability (MMLU 85.9% vs 85.9%).
What is the main difference between the Nova Pro and the Gemini 1.5 Pro?
Nova Pro leads general capability (MMLU 85.9% vs 85.9%). Gemini 1.5 Pro offers the larger context window, useful for long documents and codebases.
Which is better value?
Nova Pro offers more capability per dollar — a better value pick for high-volume use, delivering 1.56× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the Nova Pro if you process large volumes of input and want the lowest cost. Choose the Gemini 1.5 Pro if you work with long documents or large codebases.
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.