GPT-4o mini vs Nova Pro
Head-to-head specifications
| Metric | GPT-4o mini | Nova Pro | Difference |
|---|---|---|---|
| MMLU (general capability) | 82.0% | 85.9% | -3.9% |
| Context window | 128K tokens | 300K tokens | — |
| Price (input / output per 1M) | $0.15 / $0.6 | $0.8 / $3.2 | — |
| Access | Proprietary API | Proprietary API | — |
- Nova Pro leads general capability (MMLU 85.9% vs 82.0%).
- Nova Pro offers the larger context window, useful for long documents and codebases.
Verdict: GPT-4o mini or Nova Pro?
GPT-4o mini advantages
- Input cost (+81%)
- Output cost (+81%)
Nova Pro advantages
- General capability (+5%)
- Context window (+57%)
Which should you choose?
- Choose the GPT-4o mini if you process large volumes of input and want the lowest cost.
- Choose the Nova Pro if you need the strongest reasoning and accuracy.
- Choose the GPT-4o mini if you generate a lot of output and want the lowest cost.
Value for money
GPT-4o mini offers more capability per dollar — a better value pick for high-volume use, delivering 5.09× the MMLU-per-cost of the alternative.
GPT-4o mini vs Nova Pro: which should you choose?
GPT-4o mini — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 82.0%.
Nova Pro — Amazon large language model (2024) with a 300K-token context window and an MMLU score of 85.9%.
GPT-4o mini vs Nova Pro: Nova Pro scores higher on the MMLU benchmark. Nova Pro leads general capability (MMLU 85.9% vs 82.0%). 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 Nova Pro scores 85.9% versus 82.0%. 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
GPT-4o mini is proprietary api and Nova 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 GPT-4o mini better than the Nova Pro?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Nova Pro leads general capability (MMLU 85.9% vs 82.0%).
What is the main difference between the GPT-4o mini and the Nova Pro?
Nova Pro leads general capability (MMLU 85.9% vs 82.0%). Nova Pro offers the larger context window, useful for long documents and codebases.
Which is better value?
GPT-4o mini offers more capability per dollar — a better value pick for high-volume use, delivering 5.09× the MMLU-per-cost of the alternative.
Which should I choose?
Choose the GPT-4o mini if you process large volumes of input and want the lowest cost. Choose the Nova Pro 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.