GPT-5.1 vs Nova 2.0 Pro Preview
Head-to-head specifications
| Metric | GPT-5.1 | Nova 2.0 Pro Preview | Difference |
|---|---|---|---|
| Intelligence Index | 37.0 | 26.0 | +42.3% |
| Coding Index | 49.4 | 34.0 | +45.3% |
| Agentic Index | 21.0 | 7.0 | — |
| Context window | 512K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.77 | $0.87 | -11.5% |
| Output speed (tokens/s) | 106 | 123 | -13.8% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 26.0).
- GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.1× cheaper.
- GPT-5.1 offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: GPT-5.1 or Nova 2.0 Pro Preview?
GPT-5.1 advantages
- General intelligence (+30%)
- Coding ability (+31%)
- Agentic task performance (+67%)
- Context window (+49%)
- Affordability (+11%)
Nova 2.0 Pro Preview advantages
- Output speed (+14%)
Which should you choose?
- Choose the GPT-5.1 if you need the strongest overall reasoning and accuracy.
- Choose the Nova 2.0 Pro Preview if low latency and fast generation matter for your application.
- Choose the GPT-5.1 if coding and software development are your main workload.
Value for money
GPT-5.1 offers more intelligence per dollar (1.6× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.1 vs Nova 2.0 Pro Preview: which should you choose?
GPT-5.1 — OpenAI multimodal model with an Intelligence Index of 37, a 512K-token context window and a blended price of $0.77/1M tokens.
Nova 2.0 Pro Preview — Amazon multimodal model with an Intelligence Index of 26, a 262K-token context window and a blended price of $0.87/1M tokens.
GPT-5.1 vs Nova 2.0 Pro Preview: GPT-5.1 scores higher on the Intelligence Index. GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 26.0). GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.1 scores 37.0 versus 26.0. For software development, the Coding Index puts GPT-5.1 ahead (49.4 vs 34.0). On agentic, multi-step tool-use tasks, GPT-5.1 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.1 accepts up to 512K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Nova 2.0 Pro Preview generates faster (123 vs 106 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5.1 is the cheaper model to run ($0.77 vs $0.87 per 1M tokens). GPT-5.1 is proprietary api and Nova 2.0 Pro Preview is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
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-5.1 better than the Nova 2.0 Pro Preview?
GPT-5.1 is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 26.0).
What is the main difference between the GPT-5.1 and the Nova 2.0 Pro Preview?
GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 26.0). GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.1× cheaper.
Which is better value?
GPT-5.1 offers more intelligence per dollar (1.6× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.1 if you need the strongest overall reasoning and accuracy. Choose the Nova 2.0 Pro Preview if low latency and fast generation matter for your application.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.