Claude 4 Sonnet vs GPT-5.1
Head-to-head specifications
| Metric | Claude 4 Sonnet | GPT-5.1 | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 37.0 | -21.6% |
| Coding Index | 37.6 | 49.4 | -23.9% |
| Agentic Index | 16.6 | 21.0 | — |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $1.20 | $0.77 | +55.8% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 29.0).
- GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.6× cheaper.
- Claude 4 Sonnet offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Claude 4 Sonnet or GPT-5.1?
Claude 4 Sonnet advantages
- Context window (+49%)
GPT-5.1 advantages
- General intelligence (+22%)
- Coding ability (+24%)
- Agentic task performance (+21%)
- Affordability (+36%)
Which should you choose?
- Choose the Claude 4 Sonnet if you work with long documents or large codebases.
- Choose the GPT-5.1 if you need the strongest overall reasoning and accuracy.
Value for money
GPT-5.1 offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude 4 Sonnet vs GPT-5.1: which should you choose?
Claude 4 Sonnet — Anthropic multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $1.2/1M tokens.
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.
Claude 4 Sonnet vs GPT-5.1: GPT-5.1 scores higher on the Intelligence Index. GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 29.0). GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.6× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.1 scores 37.0 versus 29.0. For software development, the Coding Index puts GPT-5.1 ahead (49.4 vs 37.6). 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 Claude 4 Sonnet accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, GPT-5.1 is the cheaper model to run ($0.77 vs $1.20 per 1M tokens). Claude 4 Sonnet is proprietary api and GPT-5.1 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 Claude 4 Sonnet better than the GPT-5.1?
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 29.0).
What is the main difference between the Claude 4 Sonnet and the GPT-5.1?
GPT-5.1 leads overall capability (Intelligence Index 37.0 vs 29.0). GPT-5.1 is the cheaper model to run at $0.77/1M blended tokens — about 1.6× cheaper.
Which is better value?
GPT-5.1 offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Claude 4 Sonnet if you work with long documents or large codebases. Choose the GPT-5.1 if you need the strongest overall reasoning and accuracy.
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.