GPT-5.5 vs Claude Opus 4.7
Head-to-head specifications
| Metric | GPT-5.5 | Claude Opus 4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 55.0 | 54.0 | +1.9% |
| Coding Index | 74.9 | 73.6 | +1.8% |
| Agentic Index | 44.9 | 44.4 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.54 | $1.43 | +7.7% |
| Output speed (tokens/s) | 67 | 47 | +42.6% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 54.0).
- Claude Opus 4.7 is the cheaper model to run at $1.43/1M blended tokens — about 1.1× cheaper.
Verdict: GPT-5.5 or Claude Opus 4.7?
GPT-5.5 advantages
- Output speed (+30%)
Claude Opus 4.7 advantages
- Affordability (+7%)
Which should you choose?
- Choose the GPT-5.5 if low latency and fast generation matter for your application.
- Choose the Claude Opus 4.7 if you want the lowest cost per token at scale.
Value for money
Claude Opus 4.7 offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.5 vs Claude Opus 4.7: which should you choose?
GPT-5.5 — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.54/1M tokens.
Claude Opus 4.7 — Anthropic multimodal model with an Intelligence Index of 54, a 1M-token context window and a blended price of $1.43/1M tokens.
GPT-5.5 vs Claude Opus 4.7: GPT-5.5 scores higher on the Intelligence Index. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 54.0). Claude Opus 4.7 is the cheaper model to run at $1.43/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.5 scores 55.0 versus 54.0. For software development, the Coding Index puts GPT-5.5 ahead (74.9 vs 73.6). On agentic, multi-step tool-use tasks, GPT-5.5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.5 generates faster (67 vs 47 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Claude Opus 4.7 is the cheaper model to run ($1.43 vs $1.54 per 1M tokens). GPT-5.5 is proprietary api and Claude Opus 4.7 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-5.5 better than the Claude Opus 4.7?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 54.0).
What is the main difference between the GPT-5.5 and the Claude Opus 4.7?
GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 54.0). Claude Opus 4.7 is the cheaper model to run at $1.43/1M blended tokens — about 1.1× cheaper.
Which is better value?
Claude Opus 4.7 offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.5 if low latency and fast generation matter for your application. Choose the Claude Opus 4.7 if you want the lowest cost per token at scale.
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.