GPT-5.5 vs Claude Opus 4.8
Head-to-head specifications
| Metric | GPT-5.5 | Claude Opus 4.8 | Difference |
|---|---|---|---|
| Intelligence Index | 55.0 | 56.0 | -1.8% |
| Coding Index | 74.9 | 74.3 | +0.8% |
| Agentic Index | 44.9 | 47.2 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.54 | $1.38 | +11.6% |
| Output speed (tokens/s) | 67 | 53 | +26.4% |
| Access | Proprietary API | Proprietary API | — |
- Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0).
- Claude Opus 4.8 is the cheaper model to run at $1.38/1M blended tokens — about 1.1× cheaper.
Verdict: GPT-5.5 or Claude Opus 4.8?
GPT-5.5 advantages
- Output speed (+21%)
Claude Opus 4.8 advantages
- Agentic task performance (+5%)
- Affordability (+10%)
Which should you choose?
- Choose the GPT-5.5 if low latency and fast generation matter for your application.
- Choose the Claude Opus 4.8 if you build agents or multi-step tool-use workflows.
Value for money
Claude Opus 4.8 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 4.8: 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 4.8 — Anthropic multimodal model with an Intelligence Index of 56, a 1M-token context window and a blended price of $1.38/1M tokens.
GPT-5.5 vs Claude Opus 4.8: Claude Opus 4.8 scores higher on the Intelligence Index. Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0). Claude Opus 4.8 is the cheaper model to run at $1.38/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude Opus 4.8 scores 56.0 versus 55.0. For software development, the Coding Index puts GPT-5.5 ahead (74.9 vs 74.3). On agentic, multi-step tool-use tasks, Claude Opus 4.8 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 53 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Claude Opus 4.8 is the cheaper model to run ($1.38 vs $1.54 per 1M tokens). GPT-5.5 is proprietary api and Claude Opus 4.8 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 4.8?
Claude Opus 4.8 takes the overall edge, though GPT-5.5 wins in specific areas worth weighing. Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0).
What is the main difference between the GPT-5.5 and the Claude Opus 4.8?
Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0). Claude Opus 4.8 is the cheaper model to run at $1.38/1M blended tokens — about 1.1× cheaper.
Which is better value?
Claude Opus 4.8 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 4.8 if you build agents or multi-step tool-use workflows.
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.