Claude Opus 4.8 vs GPT-5.5: A Benchmark Comparison
Head-to-head benchmark comparison of Claude Opus 4.8 and GPT-5.5 across coding, terminal use, reasoning, and long context. Find out which model wins and when.
Claude Opus 4.8 and GPT-5.5 are the strongest general-purpose models available today, and neither wins everywhere. This comparison walks through every major benchmark category so you can choose the right model for your workload.
The short answer
GPT-5.5 holds an edge on long-context retrieval and terminal tasks, while Opus 4.8 leads on hard agentic coding, tool use, and computer-use evaluations. On reasoning, they are almost identical. The model you should deploy depends on which category your work actually lives in.
Agentic coding
This is where the gap is most pronounced. SWE-bench Pro is the hardest real-world software-engineering eval available, requiring a model to resolve genuine GitHub issues without handholding. Opus 4.8 scores 69.2% against GPT-5.5's 58.6% — a 10.6-point lead that is hard to dismiss. On SWE-bench Verified, however, GPT-5.5 edges ahead at 88.7% versus Opus 4.8's 88.6% — essentially a tie on the easier subset.
The takeaway: for complex, production-grade coding tasks, Opus 4.8 has a meaningful advantage. For more routine code-review-style work, both models perform equally. See the best LLM for coding in 2026 for a deeper breakdown including Mythos Preview.
Terminal and computer use
GPT-5.5 leads on Terminal-Bench 2.1 with 78.2% versus Opus 4.8's 74.6%. This eval simulates real shell sessions, so a 3.6-point gap matters for teams building agentic CLI pipelines.
Opus 4.8 recovers on OSWorld-Verified (83.4% vs GPT-5.5's 78.7%), a GUI computer-use benchmark. If your agent needs to control a desktop rather than a terminal, Opus 4.8 is the stronger choice.
Long-context retrieval
AA-LCR (Long Context Retrieval) is the clearest win for GPT-5.5: it scores 74.3% while Opus 4.8 scores 67.7% — a 6.6-point gap. Gemini 3.1 Pro is not reported on this benchmark. If your workload involves searching through very large documents, GPT-5.5's long-context architecture gives it a tangible edge here.
Reasoning and knowledge
On GPQA Diamond (graduate-level science), the two models are near-identical: Opus 4.8 at 93.6% and GPT-5.5 at 93.6% — exactly tied. Humanity's Last Exam tells a different story: Opus 4.8 reaches 49.8% without tools and 57.9% with tools, versus GPT-5.5's 41.4% / 52.2%. On the hardest reasoning frontier, Opus 4.8 has a consistent lead. For a full reasoning breakdown, see the best LLM for reasoning in 2026.
Tool use and search
MCP-Atlas, which tests structured tool-calling workflows, shows Opus 4.8 at 82.2% versus GPT-5.5's 75.3%. BrowseComp (web search) is essentially a tie: GPT-5.5 at 84.4% and Opus 4.8 at 84.3%. For agentic systems that rely on external APIs, Opus 4.8's MCP-Atlas lead is worth noting. You can explore the full results in our live benchmark comparison.
Head-to-head summary
| Benchmark | Opus 4.8 | GPT-5.5 | Winner |
|---|---|---|---|
| SWE-bench Pro | 69.2% | 58.6% | Opus 4.8 |
| SWE-bench Verified | 88.6% | 88.7% | Tie |
| Terminal-Bench 2.1 | 74.6% | 78.2% | GPT-5.5 |
| AA-LCR | 67.7% | 74.3% | GPT-5.5 |
| OSWorld-Verified | 83.4% | 78.7% | Opus 4.8 |
| MCP-Atlas | 82.2% | 75.3% | Opus 4.8 |
| GPQA Diamond | 93.6% | 93.6% | Tie |
| HLE (with tools) | 57.9% | 52.2% | Opus 4.8 |
| BrowseComp | 84.3% | 84.4% | Tie |
The verdict
- Choose Opus 4.8 for agentic coding (especially hard PRs), computer use, tool-calling pipelines, and frontier reasoning.
- Choose GPT-5.5 for terminal automation, long-document retrieval, and workloads that live in very large context windows.
- SWE-bench Verified is saturated at 88%+ — use SWE-bench Pro to see a real difference.
- Read the complete guide to LLM benchmarks to understand how these numbers are produced and what to watch out for.
