Claude Opus 4.7 shipped April 16, 2026. GPT-5.5 followed April 23. Both companies called their model the strongest general-purpose model available. Both were right in different places.
Here is the actual comparison.
The Release Gap
One week separated the two launches. That gap mattered: teams using Opus 4.7 inside Claude Code had a week where they were running the strongest model available before GPT-5.5 closed the gap. GPT-5.5 released to API and ChatGPT simultaneously; Opus 4.7 reached Claude Code users first, then rolled out to API access.
Both are now available across their respective platforms.
Benchmarks
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| SWE-Bench Pro | 64.3% | 61.8% |
| SWE-Bench Verified | ~82% | ~80% |
| GPQA (science reasoning) | Leads | — |
| HLE (hard reasoning) | Leads | — |
| MCP Atlas (agentic) | Leads | — |
| Terminal-Bench 2.0 | — | Leads |
| BrowseComp (web research) | — | Leads |
| OSWorld (computer use) | — | Leads |
| CyberGym (security tasks) | — | Leads |
The benchmark split is clean. Opus 4.7 wins on architectural reasoning, science problems, and agentic task completion (MCP Atlas). GPT-5.5 wins on terminal-native work, web research, computer use, and security tasks.
This is not a case of one model being uniformly better. It is two models that have trained into different strengths.
Coding Specifically
For code generation and autonomous bug fixing, Opus 4.7 holds a consistent lead on SWE-Bench Pro (64.3% vs 61.8%) and SWE-Bench Verified. The advantage is most pronounced on tasks that require reasoning across a large codebase — understanding how components interact, tracing data flows across files, or refactoring without breaking distant dependencies.
GPT-5.5 is stronger on tasks that require precise tool use and file navigation: structured API calls, strict schema adherence, and workflows that chain many discrete operations. Terminal-Bench 2.0 captures this well, and GPT-5.5 leads there clearly.
If your coding work skews toward complex reasoning across a large context, Opus 4.7. If it skews toward precision tool use and CLI-native workflows, GPT-5.5.
Token Efficiency
This is the most important practical difference for teams running agents at scale.
GPT-5.5 uses 72% fewer output tokens than Claude Opus 4.7 on equivalent coding tasks. It is more concise. It reaches the same answer with less text. This is not a quality difference — it is a verbosity difference.
At API pricing:
| Claude Opus 4.7 | GPT-5.5 | |
|---|---|---|
| Input | $5/M tokens | $5/M tokens |
| Output | $25/M tokens | $30/M tokens |
Opus 4.7 is cheaper per output token ($25 vs $30). But GPT-5.5 produces 72% fewer of them. On an equivalent task, GPT-5.5 costs less to run despite the higher per-token output rate. The crossover only flips if you are running tasks where Opus 4.7's greater verbosity is load-bearing — e.g., detailed explanations, long-form documentation generation.
For most agentic coding work, GPT-5.5 is cheaper to operate at scale. For tasks where you need the extra reasoning depth, Opus 4.7's output token cost is still lower enough to matter.
Context Window
Both models support extended context, but check your platform's current limits — context window availability varies by API tier and has been rolling out gradually since launch. Both comfortably handle large codebases in practice.
Where to Use Each
Claude Opus 4.7 is the better choice when:
- You are working across a large, interconnected codebase where reasoning about distant dependencies matters
- Agentic task completion with MCP tools is the core workflow
- Hard science or engineering problems with multi-step reasoning are in scope
- You are using Claude Code — Opus 4.7 is the default model and is tightly integrated
- Output token cost matters and verbosity is acceptable
GPT-5.5 is the better choice when:
- Precise tool use and strict schema adherence are the primary requirement
- Terminal-native workflows, DevOps, CLI tooling — Terminal-Bench 2.0 captures this accurately
- Web research and computer use tasks (BrowseComp, OSWorld)
- Token efficiency at scale — 72% fewer output tokens is meaningful when running hundreds of agentic tasks per day
- Security research (CyberGym lead suggests stronger performance on structured security tasks)
The Practical Answer
Most teams building on one of these models are doing so for platform reasons — Claude Code defaults to Opus 4.7, Codex defaults to GPT-5.5 — rather than selecting purely on benchmark criteria. Both are genuinely strong enough that the integration story matters more than the benchmark margin.
If you are selecting a model for a new API-based application: benchmark on your actual task distribution. The SWE-Bench gap is real but narrow. The token efficiency gap is also real and may dominate your cost calculation.
Neither is a runaway winner. The answer depends on what you are building.
Benchmark data as of April–May 2026. API pricing verified May 2026.
Sources:
- GPT-5.5 vs Claude Opus 4.7: Real-World Coding Performance - MindStudio
- Claude Opus 4.7 vs GPT-5.5: Which Frontier Model Is Best? - DataCamp
- GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks - LLM Stats
- Claude Opus 4.7 vs GPT-5.5: Full Comparison - fwdslash
- GPT-5.5 vs Claude Opus 4.7: Benchmarks & Pricing - Lushbinary
- Claude Opus 4.7 vs GPT-5.5: AI Benchmark Comparison - BenchLM.ai