Claude Sonnet 5 and GPT-5.5 sit in the same competitive space: capable, agentic, mid-to-upper-tier models that most teams will actually run day to day. This comparison looks at coding strength, agentic behavior, context, and price so you can pick the right default.
At a glance
| Claude Sonnet 5 | GPT-5.5 | |
|---|---|---|
| Vendor | Anthropic | OpenAI |
| Context window | 1M tokens | large |
| SWE-bench Pro | 63.2% | around 58.6% |
| OSWorld (computer use) | 81.2% | competitive |
| Effort levels | low to x-high | reasoning controls |
| Input price | $2 intro, then $3 | varies |
| Output price | $10 intro, then $15 | varies |
Coding performance
On SWE-bench Pro, the benchmark closest to real multi-file engineering, Sonnet 5 reaches 63.2 percent versus around 58.6 percent for GPT-5.5. That is a meaningful edge for Claude on agentic coding. Sonnet 5 also lands close to Opus 4.8, which leads the field at 69.2 percent.
GPT-5.5 remains strong on broad knowledge and has a deep ecosystem, and benchmark gaps within a few points often come down to agent scaffolding rather than the model itself. But for pure coding throughput on hard tasks, Sonnet 5 has the edge today.
Agentic behavior
Sonnet 5 was built specifically to act: plan, drive browsers and terminals, check its own output, and run autonomously. Early partners describe it finishing tasks older models would abandon. If your use case is autonomous, multi-step agent work, Sonnet 5’s design focus shows.
Context window
Sonnet 5 offers a full one million token context window, large enough to load an entire codebase in one prompt. That is a practical advantage for whole-repository reasoning and large document analysis.
Pricing
Sonnet 5 launches at $2 input and $10 output per million tokens through August 31, 2026, then $3 and $15. Note the new tokenizer can raise effective token counts by up to 1.35 times, so compare on real workloads, not just sticker rates. See Sonnet 5 pricing explained. GPT-5.5 pricing varies by tier; check current OpenAI rates for your usage.
Which should you choose?
- Choose Sonnet 5 for agentic coding, whole-codebase reasoning, and computer-use workflows, especially if you want near-flagship quality at a low price.
- Choose GPT-5.5 if you are deep in the OpenAI ecosystem, need its broad general knowledge, or rely on tooling built around it.
Many teams run both and route by task. For the Claude side of that strategy, see Sonnet 5 vs Opus 4.8 and the effort levels guide.
Benchmarks in context
The headline coding gap, 63.2 percent for Sonnet 5 versus around 58.6 percent for GPT-5.5 on SWE-bench Pro, is meaningful but should be read carefully. SWE-bench Pro measures resolving real issues across multi-file repositories, which rewards planning and tool use. Within a few points, the agent scaffolding around a model (how it retrieves files, runs tests, and retries) often matters as much as the base model. So treat the gap as a real but not decisive edge for Sonnet 5 on agentic coding.
GPT-5.5 remains very strong on broad general knowledge and benefits from one of the deepest tool and integration ecosystems in the industry. For teams whose workloads lean on world knowledge, reasoning breadth, or existing OpenAI tooling, that ecosystem advantage can outweigh a few benchmark points.
Real-world use cases
Sonnet 5 is the stronger pick for:
- Agentic coding and whole-codebase reasoning with its 1M context window.
- Computer-use workflows where its 81.2 percent OSWorld score helps.
- Teams that want near-flagship quality at a low, predictable price.
GPT-5.5 is the stronger pick for:
- Workloads deeply integrated with OpenAI tooling and the wider ecosystem.
- Tasks that lean on broad general knowledge and reasoning breadth.
- Teams already standardized on OpenAI for non-coding work.
Running a two-model strategy
Many teams do not choose one. A practical setup routes coding and agentic tasks to Sonnet 5, escalates the hardest coding to Opus 4.8, and keeps GPT-5.5 for general knowledge work and anything tied to the OpenAI ecosystem. A router makes this easy; see the Sonnet 5 OpenRouter setup. When you compare costs, remember Sonnet 5’s new tokenizer can raise effective token counts by up to 1.35 times; see pricing explained.
Frequently asked questions
Is Sonnet 5 better than GPT-5.5 at coding? On SWE-bench Pro, yes: 63.2 percent versus around 58.6 percent. Real-world gaps depend on your agent setup.
Which has the bigger context window? Sonnet 5 offers a 1M token context window.
Is Sonnet 5 cheaper than GPT-5.5? Often, especially during the introductory window, but compare on your real token usage given Sonnet 5’s new tokenizer.
Can I use both? Yes. Many teams route coding to Sonnet 5 and other tasks to GPT-5.5.
Does GPT-5.5 have a bigger ecosystem than Sonnet 5? Yes. OpenAI’s tooling and integration ecosystem is one of the deepest in the industry, which can outweigh a few benchmark points for teams already built around it.
Which is better for non-coding work? GPT-5.5 is very strong on broad general knowledge and reasoning breadth. For coding and agentic tasks specifically, Sonnet 5 has the edge.
Should a new project pick Sonnet 5 or GPT-5.5? If the project is coding-first or agent-heavy, start with Sonnet 5 for its SWE-bench Pro lead and 1M context. If it leans on general knowledge or existing OpenAI tooling, GPT-5.5 is the more natural base. Many teams use both and route by task.
The bottom line
For coding-first, agentic workloads, Sonnet 5 is the stronger and cheaper pick today, with a clear SWE-bench Pro lead and a 1M context window. GPT-5.5 stays compelling for ecosystem and breadth. Start with the Sonnet 5 complete guide to set it up.