GPT-5.5 vs Claude Opus 4.7: which is better for coding?

Quick recommendation

• Choose Claude Opus 4.7 first for repo-level bug fixes, test-passing patches, and pull-request-style code edits.
• Choose GPT-5.5 first for terminal-heavy agents that build, test, lint, inspect logs, and decide the next command.
• Test both if the work combines patch quality, long-running reasoning, tool calls, and local developer-environment control.

Side-by-side: where each model looks stronger

Why Claude Opus 4.7 is compelling for code fixes

Claude Opus 4.7 has the clearer public edge on repository repair benchmarks. On SWE-bench Pro, GPT-5.5 was reported at 58.6%, while Claude Opus 4.7 was reported at 64.3%. Anthropic’s own coding page also highlights 64.3% for Opus 4.7 on SWE-bench Pro.

That matters because many real coding-assistant tasks are not “write me a function from scratch.” They are closer to: here is a failing test, here is a large codebase, find the right file, make the smallest safe change, and do not break anything else.

Anthropic’s own positioning points in the same direction. Its Claude API release notes say Claude Opus 4.7 launched on April 16, 2026, as Anthropic’s most capable generally available model for complex reasoning and agentic coding.

Claude Opus 4.7 also adds a beta feature called task budgets, which gives the model a rough token target for a full agentic loop, including thinking, tool calls, tool results, and final output. The model sees a running countdown and uses it to prioritize work as the budget is consumed. Anthropic has also said Opus 4.7 users now default to xhigh effort.

That makes Claude Opus 4.7 especially worth testing for:

• reproducing and fixing bugs in an existing codebase;
• reading failing tests and making a minimal patch;
• preparing a pull-request-sized diff;
• reasoning through codebase context before editing;
• long-running coding tasks where the model must manage work over multiple steps.

The caveat is important: this does not prove Claude is better at every kind of coding. DataCamp notes that SWE-bench has several variants and that both vendors highlighted the benchmark version where they performed best. Treat the benchmark as a screening signal, not a final purchasing decision.

Why GPT-5.5 is compelling for terminal agents

GPT-5.5’s strongest public coding-adjacent signal is in terminal-based agent work. VentureBeat’s Terminal-Bench 2.0 table lists GPT-5.5 at 82.7, compared with 69.4 for Claude Opus 4.7.

That is a different skill from simply generating code. Terminal-Bench 2.0 is described as simulating complex command-line workflows that require planning, iteration, and tool coordination. In other words, it is closer to the way a developer agent might actually work: run a command, inspect the output, change strategy, rerun tests, and keep narrowing the problem.

GPT-5.5 should be high on your shortlist if your coding setup depends on:

• repeated build, test, lint, or migration commands;
• reading terminal output and choosing the next action;
• combining several CLI tools to solve a problem;
• automating development-environment tasks;
• agent loops where command selection matters as much as code generation.

The caveat is the mirror image of Claude’s: a high Terminal-Bench 2.0 score does not automatically mean better merged pull requests. A model can be strong at orchestrating commands and still need evaluation on whether its final diff is small, safe, and maintainable.

Tool-use results are mixed

Broader tool-use benchmarks do not give a clean win to either model. In OpenAI’s GPT-5.5 materials, BrowseComp is listed at 84.4% for GPT-5.5 and 79.3% for Claude Opus 4.7, but MCP Atlas is listed at 75.3% for GPT-5.5 and 79.1% for Claude Opus 4.7.

So “the model uses tools” is too broad a category. A coding assistant that searches documentation, a browser-based research helper, a local terminal agent, and a patch generator are all different products. They need different tests.

Three benchmark traps to avoid

First, do not confuse overall model rankings with coding rankings. BenchLM’s overall ranking lists GPT-5.4 at 88 and Claude Opus 4.7 at 86, but that is not GPT-5.5 and it is not a coding-specific leaderboard.

Second, do not let one SWE-bench number decide everything. SWE-bench has multiple variants, and public benchmark choices can reflect vendor positioning as much as real-world developer needs.

Third, do not treat terminal performance as code quality. Terminal-Bench 2.0 is useful for judging command-line planning, iteration, and tool coordination, but you still need to inspect the resulting patch as code.

How to run a fair A/B test

For an engineering team, the safest answer is to benchmark both models inside your own workflow. Keep the setup as equal as possible:

• start from the same repository, branch, and commit;
• give both models the same issue description and reproduction steps;
• use the same test commands, timeouts, and tool permissions;
• apply the same time, token, or budget limits;
• prevent one model from seeing hints the other did not get;
• review the final diffs using the same criteria.

Then judge the outputs by practical engineering standards:

• Did the tests pass on the first attempt?
• How much human intervention was required?
• Is the diff minimal and easy to review?
• Did it introduce security, performance, or type-safety regressions?
• Would a reviewer actually merge it?
• Is the latency and cost acceptable for your expected usage?

Bottom line

For conventional software-engineering work — fixing bugs, passing tests, and creating reviewable patches — start with Claude Opus 4.7. The public SWE-bench Pro signals favor Claude Opus 4.7 over GPT-5.5 for that style of coding task.

For CLI-driven developer agents — running commands, reading logs, coordinating tools, and iterating through terminal workflows — start with GPT-5.5. The reported Terminal-Bench 2.0 numbers favor GPT-5.5 by a wide margin.

The practical rule is simple: Claude first for code repair; GPT first for terminal automation. Your final choice should be whichever model produces more mergeable code, with fewer retries, in your own repository.