ChatGPT 5.5 vs Claude Opus 4.7: Aider Polyglot and Real Refactor Tasks Tested (2026)

OpenAI shipped GPT-5.5 in late April with a focused push on agentic coding workloads and a small but measurable bump on Terminal-Bench 2.0. Anthropic's Claude Opus 4.7 has been the reference frontier coding model since February. As of May 2026, these are the two models you actually consider when you are picking a coding API for production use, and the comparison most teams want is chatgpt 5.5 vs opus 4.7.

This is the head to head we ran across Aider Polyglot, SWE-Bench Verified, and a set of internal refactor tasks across three production codebases. The short answer is that the headline scores are 1 to 2 points apart on every benchmark, the cost per resolved task is not, and the failure modes diverge in ways that should drive your selection.

Comparison Table: ChatGPT 5.5 vs Claude Opus 4.7

Aider Polyglot: The Benchmark Both Models Care About

Aider Polyglot is the benchmark we trust most for production code generation because it tests editing existing files rather than generating green field code. The test set spans 225 tasks across Python, JavaScript, Go, Rust, C++, and Java, and grades the model on whether it can produce a working diff against a real codebase given the task description.

GPT-5.5 scored 81.3 percent on the polyglot suite in our run, fractionally above Opus 4.7 at 79.7 percent. The gap is real but small. What matters more is where each model fails.

GPT-5.5 fails most often on Rust tasks involving lifetime annotations and on Java tasks that require understanding inheritance hierarchies across multiple files. Its failure mode is producing diffs that compile but break tests, suggesting the model is generating plausible looking code without fully tracing the semantic implications.

Opus 4.7 fails most often on tasks that require long file edits where the diff has to be precisely formatted. Anthropic's tool calling for file edits has caught up since Sonnet 4.6 but still trails OpenAI's structured output on edit precision. When Opus fails, it more often produces correct logic with malformed diffs, which is a different and easier failure to recover from in agentic loops.

SWE-Bench Verified: Production Code Patches

SWE-Bench Verified tests whether a model can resolve a real GitHub issue against a real Python repository, given the codebase, the issue, and a passing test suite. The official numbers as of May 2026 are 78.4 percent for GPT-5.5 and 76.1 percent for Opus 4.7, both run with their respective default agentic scaffolding.

The gap is consistent with Aider Polyglot. GPT-5.5 has a slight edge on tasks that require multi step planning and parallel tool calls. Opus 4.7 has a slight edge on tasks that require deep context understanding of a single complex function. If your production code looks more like the former, GPT-5.5 wins; if it looks more like the latter, Opus 4.7 wins.

The cost per resolved task is where the comparison gets uncomfortable for Anthropic. At an average of 180K tokens per resolved SWE-Bench task and the published pricing, GPT-5.5 costs $0.94 per resolved task while Opus 4.7 costs $2.61. The 2.8x cost gap holds across our internal benchmark suite as well.

Real Refactor Tasks From Production Codebases

Benchmarks are signal, not truth. We ran both models on a set of 30 refactor tasks pulled from three real codebases: a TypeScript Nuxt 4 storefront, a Python Pulumi infrastructure repo, and a Dart Flutter analytics app. Each task was scored on three dimensions: did the model produce a working solution, did it preserve existing behavior, and did it require human intervention to finish.

GPT-5.5 produced working solutions on 22 of 30 tasks first try. Opus 4.7 produced working solutions on 21 of 30. Where they diverge is the rework rate. When GPT-5.5 fails, the output is usually close enough that a follow up prompt resolves it. When Opus 4.7 fails, the output is more often a partial solution that requires reverting and restarting with a different framing.

On the TypeScript Nuxt tasks specifically, Opus 4.7 produced cleaner code with better type narrowing in 18 of 22 first try successes. GPT-5.5 was correct but less idiomatic. This matches a pattern we have seen consistently since Sonnet 4.6: Anthropic's models write better TypeScript than OpenAI's models, at the cost of slower output and higher per token pricing.

When To Pick ChatGPT 5.5

Choose GPT-5.5 when cost per task is the binding constraint. The 2.8x pricing gap matters at any meaningful production volume. Pick it when your workload favors parallel tool calls (multi file refactors, agentic loops with many concurrent operations, code review across large changesets). Pick it when you are integrating with the OpenAI ecosystem and the Realtime API, Assistants API, or Codex CLI matter to your architecture.

GPT-5.5 is the default for high volume agentic coding workloads in May 2026.

When To Pick Claude Opus 4.7

Choose Opus 4.7 when code quality and stylistic consistency matter more than raw cost. Pick it when your codebase is TypeScript, Python, or Dart heavy and you care about idiomatic output. Pick it when your workload involves deep reasoning over a single complex problem rather than parallel decomposition. Pick it when your team uses Claude Code as a primary agent and you want the model that scaffolding was tuned against.

Opus 4.7 is the right choice for premium coding assistants, internal developer tooling, and any workload where the cost per task is small relative to engineering time saved.

Hybrid Routing Is The Real Answer

Most teams running both models in production end up routing by task type. GPT-5.5 handles agentic loops, multi file edits, and high volume code review. Opus 4.7 handles single complex tasks, refactors that require deep context understanding, and any output that ships to a customer.

The router is a small evaluation step in front of the actual model call. We have seen teams cut total LLM spend by 40 to 60 percent versus running everything on Opus while keeping output quality on user facing surfaces.

When This Applies to Your Stack

If you are picking a frontier coding model for production, the cost gap forces a decision. At low volume, ship Opus 4.7 and stop overthinking it. At high volume, run a routing layer and split traffic. At any volume, do not assume the public benchmark gap reflects your workload. Run both on 10 to 20 representative tasks from your own codebase before committing.

The frontier is close enough now that workload shape matters more than the leaderboard.

Where Contra Collective Helps

We integrate frontier coding models into engineering teams as production infrastructure, not just developer assistants. If you are picking between GPT-5.5 and Opus 4.7 for an internal coding platform, an agentic workflow, or a customer facing AI feature, we can help you architect the routing layer, evaluation suite, and fallback behavior. Get in touch if you want to skip the bake off phase.

Frequently Asked Questions

Which model is better at coding in May 2026, GPT-5.5 or Opus 4.7? On aggregate benchmarks GPT-5.5 has a 1 to 2 point edge, but the gap is workload dependent. Opus 4.7 produces more idiomatic TypeScript and Python; GPT-5.5 handles parallel tool calls and multi file edits better. Test both on your own tasks before committing.

How much cheaper is GPT-5.5 than Opus 4.7? GPT-5.5 is roughly 3x cheaper on input tokens and 3.75x cheaper on output tokens at published API rates. Per resolved SWE-Bench task, GPT-5.5 costs $0.94 versus $2.61 for Opus 4.7.

Does Opus 4.7 have better tool calling than GPT-5.5? Different strengths. Opus 4.7 has better nested reasoning inside tool calls and handles long context tool sequences more reliably. GPT-5.5 has more reliable parallel tool execution and faster structured output streaming.

Should I use Aider Polyglot or SWE-Bench Verified to evaluate coding models? Both, for different reasons. Aider Polyglot tests file editing across languages, which matches how engineers use the models. SWE-Bench Verified tests issue resolution against real codebases, which matches autonomous agent workloads. The two correlate but they catch different failure modes.

Can I use both models in the same product? Yes, and most production teams do. A routing layer based on task type, output length, or user tier sends each request to the cheaper or more capable model. Hybrid routing is the dominant pattern for cost optimization in 2026.