GPT-5.5 vs Claude Opus 4.7: How to Pick the Right Model

Claude Opus 4.7 launched April 16. GPT-5.5 followed seven days later on April 23. Both are their respective companies' current flagship models; both have the benchmark scores to hold that claim. Most comparison pieces frame the question as a horse race. That framing misses what's actually useful.

These models aren't competing on the same track. They're optimized for different workloads, and once you look at which benchmarks each model leads rather than overall rankings, the picture gets clearer.

Where the Benchmark Data Points

Opus 4.7 leads on code-specific benchmarks. SWE-bench Pro: 64.3% for Opus vs 58.6% for GPT-5.5. SWE-bench Verified: 87.6% vs approximately 85%. CursorBench, which tests AI coding performance in real IDE environments: 70% for Opus vs approximately 65% for GPT-5.5. GPQA Diamond, a PhD-level science reasoning test: 94.2% for Opus vs approximately 93% for GPT-5.5. The coding margins aren't overwhelming, but they're consistent.

GPT-5.5 leads on agentic and computer-use benchmarks. Terminal-Bench 2.0, which measures CLI task completion in a sandboxed terminal: 82.7% for GPT-5.5 vs approximately 72% for Opus. OSWorld-Verified, which tests computer use tasks including GUI navigation and multi-step workflows: 78.7% vs approximately 65%. GDPval, a knowledge work automation benchmark: 84.9% vs approximately 78%. These gaps are larger and harder to dismiss.

The pattern holds up: Opus wins when the task involves writing or reviewing code in structured IDE environments. GPT-5.5 wins when the task involves operating a computer, running terminal commands, or orchestrating sequences of tools to complete a goal autonomously.

Pricing

Both models are $5.00 per million input tokens with $1.25 per million for cached input. The divergence is on output: GPT-5.5 at $30.00 per million; Opus 4.7 at $25.00 (17% less).

There's a complication worth knowing about. Third-party testing suggests GPT-5.5 generates significantly fewer output tokens than Opus 4.7 on equivalent tasks; one analysis found GPT-5.5 using 72% fewer output tokens on comparable coding work. If that holds for your workload, the real-world cost difference could favor GPT-5.5 despite its higher rate. Whether it holds depends heavily on task type.

For production API usage where cost matters, run both models on a representative sample of your actual requests before making assumptions from rate cards alone.

Architecture Differences That Matter

GPT-5.5 is the first fully retrained base model from OpenAI since GPT-4.5. This isn't a fine-tune. The architecture was rebuilt from scratch for agentic multi-tool orchestration and is natively omnimodal: text, images, audio, and video are processed by a single model rather than routed through separate components.

Opus 4.7 brings a 12-point improvement on CursorBench over its predecessor, supports image inputs at up to 2,576 pixels (3.75 megapixels), and maintains Anthropic's 1-million-token context window. The context length matters in specific scenarios: passing in a large codebase, a document corpus, or a long conversation history that the model needs to reason over coherently.

When to Use Which

There's a reasonably clean split.

GPT-5.5 makes sense for agentic workflows: browser automation, computer control, terminal operations, or coordinating sequences of tool calls to accomplish a multi-step goal. The benchmark advantages in Terminal-Bench 2.0 and OSWorld are meaningful, and the native omnimodal design is a real structural advantage when tasks involve multiple modalities or tools in sequence.

Opus 4.7 makes sense when the work is primarily code-focused: complex refactoring, debugging across large codebases, or IDE-integrated development where the SWE-bench gap is relevant. The 1-million-token context is useful when the codebase is large enough that truncation would otherwise force you to chunk inputs manually.

For everything else (writing, summarization, research, analysis, general question answering), both models operate at the frontier and the practical difference is unlikely to matter. Existing integrations, latency, and which API you already have access to are better selection criteria than benchmark gaps that don't show up in day-to-day work.

One Thing Worth Flagging

Both companies are running on short release cycles. Anthropic shipped Opus 4.7 on April 16; the next release is likely within weeks. OpenAI's GPT-5.5 launched April 23 with a GPT-6 reportedly in progress. The benchmark leads either model holds right now come with an expiration date.

For developers building applications on these models, deprecation timelines and versioning stability are worth evaluating alongside current performance. Both providers support API version pinning, but their historical track records on backward compatibility differ. It's worth checking before committing to a model for a production system.

For deeper context on GPT-5.5's architecture changes and what "first fully retrained base model" actually means, OpenAI's launch post has the technical detail. For benchmark methodology and raw score tables, LLM Stats has a thorough comparison that breaks down the test conditions behind each number.

Our May 2026 chatbot pricing roundup covers the subscription tier picture if the API pricing context above isn't quite what you were looking for. And for context on what GPT-5.5 changed from GPT-5.4, our launch coverage goes through the key differences.

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