GPT-5.5 vs Claude Opus 4.7: benchmarks, pricing, and the right model for each production workload.

Two frontier flagships shipped seven days apart in April 2026. Anthropic released Claude Opus 4.7 on April 16. OpenAI released GPT-5.5 on April 23. Both arrive with 1M-token context windows, both lean on thinking-style reasoning, and both are explicitly positioned as the labs' best models for agentic coding — the highest-stakes commercial AI workload of the year. This guide is a head-to-head, benchmark-by-benchmark comparison: where each model wins, where each model loses, and how to route workloads between them in a production stack.

All numbers are sourced directly from each lab's release pages and official model documentation. Where OpenAI ran an internal eval against Opus 4.7 and Anthropic published a different number for the same benchmark (notably CyberGym), both figures are cited and the methodology gap is flagged. For deeper context on each individual model, our GPT-5.5 complete guide and Claude Opus 4.7 complete guide cover each release in full.

Before the benchmarks, the basics. Both models are the current flagships from their respective labs, both ship with 1M-token context windows, both run on multiple cloud platforms, and the two release dates are seven days apart — a tighter window than any previous frontier-vs-frontier release in 2026. The structural similarities make the differences easier to read: which lab won which axis, by how much, and at what price.

Two structural notes worth pulling out: Opus 4.7 ships GA on the big-three enterprise clouds plus Microsoft Foundry from day one — relevant for procurement teams with existing AWS or GCP commits. GPT-5.5 is in ChatGPT and Codex now but the API is still rolling out at the time of writing, with OpenAI citing additional safety and security work for serving partners at scale.

Agentic coding is the single most contested benchmark category in April 2026 — and the area where GPT-5.5 separates most clearly from prior generations and from Opus 4.7. On Terminal-Bench 2.0 (planning, iteration, and tool coordination across command-line workflows), GPT-5.5 scores 82.7% versus 69.4% for Opus 4.7 per OpenAI's eval. On the internal Expert-SWE benchmark — long-horizon coding tasks with a median estimated 20-hour human completion time — GPT-5.5 hits 73.1%; Opus 4.7 isn't reported on this internal eval. The MCP-Atlas tool-orchestration benchmark, however, runs the other way: 79.1% Opus 4.7 vs 75.3% GPT-5.5.

For deeper agentic-coding context, our Claude Opus 4.7 vs GPT-5.4 agentic coding analysis documented the prior matchup. The headline shift with GPT-5.5 is that OpenAI now leads Terminal-Bench by 13.3 points (vs the 5.7 GPT-5.4 lead it had over Opus 4.7), while Opus 4.7's SWE-Bench Pro and MCP-Atlas leads remain intact at the same magnitudes.

SWE-Bench Pro is the most-cited number whenever an Opus release ships, and Opus 4.7's 64.3% extends Anthropic's lead over OpenAI on this specific benchmark. The honest framing is that Anthropic itself disclosed memorization concerns for a subset of SWE-bench Verified, Pro, and Multilingual problems with Opus 4.7 — and excluded the affected items from the final scoring. OpenAI cites this caveat directly in the GPT-5.5 release page table footer.

Anthropic did not publish the absolute SWE-bench Verified percentage on the Opus 4.7 news page — instead framing improvement as "3× more production tasks than Opus 4.6" on a Rakuten benchmark. The 87.6% SWE-bench Verified and 64.3% SWE-Bench Pro numbers that circulate widely are the post-exclusion figures from Anthropic's release materials.

What this means in practice: the SWE-Bench gap between Opus 4.7 and GPT-5.5 is real (Opus 4.7 is materially better at the kind of pull-the-codebase-and-fix-the-issue task SWE-bench measures), but it isn't quite the 5.7-point clean split the headline numbers suggest. For teams making procurement decisions on this single benchmark, the honest move is to run both models against your own real PRs and measure pass rate — both Anthropic and OpenAI ship cookbook examples for exactly this. Production reports from large engineering orgs in early access (Cursor, GitHub partner teams) were positive on Opus 4.7 for this workload; OpenAI's shipped quote from NVIDIA was about feature velocity, not refactor quality.

Computer use is the second axis where GPT-5.5 and Opus 4.7 compete most directly, and the benchmark margin is much tighter than agentic coding. On OSWorld-Verified, GPT-5.5 scores 78.7% versus 78.0% for Opus 4.7 — within noise range. On Tau2-bench Telecom (run without prompt tuning), GPT-5.5 hits 98.0%. Toolathlon goes to GPT-5.5 at 55.6% (Opus 4.7 not reported). MCP-Atlas, the tool-orchestration benchmark that tests handling complex tool sets via the Model Context Protocol, goes to Opus 4.7 at 79.1% vs 75.3%.

The pattern that holds across these benchmarks: GPT-5.5 leads on standalone computer-use and browsing evals where the model operates a single interface from start to finish; Opus 4.7 leads when the workflow involves orchestrating many tools through the Model Context Protocol. For agencies building AI transformation programs, the practical implication is that the choice often tracks how MCP-heavy your agent stack is — Anthropic-native stacks lean Opus 4.7, OpenAI-native stacks lean GPT-5.5, and multi-vendor routers can split the work.

Knowledge work and research is where the benchmark picture is most mixed. GPT-5.5 leads GDPval (general-domain knowledge work, 44 occupations) at 84.9% vs 80.3%. It also leads FrontierMath Tier 4 (the hardest math) at 35.4% vs 22.9%, and ARC-AGI-2 at 85.0% vs 75.8%. Opus 4.7 leads GPQA Diamond (94.2% vs 93.6%), Humanity's Last Exam with tools (54.7% vs 52.2%), and Humanity's Last Exam without tools (46.9% vs 41.4%). For BrowseComp-style retrieval-grounded research, GPT-5.5 Pro leads at 90.1%.

For deep biomedical research, GPT-5.5 also leads BixBench at 80.5% (Pro hits 33.2% on GeneBench). GPT-5.5 Pro pushes FrontierMath Tier 1–3 to 52.4% and Tier 4 to 39.6% — the best published math numbers across the generally-available frontier in April 2026.

Both GPT-5.5 and Claude Opus 4.7 ship with 1M-token context windows in their APIs. The headline is at parity. The differentiator is what happens at the upper end of the window — specifically, how reliably each model retrieves information placed deep in a long context. On OpenAI's MRCR v2 8-needle benchmark, the gap is the largest single discrepancy in this entire comparison.

One nuance worth flagging: Anthropic's new tokenizer in Opus 4.7 uses 1.0–1.35× more tokens than Opus 4.6 on the same input depending on content type. So Opus 4.7 at 1M tokens holds slightly less raw information than Opus 4.6 did at the same count. For exact-content-volume comparisons, the practical ceiling is closer to 750K-equivalent. GPT-5.5 uses OpenAI's existing tokenizer, so a token count is comparable across the 5.x line.

Pricing is the cleanest comparison in this guide. Inputs are tied at $5 per 1M tokens. Outputs go to Opus 4.7 at $25 per 1M (vs $30 for GPT-5.5), a 17% discount. Both labs offer batch and priority tiers. The wrinkle is Anthropic's new tokenizer, which can push input token counts up 1.0–1.35× on the same content vs Opus 4.6.

Day-one cloud availability tilts to Anthropic. Opus 4.7 has been generally available since April 16 across the Claude API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry. GPT-5.5 is live in ChatGPT and Codex, but the API rollout is still in progress at time of writing — OpenAI cited additional safety and security work needed before serving the model at API scale.

For procurement teams with existing AWS or GCP commits, Opus 4.7's day-one Bedrock and Vertex availability is a real advantage — no new vendor relationship needed. For teams already on the OpenAI ecosystem, Codex availability today and API availability shortly is the equivalent. For broader Codex deployment guidance, see our Codex for almost everything release guide.

The headline of this comparison: there is no single "better model." GPT-5.5 and Opus 4.7 win different benchmark groups for different reasons, and most production stacks now have multi-model routers that send each task to whichever model is currently strongest for that task class. Here's the practical decision matrix based on the benchmark spreads above and what's actually shipping.

For broader frontier-model context that includes Gemini 3.1 Pro in the matrix, see our GPT-5.4 vs Opus 4.6 vs Gemini 3.1 Pro analysis — the routing logic still applies, with GPT-5.5 strengthening OpenAI's position on agentic and long-context axes.