GPT-5.5 vs Claude Opus 4.7: Benchmarks & Pricing

Release Snapshot: April 16 vs April 23, 2026

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 Head-to-Head

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 and the Memorization Caveat

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.

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.

One related point on Terminal-Bench 2.0: Anthropic's own news page describes Opus 4.7 as having "passed tasks prior Claude models couldn't" but does not publish the absolute 69.4% figure cited in OpenAI's comparison table. That 69.4% came from OpenAI's evaluation of Opus 4.7 using its own eval harness — a different setup from how Anthropic would run it. Both numbers are legitimate; treat the 13.3-point Terminal-Bench gap as directional rather than absolute.

Computer Use and Tool Orchestration

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, Research, and Math

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%.

Two patterns worth pulling out. First, on the academic-style evals (GPQA Diamond, Humanity's Last Exam without tools), Opus 4.7 retains a small but consistent lead — historically a Claude-family strength. Second, on the harder reasoning evals that test problem-solving at the frontier (FrontierMath Tier 4, ARC-AGI-2), GPT-5.5 has a meaningful lead, and GPT-5.5 Pro extends that lead further. For deep biomedical research, GPT-5.5 also leads BixBench at 80.5% (Pro hits 33.2% on GeneBench).

The CyberGym number deserves an honest note. Anthropic published 73.8% on CyberGym for Opus 4.7 with an updated harness designed to "better elicit cyber capability." OpenAI's eval table reports Opus 4.7 at 73.1%. The 0.7-point gap is methodology, not substance — both numbers are legitimate. GPT-5.5 at 81.8% outscores either reading by a meaningful margin.

Long Context: Both Ship 1M, Different Retrieval

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 1.0–1.35x 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, Tokenizer, and Real Cost per Task

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; OpenAI publishes Batch and Flex at half rate with Priority at 2.5x. Anthropic's prompt-cache and batch discounts are documented on the platform.claude.com pricing page. The wrinkle is Anthropic's new tokenizer, which can push input token counts up 1.0–1.35x on the same content vs Opus 4.6.

Availability and Developer Surface

Day-one cloud availability tilts to Anthropic. Opus 4.7 has been generally available since April 16, 2026 across the Claude API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry. GPT-5.5 is live in ChatGPT (Plus, Pro, Business, Enterprise) and Codex (all paid plans, with optional Fast mode at 1.5x speed for 2.5x cost), but the API rollout on the Responses and Chat Completions endpoints is still in progress at the time of writing. OpenAI cited additional safety and security work needed before serving the model at API scale, especially for partners integrating it into agent platforms.

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.

Which to Pick: Recommendations by Use Case

For most production stacks in April 2026, the answer isn't single-vendor. It's a routing layer that picks the right model for each 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 and Opus 4.7 extending Anthropic's lead on SWE-Bench Pro and MCP-Atlas.

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