GPT-5.5 vs Claude Opus 4.7: Which Frontier Model Should Your Team Build On (May 2026)

Two flagship frontier models shipped within seven days of each other: Claude Opus 4.7 on April 16, 2026 and GPT-5.5 on April 23, 2026. If you're a CTO or staff engineer writing the cheque for your team's model spend over the next two quarters, the decision in front of you is concrete and consequential: which of these two does your product, your tooling, and your agentic infrastructure get built on?

This is a buyer's comparison, not a marketing reel. We pull benchmark numbers from primary sources only, flag where evidence is thin, and end with an opinionated decision matrix by use case. We've shipped against both. The short version: they are remarkably close on raw intelligence, GPT-5.5 wins on speed and breadth, Opus 4.7 wins on agentic coding and long-context discipline — and the right call depends more on your workload shape than on any single benchmark.

Want the full picture? Read our continuously-updated Claude Opus 4.7 Complete Guide (2026) — benchmarks, pricing, agentic capabilities, and team-deployment patterns.

Building on GPT-5.5? Bookmark our GPT-5.5 Complete Guide (2026) — model variants, API patterns, costs, and migration notes from GPT-5.

The TL;DR matrix

Numbers below are from primary vendor pages or Artificial Analysis. Where a number isn't published, we use an em-dash rather than a guess.

Honest caveat: neither vendor publishes a complete set of head-to-head numbers in a single document. Anthropic's "What's new" page is a behavior-changes doc, not a benchmark sheet. OpenAI's launch coverage emphasizes qualitative claims and a couple of headline benchmarks. The Artificial Analysis comparison is currently the best apples-to-apples third-party scoreboard.

What's new in GPT-5.5

OpenAI shipped GPT-5.5 in two flavors: a standard "GPT-5.5" tier for Plus and Business, and "GPT-5.5 Pro" for Pro and Enterprise. TechCrunch's coverage quotes Greg Brockman calling it "a real step forward towards the kind of computing that we expect in the future," and OpenAI describes the model as "faster, sharper thinker for fewer tokens compared to something like 5.4."

The internal codename, per public reporting, was "Spud." OpenAI withheld API access until April 24 — a day after the consumer launch — citing the need for "different safeguards" before exposing the model to programmatic use.

Headline gains called out at launch:

• Terminal-Bench 2.0: 82.7% — a meaningful jump over GPT-5 on agentic command-line tasks.
• FrontierMath: 51.7% on tiers 1–3, 35.4% on tier 4, the hardest currently-public math benchmark.
• Scientific reasoning & drug discovery are emphasized as differentiators in OpenAI's positioning.
• Multimodal "superapp" framing — OpenAI is positioning ChatGPT itself, not just the model, as the vehicle.

What's not in the launch post: a list-price-per-token table, a SWE-bench Verified number, the context window size, or the knowledge cutoff. Engineering leaders evaluating GPT-5.5 should expect to fish those out of the API docs and changelog as they materialize.

What's new in Claude Opus 4.7

Anthropic's release is more API-engineer-friendly. The "What's new in Claude Opus 4.7" doc is exactly what you'd want from a frontier-model launch: behavior changes, breaking changes, and a migration checklist.

The headline capabilities:

• 1M-token context window at standard pricing — Anthropic explicitly notes "no long-context premium," which is a real cost story for retrieval-heavy and long-codebase work.
• 128k max output tokens — high enough to write a non-trivial codebase or report in a single response.
• Adaptive thinking + new xhigh effort level, which Anthropic recommends as the default for coding and agentic use cases.
• Task budgets (beta) — you give the model an advisory token budget across a full agentic loop and it self-moderates against a running countdown. Distinct from max_tokens, which is a hard per-request cap.
• High-resolution vision — up to 2576px / 3.75MP with 1:1 coordinate mapping. This is a genuine win for computer-use agents and screenshot-heavy workflows.
• Memory tooling — better at writing and reading file-system scratchpads across turns.

The breaking changes matter if you're already on Opus 4.6: thinking.budget_tokens is gone (use adaptive thinking), temperature/top_p/top_k now 400 if you set them, thinking content is omitted from responses by default, and the new tokenizer can use up to ~35% more tokens on the same input. Update your max_tokens headroom accordingly.

Coding head-to-head

For most engineering teams, this is the only benchmark that actually matters. The honest picture:

GPT-5.5 wins on Terminal-Bench 2.0 and on raw intelligence per the Artificial Analysis index (60 vs 57). It's also faster — ~74 tok/s vs ~50 tok/s — which compounds in IDE autocomplete and tight inner-loop chat.

Claude Opus 4.7 wins on long-context coding discipline, agentic coherence over multi-hour runs, and IDE integration depth. Anthropic's news post claims Opus 4.7 "works coherently for hours" on long-running tasks. GitHub Copilot's Opus 4.7 announcement describes "stronger multi-step task performance and more reliable agentic execution" and "meaningful improvement in long-horizon reasoning and complex, tool-dependent workflows." Tom's Guide ran a 7-category comparison that Opus 4.7 swept, though that's a small-n consumer comparison, not a rigorous benchmark.

The IDE story matters for actual developer-day adoption. Opus 4.7 is the default high-tier model in Claude Code, in Cursor's max tier, and now in GitHub Copilot Pro+/Business/Enterprise across VS Code, JetBrains, Xcode, and github.com. GPT-5.5 has parity on availability but doesn't currently lead any IDE's "default agentic" slot. If you're picking a stack to standardize on for your engineers, that distribution reality matters as much as the benchmark deltas. We unpack the broader IDE landscape in our Cursor IDE complete guide.

Refactor and long-context behavior is where Opus 4.7 separates: 1M tokens at flat pricing, plus the new tokenizer and task budgets, give you headroom to dump whole repos into a single prompt without splitting a service-cost line item across two ledgers. GPT-5.5's context window isn't disclosed in the launch post; assume ~400k as a working number until OpenAI's docs catch up, and budget accordingly.

Reasoning and math

OpenAI is currently leading on the hardest published math evals. FrontierMath — widely regarded as the toughest current public math benchmark — sees GPT-5.5 at 51.7% on tiers 1–3 and 35.4% on tier 4. Anthropic has not published a comparable FrontierMath score on the Opus 4.7 news page as of writing.

For AIME 2025/2026, GPQA Diamond, and MMLU-Pro: neither vendor publishes a clean head-to-head on their respective launch pages. Artificial Analysis's composite Intelligence Index rolls these and several other evaluations into a single number; on that composite, GPT-5.5 (xhigh) leads Opus 4.7 (max) by 60 to 57 — close, but a real lead.

If your product depends on hardest-tier math or scientific reasoning — quant research, drug discovery, theorem-proving copilots — GPT-5.5 is the safer default in May 2026.

Agentic tool use

This is the dimension that's hardest to benchmark and the one CTOs are losing the most sleep over. Three things to watch:

Long-horizon coherence. Anthropic explicitly markets Opus 4.7 as the model that "works coherently for hours" and ships task budgets as a first-class primitive for capping agentic loops. If you're building agents that run for >30 minutes per task — code-review bots, finance-research agents, customer-support escalation handlers — this is a signal worth weighting. GPT-5.5's Terminal-Bench 2.0 score of 82.7% is a strong rebuttal on shorter, command-line-shaped agentic tasks.

Tool-call discipline. Opus 4.7 explicitly does "fewer tool calls by default", leaning more on internal reasoning, and spawns "fewer subagents by default." If you've spent the last year tuning prompts to stop GPT-5 or Sonnet 3.7 from over-calling tools, that scaffolding may now be counter-productive on Opus 4.7. GPT-5.5 hasn't published an equivalent behavior-change note.

MCP and ecosystem. Both vendors support Model Context Protocol; Anthropic's MCP-native posture is more developed simply because they invented it. For teams standardizing on MCP for tool plumbing, Opus 4.7 is the path of least resistance. We covered the broader landscape in our AI coding agents complete guide.

Cost economics

Anthropic lists Opus 4.7 at $5 / $25 per 1M input/output tokens, with up to 90% off via prompt caching and 50% via batch. OpenAI did not publish list pricing in the GPT-5.5 launch coverage we reviewed; Artificial Analysis's blended estimate puts GPT-5.5 (xhigh) at $11.30 per 1M tokens vs Opus 4.7 (max) at $10.90 — near-parity on blended cost.

The honest read: on tight chat loops, costs are close. On long-context coding work, Opus 4.7 has a structural advantage because the 1M window is at flat pricing. On heavy-output workloads (long generated reports, large code emissions), Opus 4.7's $25/M output is a real line item — if your use case is output-heavy and short-input, GPT-5.5 may be cheaper once OpenAI publishes API pricing. Run your own workload through both before committing.

When to pick which

Migration notes

Sonnet 3.7 / Opus 4.6 → Opus 4.7:

• Drop temperature, top_p, top_k — non-default values now 400.
• Replace thinking: {budget_tokens: N} with thinking: {type: "adaptive"} + output_config.effort.
• Set thinking.display: "summarized" if your UI streams reasoning — default is now omitted, which will look like a long pre-output pause.
• Bump max_tokens headroom by ~35% — new tokenizer is less efficient on some text shapes.
• Strip "double-check before answering" scaffolding — the model self-verifies more reliably and the prompt scaffolding can now hurt.

GPT-5 → GPT-5.5:

• API access lagged consumer launch by a day. Plan for staggered rollout.
• "Pro" is a separate variant gated to Pro/Business/Enterprise tiers — check your contract before assuming access.
• Pricing wasn't published in the launch post. Verify against the live OpenAI pricing page before committing capacity.

Evals you should actually run before committing

Vendor benchmarks are starting points, not decisions. Before you sign a six-figure capacity commit, run these five evals against your own workload:

1. Replay 200 real production prompts through both models, side by side. Score blind. This will tell you more than any leaderboard. Pay particular attention to the long tail — the bottom 10% of quality scores is where users churn.
2. End-to-end agent run on a real multi-step task from your product. Measure tokens consumed, wall-clock time, tool-call count, and final-output quality. Opus 4.7's "fewer tool calls by default" behavior often shifts the cost arithmetic vs. raw per-token price.
3. Long-context stress test: a 500k-token prompt drawn from your actual data, with a needle-in-haystack question at the start, middle, and end. Opus 4.7's 1M window is only a feature if recall holds up across it.
4. Latency p95/p99 under concurrency matching your peak load. Median tok/s lies; tail latency is what your users feel. GPT-5.5's median speed advantage may or may not survive your concurrency profile.
5. Refusal-rate diff on your domain. If you're in security, finance, or healthcare, run your edge cases through both. Opus 4.7 explicitly ships "real-time cybersecurity safeguards" that may refuse legitimate work; GPT-5.5 has its own refusal profile. Vendor-specific allow-list programs exist on both sides.

Budget two engineer-weeks for this work. It's the single highest-ROI engineering investment you'll make this quarter, and the artifacts (eval suites, replay harness) become permanent infrastructure for the next model migration — which, given the cadence of 2026, is six months away.

The hiring angle

Your model choice quietly reshapes who you should hire. A team standardizing on Opus 4.7 with 1M-context refactor agents and MCP tool plumbing needs engineers comfortable thinking in long-running asynchronous workflows, with strong instincts for prompt scaffolding, retrieval, and agent observability. A team building on GPT-5.5 in a chat-shaped product needs different instincts: tight latency budgets, function-calling discipline, evaluating output quality at scale, and managing the OpenAI roadmap risk that comes with rapid model deprecation cycles.

In practice, the engineers who do well on both stacks share one trait: they treat the model as a moving substrate, not a fixed dependency. They write evals before they write features. They version their prompts. They know when to switch models and when to switch problems. Hiring for that mindset is harder than hiring for any specific framework, which is why senior IC time is the bottleneck on most AI products in 2026.

FAQ

Is GPT-5.5 better than Claude Opus 4.7? On the Artificial Analysis Intelligence Index, GPT-5.5 (xhigh) leads 60 to 57 and is faster (~74 vs ~50 tok/s). On agentic coding, long-context refactors, and IDE-default deployment, Opus 4.7 currently has the edge. Neither is universally "better" — pick by workload.

Which is cheaper? On Artificial Analysis's blended estimate, near-parity ($11.30 vs $10.90 per 1M). Opus 4.7's $5/$25 list price with prompt caching is a structural advantage on input-heavy workloads. GPT-5.5 list pricing was not disclosed in the launch post we reviewed.

Does GPT-5.5 have a 1M-token context window? Not confirmed in OpenAI's launch coverage. Opus 4.7 explicitly ships 1M tokens at standard pricing with no long-context premium.

Which model is in GitHub Copilot? Both are available. Opus 4.7 went GA in Copilot on April 16, 2026 across VS Code, JetBrains, Xcode, and github.com for Pro+/Business/Enterprise.

Is the SWE-bench Verified score public? Neither vendor's launch page we reviewed publishes a single headline SWE-bench Verified number. Anthropic claims "state-of-the-art" on coding evals; OpenAI emphasizes Terminal-Bench 2.0 (82.7%) instead. Treat any third-party "leaked SWE-bench" claim with caution until the vendor confirms.

Should I migrate now or wait? If you're on Opus 4.6, the migration is non-trivial (sampling params removed, thinking format changed). Plan a one-sprint cutover. If you're on GPT-5, GPT-5.5 is largely drop-in — verify your prompts on the new model first, especially anything that depends on tool-call frequency.

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