GPT-5.5 vs Claude Opus 4.7: A Practical Head-to-Head — Benchmarks, Coding, Agents, Pricing, How to Choose

In April 2026, two flagship AI models shipped within a single week of each other: Anthropic Claude Opus 4.7 (April 16) and OpenAI GPT-5.5 (April 23). Both are pitched as the "next-generation agent foundation," yet their design philosophies, sweet spots, and pricing structures could hardly be more different.

This article compares the two head to head using public benchmarks, official documentation, and third-party evaluations, then asks the practical question: which one should you actually use, and when?

1. Where each model stands

Both models are flagships gunning for "the lead role in agentic workloads," but their pitches diverge sharply.

Claude Opus 4.7 — the craftsman who finishes the job in your codebase

Anthropic positions Opus 4.7 as the strongest model for real-world software engineering. It scores 87.6% on SWE-bench Verified and 64.3% on SWE-bench Pro, beating every other publicly available model on patch-generation tasks against real GitHub repositories. A new tokenizer ships with it, the visual resolution jumps from 1.15MP to 3.75MP, and the additions clearly target long-running agents: an xhigh effort level, task budgets (beta), and the /ultrareview command in Claude Code.

GPT-5.5 — the omnimodal generalist that operates your machine

OpenAI describes GPT-5.5 as "a new class of intelligence for real work and AI agents." It is natively omnimodal, handling text, images, audio, and video in a single model, and it tops the leaderboard on agent-style benchmarks: 82.7% on Terminal-Bench 2.0, 78.7% on OSWorld-Verified, and 98.0% on Tau2-bench Telecom — winning on planning, terminal control, and customer-support workflows. Other selling points are deep Codex integration and an efficiency claim of roughly 40% fewer output tokens versus GPT-5.4.

2. Spec sheet at a glance

Lined up against the official documentation, the headline specs look like this.

Pricing and specs as of May 2026. Note: thanks to the new tokenizer, Opus 4.7 consumes 1.0–1.35x more tokens than Opus 4.6 for the same text.

3. Benchmark deep dive

The cliché says flagships are "neck and neck," but benchmark by benchmark there is a clear pattern. Their strong suits are almost mirror images of each other.

3-1. Coding

The key thing is what each benchmark actually measures. SWE-bench Pro / Verified evaluate patch generation against real GitHub issues — that is, the ability to modify an existing codebase. Terminal-Bench 2.0, by contrast, scores agents that autonomously drive a terminal from the command line, measuring the plan-and-execute loop. Opus 4.7 wins the former, GPT-5.5 wins the latter — which translates directly into the practical split: "Opus for landing big PRs in Cursor, GPT for building from scratch in the CLI."

3-2. Agents and tool use

Across agent benchmarks overall, GPT-5.5 has broader strength. The gap shows up in OS control, customer support, and composite tool chains — the territory closest to "business automation." Opus 4.7 holds its lead on deep tool use over MCP (Model Context Protocol) and long-running coding sessions in Cursor / Claude Code.

3-3. Reasoning and knowledge work

GPQA Diamond (graduate-level reasoning) is essentially a tie. On Anthropic's GDPVal-AA — a knowledge-work Elo covering 44 occupations — Opus 4.7 leads GPT-5.4 by 79pt, but GPT-5.5's score on the same benchmark hasn't been published; that area is still being updated. For now, treat "logical reasoning and PhD-grade knowledge tests" as effectively even.

4. Real-world cost — the token-efficiency wall

Look at sticker prices and Opus 4.7 ($25/MTok) is cheaper than GPT-5.5 ($30/MTok). But on real projects the invoice often flips — and the reason is how many output tokens each model produces.

Opus 4.7's signature "narrate-then-code" pattern — say what you'll do, do it, then summarize what you did — is a real asset for code review and learning. But if all you want is the deliverable, those extra output tokens are wasted spend. GPT-5.5 is the opposite: it cuts straight to the result, but "why it wrote it that way" is harder to see. The fit depends on what you actually want from the project.

Watch out for the new tokenizer too. Opus 4.7 uses 1.0–1.35x more tokens than Opus 4.6 for the same Japanese text, so for long Japanese prose or long design docs the input side gets more expensive as well.

5. Strengths and weaknesses at a glance

Compressing everything above onto a single page:

6. Pick the right model for the job

"Which one should I use" splits cleanly along task type.

Third-party evaluations (DataCamp, MindStudio, llm-stats and others) repeatedly land on the same split: "GPT for automating new builds, Opus for fixing existing code and running long-lived agents."

7. Migration and dual-vendor strategy

The pragmatic answer in May 2026 is not "pick one and standardize" but "pick the right tool per task" — that optimizes both cost and quality.

Pattern A. Dual-vendor operation (recommended)

• Core coding (Cursor / Claude Code): Opus 4.7
• CLI and terminal automation: GPT-5.5
• Business RPA and support chatbots: GPT-5.5
• Long-document analysis and classification: GPT-5.5 (short outputs are cheap)
• Review and PR-approval assistance: Opus 4.7 (narrated reasoning doubles as audit log)

Pattern B. Router approach

Use OpenRouter / LiteLLM and similar to classify task type and dispatch dynamically. A simple rule — coding to Opus, agent work to GPT, reasoning to whichever is cheaper — keeps vendor lock-in low and pushes real costs down.

Pattern C. Single-vendor operation

If security or data-governance constraints rule out using more than one vendor, choose by primary use case. As of May 2026, the straightforward call is Opus 4.7 for organizations with large SaaS code estates, and GPT-5.5 for organizations centered on business-process automation.

Summary

• Opus 4.7: top for real codebase work and deep MCP / Cursor use. The craftsman. Output tokens are heavy and cost adds up, but the visible chain of thought pays off in audit and review.
• GPT-5.5: broadly strong on terminal control, customer support, and omnimodal tasks. Output tokens are low and real-world cost is roughly a quarter of Opus — at the price of thin explanations.
• Reasoning is essentially even. The 0.6pt gap on GPQA Diamond is noise.
• How to choose: don't aggregate benchmark scores — ask which benchmark most resembles your actual work.
• The pragmatic answer in May 2026 is to run both and split by task. That gives the best cost/quality outcome.

FAQ

Q1. Which is the "next-generation" model — Claude Opus 4.7 or GPT-5.5?

Same generation. They shipped a week apart, and it's most accurate to view them as the two flagships of the same generation. The difference is design philosophy, not generation.

Q2. Opus has the lower sticker price — why does GPT often come in cheaper in practice?

Because Opus emits a narrated chain of thought plus code plus a summary, its output token count is high. GPT cuts straight to the answer and uses about 72% fewer output tokens. Compare invoices on the same task and the difference can land near 1/4.

Q3. I'm on Cursor / Claude Code — which model should I optimize for?

Day-to-day development inside Cursor / Claude Code is still best with Opus 4.7. Editor integration, MCP wiring, and the narrate-then-code habit all sit well with how IDEs feel.

Q4. What about building a business agent or chatbot?

GPT-5.5. With Tau2-bench Telecom 98% and OSWorld 78.7% it leads broadly across business-automation work, and being omnimodal it can handle phone, voice, and image input in the same model.

Q5. Reasoning benchmarks are tied — but for genuinely hard problems, which is better?

GPQA Diamond at 94.2% vs 93.6% is effectively even. The realistic split is operational: GPT-5.5 for long-context composite reasoning, Opus 4.7 when you want step-by-step explanation along the way.

Q6. Is migrating from older GPT-4 / Claude 3 worth it?

Yes, substantially. The generation jump shows roughly 30–40pt of SWE-bench movement on coding tasks, and 20–30pt on OSWorld / Terminal-Bench for agentic work. Updating models on long-running projects is becoming a standard call to make during 2026.

Q7. As an end user (ChatGPT / Claude.ai), how should I pick?

Roughly the same logic as the work split: Claude.ai when you want code written, ChatGPT for research, summarization, audio, and image generation. If you'll only pay for one, choose by your dominant use case to avoid mismatch.