GPT-5.5 vs Claude Opus 4.7
Two frontier flagships. Seven days apart. Split decision.
Anthropic shipped Claude Opus 4.7 on April 16, 2026. OpenAI shipped GPT-5.5 on April 23. Both have 1M-token context windows. Both are pitched as the lab's best work for agentic coding. The benchmarks tell a more interesting story than either announcement does — neither model wins outright, and the right answer depends on what you're actually doing.
OpenAI
GPT-5.5 wins on…
- →Terminal-Bench 2.0 (82.7% vs 69.4%)
- →Long-context retrieval (74.0% vs 32.2%)
- →OSWorld computer use
- →FrontierMath, customer-service workflows
Anthropic
Claude Opus 4.7 wins on…
- →SWE-bench Pro (64.3% vs 58.6%)
- →MCP-Atlas multi-tool agents (77.3%)
- →Vision (3.75MP, 3x prior resolution)
- →Finance Agent (64.4% SOTA)
The honest verdict: it's a split decision. The right model depends on the task. Which is exactly why running just one is leaving performance on the table.
Benchmark by benchmark
All numbers are vendor-reported from official launch announcements. Green cell marks the leader on each row.
| Benchmark | What it measures | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|---|
| SWE-bench Verified | Real-world software engineering tasks | ~80% | 87.6% |
| SWE-bench Pro | Harder multi-language engineering | 58.6% | 64.3% |
| Terminal-Bench 2.0 | Command-line agent workflows | 82.7% | 69.4% |
| MCP-Atlas | Multi-tool agentic orchestration | ~71% | 77.3% |
| OSWorld-Verified | Real desktop computer use | 78.7% | 78.0% |
| MRCR v2 (512K–1M) | Long-context retrieval | 74.0% | 32.2% |
| FrontierMath (1–3) | Olympic-level mathematics | 51.7% | 43.8% |
| GPQA Diamond | Graduate-level science | ~93% | 94.2% |
| Finance Agent | Financial analysis tasks | 60.0% | 64.4% |
| CharXiv (visual) | Reading dense charts & diagrams | ~74% | 82.1% |
| GDPval (knowledge work) | 44 white-collar occupations | 84.9% | ~78% |
| Tau2-bench Telecom | Customer-service workflows | 98.0% | ~89% |
| Wins (12 benchmarks) | 5 | 7 | |
Sources: openai.com/index/introducing-gpt-5-5, anthropic.com/news/claude-opus-4-7, vellum.ai, digitalapplied.com (April–May 2026). Where labs ran different methodologies, scores may not be perfectly comparable. Treat absolute numbers as directional.
Three gaps that actually matter
Most benchmark differences are within a few points. Three of them aren't — and those are the ones that should drive which model you reach for.
Long-context retrieval: 42-point gap
74.0% vs 32.2%
MRCR v2 at 512K–1M tokens
This is the largest gap on any benchmark in the comparison, and the most consequential. Both models advertise 1M-token context windows. Only one of them can actually find things in that context reliably.
If your work involves whole codebases, multi-document research, large legal contracts, long agent histories, or anything that genuinely uses the full context window — GPT-5.5 is the clear pick. Claude Opus 4.7 is faster at degrading into "I see this somewhere but can't quite locate it" once you push past 256K tokens.
Terminal workflows: 13-point gap
82.7% vs 69.4%
Terminal-Bench 2.0
Terminal-Bench measures whether a model can plan, iterate, and coordinate tools inside a real shell environment — the closest benchmark to "can this AI run a CI pipeline unattended?"
For DevOps automation, scripted server work, build-and-deploy agents, and anything that lives in a terminal: GPT-5.5 is materially better. This is also the benchmark where Anthropic openly conceded Claude regressed — a point worth taking seriously.
Real-world coding: 6-point gap
64.3% vs 58.6%
SWE-bench Pro
SWE-bench Pro is the harder version of SWE-bench Verified — it tests real production codebases across multiple languages, with the kind of bugs and refactors that don't have clean textbook fixes.
This is the benchmark closest to "what does it feel like to use this model day-to-day on real engineering work?" Opus 4.7's lead here is consistent with what shipping engineers and code-review tools (Qodo, Cursor, Warp) have reported in the wild.
What it costs you
GPT-5.5
Claude Opus 4.7
Output pricing is the meaningful difference: Claude is 17% cheaper per million output tokens. Note that GPT-5.5 uses a more efficient tokenizer for some inputs, while Opus 4.7's new tokenizer can use up to 1.35x more tokens than Opus 4.6 on the same text — the per-task cost can move either way depending on workload.
When to reach for which
A practical routing guide based on the actual benchmark gaps.
Reach for GPT-5.5
- →Anything that uses more than 256K tokens of context
- →Terminal automation, DevOps agents, CI pipelines
- →Computer-use agents (browser, desktop)
- →Hard math (FrontierMath, scientific computation)
- →Customer-service workflow automation
Reach for Claude Opus 4.7
- →Real-world software engineering and refactors
- →Multi-tool agents that orchestrate many MCP servers
- →Vision tasks: dense screenshots, diagrams, technical UIs
- →Financial analysis and document-heavy enterprise work
- →Long-form writing where natural prose matters
- →Following nuanced multi-step instructions precisely
Stop picking. Use both.
The benchmarks confirm what most users already know: different models win different tasks. MultipleChat AI gives you both — at the latest API versions — with current plan details.
Subscribe to both
current plan details
ChatGPT Plus + Claude Pro
ChatGPT Pro alone
current plan details
One ecosystem
MultipleChat AI
current plan details
Both — and 3 more models
Why this works for the GPT-5.5 vs Opus 4.7 split
When you have access to both, you stop guessing and start picking by task.
Compare Mode
Send the same prompt to GPT-5.5 and Claude Opus 4.7 in parallel columns. Pick the answer that's actually better — not the one a benchmark suggested would be.
Sources & Disagreements
When the two models disagree on a fact, you see exactly where. That's the case where one of them is wrong — and which one varies by task.
AI Collaboration modes
Have GPT-5.5 draft, Claude Opus 4.7 review. Or the reverse. Cooperative, Verification, Chain — ten interaction modes that play to each model's strengths.
Always the latest API versions
When OpenAI ships GPT-5.6 or Anthropic ships Opus 4.8, you get them through the same subscription. No re-subscribing, no migrating prompts, no model-version lock-in.
Plus Gemini, Grok, Perplexity Sonar
Three more frontier models in the same workspace. Gemini for the longest documents, Grok for fast iteration, Sonar for source-grounded research.
5 image models in parallel
Run image generation across 5 models at once from a single prompt. Compare styles instantly.
Same models. Latest API versions. Cancel anytime.
Frequently Asked Questions
Neither wins outright. GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs 69.4%), long-context retrieval (74.0% vs 32.2% at 1M tokens), and OS/computer use. Claude Opus 4.7 leads on SWE-bench Pro (64.3% vs 58.6%), MCP-Atlas multi-tool agents (77.3%), vision (3.75MP), and Finance Agent (64.4% SOTA). Pick by task.
It depends on what kind. For real-world software engineering tasks where multi-language fixes and complex production codebases matter, Claude Opus 4.7 wins on SWE-bench Pro (64.3% vs 58.6%). For terminal workflows, devops, and unattended pipeline agents, GPT-5.5 wins on Terminal-Bench 2.0 (82.7% vs 69.4%). For most production coding agents, Opus 4.7 has the edge.
GPT-5.5, by a wide margin. On MRCR v2 long-context retrieval at 512K–1M tokens, GPT-5.5 scores 74.0% versus Claude Opus 4.7's 32.2% — a 42-point gap. For entire codebases, multi-document research, or long agent histories, GPT-5.5 is the clear pick.
GPT-5.5 API pricing is published API rates for input and output tokens. Claude Opus 4.7 is current plan details — slightly cheaper on output. Both run on 1M-token context windows. ChatGPT Plus current plan details and Claude Pro current plan details both include access to their respective flagship at standard usage limits.
Yes. MultipleChat AI gives you the latest API versions of ChatGPT, Claude, Gemini, Grok and Perplexity Sonar in one one paid subscription. Compare Mode runs them in parallel on the same prompt so you can see which model wins on your specific task — rather than guessing from benchmarks.
Because frontier benchmarks confirm what users already experience: different models win different tasks. MultipleChat's Compare Mode shows you all answers side-by-side. Sources & Disagreements highlights where the models disagree on facts — exactly the cases where one model is right and another is hallucinating. AI Collaboration modes go further, having models verify or refine each other's work.
They are vendor-reported numbers from OpenAI's and Anthropic's official launch announcements (April 2026). Independent third-party benchmarks (Vellum, DigitalApplied) confirm the same general pattern. Methodology disclosures vary between labs, so treat absolute numbers as directional rather than precise.
Note on benchmarks: All scores in this article are vendor-reported figures from official OpenAI and Anthropic announcements, cross-referenced with third-party benchmark coverage (Vellum, DigitalApplied) as of May 2026. Where the two labs ran different methodologies on the same benchmark, scores may not be perfectly comparable — treat absolute numbers as directional. Model capabilities and pricing change frequently; verify current figures with each provider before making procurement decisions. MultipleChat AI does not guarantee the accuracy of third-party benchmark data and accepts no responsibility for decisions made based on this content.
Don't pick. Use both. with current plan details.
ChatGPT, Claude, Gemini, Grok and Perplexity Sonar in one subscription — at the latest API versions, with Compare Mode and AI Collaboration built in.
current plan details · Cancel anytime · No credit card to try