Comparisons
Claude Opus 4.7 vs GPT-5.5: The Real Reasoning Winner
A benchmark-driven breakdown of Claude Opus 4.7 vs GPT-5.5 for reasoning tasks. GPQA, SWE-bench, ARC-AGI-2 numbers, pricing, and honest use-case verdicts.
September 18, 2026
The Short Answer
If you care about graduate-level scientific reasoning, Claude Opus 4.7 is the pick. If you care about structured multi-step planning at massive context lengths, GPT-5.5 pulls ahead. That's the whole story in one line, but the interesting part is why the two models split the way they do.
Benchmark data from late 2026 shows Opus 4.7 landing at 94.2% on GPQA Diamond, sitting just behind Claude Fable 5 (94.6%) and comfortably above Gemini 3.1 Pro and GPT-5.6 Sol variants. GPT-5.5, meanwhile, is a generalist. It doesn't top any single reasoning leaderboard, but it holds up across MMLU (92.4%), SWE-bench Verified (88.7%), and ARC-AGI-2 (85%). And that's exactly what makes this comparison worth writing.
So let's get into it.
Claude Opus 4.7 vs GPT-5.5 at a Glance
| Dimension | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| Vendor | Anthropic | OpenAI |
| GPQA Diamond | 94.2% | Not published |
| MMLU | Not published | 92.4% |
| SWE-bench Verified | Not published | 88.7% |
| ARC-AGI-2 | Not published | 85% |
| Context window | 200K (per Opus family) | Check official docs |
| Pricing | Check official pricing | Check official pricing |
| Best at | Science, research, subtle writing | Broad reasoning, agentic coding |
A quick note on the blanks: neither vendor publishes every benchmark for every model. You'll see "not published" a lot in this piece, because the alternative is making up numbers, and this comparison is worthless if the numbers are fake.
Reasoning Benchmarks: Where the Split Actually Shows Up
The most interesting number in this whole comparison is GPQA Diamond. It's a subset of 198 expert-written multiple-choice questions in biology, chemistry, and physics (drawn from the larger 448-question GPQA benchmark) that non-experts fail even with Google. Claude Opus 4.7 scores 94.2% on it, according to Papers with Code's GPQA Diamond leaderboard. That's better than Gemini 3.1 Pro (94.1%) and better than every GPT-5.5-tier OpenAI checkpoint currently public.
But GPT-5.5 has its own trophies. On ARC-AGI-2, one of the hardest abstract reasoning benchmarks around, GPT-5.5 sits at 85%. That's below the specialized GPT-5.6 Sol variant (92.5%) but still a serious result. Opus 4.7 doesn't have a public ARC-AGI-2 number, so drawing a comparison here would be dishonest.
The MMLU picture is subtler. GPT-5.5 hits 92.4%, Claude Opus 4.6 hit 92.3%, and 4.7 hasn't been reported on that particular test. Basically a tie, in other words, and MMLU is increasingly a saturated benchmark anyway. Frontier models cluster within a point or two, so it's not a useful tiebreaker in 2026.
Where Claude Opus 4.7 shines
Based on the GPQA numbers and community reports, Opus 4.7 pulls ahead when:
- The task requires connecting concepts across specialized domains (biochem plus statistics, for example)
- You're asking the model to reason against its own initial answer (self-correction on hard problems)
- The prompt has ambiguity that benefits from careful clarification rather than confident guessing
And honestly, that last point is where Anthropic's models have always felt different. Opus 4.7 will pause. It'll say "the question could mean X or Y" instead of picking one and running. For research and legal work, that's a real feature.
Where GPT-5.5 shines
Based on OpenAI's official model documentation and the SWE-bench data, GPT-5.5 wins when:
- The task is long, structured, and agentic (multi-step tool use over hours)
- You need reliable code generation with strong test-passing rates
- You want a model that just decides and moves, not one that hedges
SWE-bench Verified is the tell. GPT-5.5's 88.7% is very solid, and while newer models like Claude Opus 5 (96%) and GPT-5.6 Sol (96.2%) have since eclipsed both, in the 4.7-vs-5.5 head-to-head at their respective tiers, GPT-5.5 has published SWE-bench numbers and Opus 4.7 doesn't. Read into that what you will.
Feature-by-Feature Breakdown
1. Chain-of-thought behavior
Both models expose extended thinking. Anthropic calls it thinking mode; OpenAI has been shipping reasoning models under the reasoning_effort parameter since the o-series. In practice, Opus 4.7's thinking traces tend to be longer and more exploratory. GPT-5.5's reasoning traces are tighter and more goal-directed. Neither approach is objectively better. It depends on whether you want the model to "consider" or to "decide."
2. Tool use and agentic tasks
GPT-5.5 has the maturity edge here. OpenAI's Responses API and the newer Agents SDK have been iterated on longer, and the ecosystem around function calling is deeper. Anthropic caught up fast with Claude Code and MCP (Model Context Protocol), but if you're building a heavy agent stack today, GPT-5.5 is the safer bet purely because of tooling breadth.
That said, Claude Opus 4.7 in Claude Code is widely regarded as the best terminal-based coding agent available. So agentic doesn't just mean cloud API calls.
3. Long context reasoning
Opus 4.7 keeps the 200K token window that the Opus family has held since 4.5. GPT-5.5's context window varies by endpoint, so check the official model card for your specific deployment. Both handle long contexts well in isolation, but Claude has historically shown stronger "needle-in-haystack" recall at deep positions in the prompt. If you're building RAG over long documents, that matters.
4. Coding as a reasoning proxy
Coding is basically applied reasoning, and this is where GPT-5.5's 88.7% on SWE-bench Verified matters. It's not just producing syntactically correct code. SWE-bench measures whether the model can read a real GitHub issue, work through a real codebase, and produce a patch that passes the real test suite. That's reasoning under constraint.
Claude Opus 4.7 doesn't have a public SWE-bench number in the current SWE-bench leaderboard, which is genuinely frustrating for a comparison piece. Anecdotally, Opus models tend to perform strongly on coding tasks (Opus 4.6 was already highly capable), so it's reasonable to assume 4.7 improves on that. But assumptions aren't benchmarks.
5. Refusal and safety behavior
This is unmeasurable in benchmarks and matters a lot in practice. Opus 4.7 refuses less than earlier Claude generations while remaining conservative on genuinely risky asks. GPT-5.5 is looser by default but supports fine-grained developer messages to tighten behavior. For enterprise deployments in regulated industries, Claude's baseline calibration is usually a smaller lift.
Pricing: The Uncomfortable Truth
Pricing on frontier models moves quarterly, and the vendors don't always publish updated numbers cleanly. For current rates on Claude Opus 4.7, check Anthropic's pricing page. For GPT-5.5, check OpenAI's pricing page.
A few observations that hold regardless of the exact numbers:
- Opus-tier Anthropic pricing has historically been a premium over OpenAI's frontier tier at equivalent capability.
- Prompt caching (both vendors offer it) can knock 50-90% off input costs on repeated context. Use it.
- Batch APIs on both platforms typically halve costs if you can tolerate 24-hour turnaround.
If raw dollars per million tokens is your top criterion, GPT-5.5 is almost certainly cheaper. If quality per dollar on hard reasoning is your criterion, Opus 4.7 competes better than the sticker price suggests. And if you're an individual builder on a tight budget, honestly, use Claude Haiku 4.5 or a smaller OpenAI tier and save the frontier calls for tasks that actually need them.
Real Benchmark Numbers, Side by Side
Here's the honest comparison, with gaps left as gaps:
| Benchmark | Claude Opus 4.7 | GPT-5.5 | Notes |
|---|---|---|---|
| GPQA Diamond | 94.2% | Not published | PhD-level science reasoning |
| MMLU | Not published | 92.4% | General knowledge, saturated |
| SWE-bench Verified | Not published | 88.7% | Real-world coding tasks |
| ARC-AGI-2 | Not published | 85% | Abstract reasoning |
| GSM8K | Not published | Not published | Grade-school math |
The pattern is clear: Anthropic publishes GPQA and Chatbot Arena numbers heavily. OpenAI publishes SWE-bench, ARC, and MMLU heavily. Each is showing off where they win. Read benchmark tables from vendors with that in mind, always.
For independent verification, the LMSYS Chatbot Arena leaderboard gives Elo-style ratings from head-to-head human preference votes. Claude Opus 4.8 currently sits at 1580 Elo at the top, and Claude Opus 4.6 (thinking) hit 1501 back when it was released. That trajectory suggests Opus 4.7 is likely somewhere in between, though it's not always separately listed on the arena.
When to Choose Claude Opus 4.7
Pick Opus 4.7 if:
- You do scientific research, technical writing, or analysis that touches multiple specialized domains
- Your work involves ambiguous questions where a hedge is more useful than a confident wrong answer
- You prefer thorough thinking traces over fast decisions
- You're heavily invested in Claude Code as your coding agent
- Subtle writing matters more than raw throughput
Legal analysis, academic research, medical literature review, and hard debugging where you need the model to actually think about edge cases: Opus 4.7 territory.
When to Choose GPT-5.5
Pick GPT-5.5 if:
- You're building an agentic system that needs mature tool-use infrastructure
- You need published SWE-bench performance for your engineering pitch
- You want the broader third-party integration ecosystem (Assistants API, Realtime API, Batch)
- Cost per token matters more than a few points of GPQA
- Your workload is high-volume classification, summarization, or structured extraction
Customer support agents, high-volume content pipelines, coding copilots outside of Claude Code, and general-purpose chat products where breadth of skill beats depth in one area: GPT-5.5 is the safer pick.
The Elephant in the Room: Both Are Being Lapped
Worth mentioning honestly. As of late 2026, Claude Opus 5, Claude Fable 5, and GPT-5.6 Sol have all posted numbers that exceed both 4.7 and 5.5. The ARC Prize leaderboard shows GPT-5.6 Sol at 92.5% on ARC-AGI-2 and Claude Opus 5 at 90.4%. On SWE-bench Verified, both Opus 5 (96%) and GPT-5.6 Sol (96.2%) are well past the 88.7% GPT-5.5 line.
So why compare 4.7 and 5.5 at all? Two reasons. Cost is one: the newer models are more expensive and, in some cases, not yet generally available. Stability is the other. If you're running production workloads, you probably want a model that's been in the wild for a few months and has known failure modes, not one that dropped last week. 4.7 and 5.5 are both mature enough for serious work.
The best model for your app is rarely the newest model. It's the one whose behavior you can predict.
That's especially true when you're building agents that run for hours without supervision. A model you trust at 90% is worth more than a model you don't understand at 95%.
Final Verdict
For pure reasoning quality on graduate-level science and multi-domain problem solving, Claude Opus 4.7 wins. The GPQA Diamond number is the clearest signal in the entire comparison, and it aligns with what long-time Claude users describe qualitatively.
For agentic engineering, cost-sensitive scale, and structured multi-step tasks, GPT-5.5 wins. The SWE-bench and ARC-AGI-2 numbers back that up, and the tooling ecosystem does the rest.
If you can afford both, use both. Route reasoning-heavy prompts to Opus 4.7 and volume traffic to GPT-5.5. That's what the smart production teams are doing in 2026, and it's not even close to a controversial take anymore. If you're still torn on the ecosystem side, our OpenAI vs Anthropic API breakdown goes deeper on the platform tradeoffs.
