Is GPT-5.5 or Claude Opus 4.7 better right now?

Claude Opus 4.7 leads on coding (Arena coding Elo 1567, SWE-bench Pro 64.3%) and long-context retrieval. GPT-5.5 leads on structured reasoning, real-time voice, and ecosystem breadth. On the LMSYS Arena overall, Opus 4.7 sits at 1497 and GPT-5.5 at 1481 — close enough that workload shape matters more than the leaderboard.

Which is cheaper at scale?

Claude Opus 4.7 is cheaper on output ($25 vs $30 per 1M) and matches GPT-5.5 on input ($5 per 1M). On a typical 70/30 input-output mix, Opus 4.7 lands ~10% cheaper. Both offer prompt caching and batch (50% discount), so effective pricing depends on traffic shape.

Does GPT-5.5 still beat Claude on reasoning benchmarks?

Yes on AAII (59 vs 57) and on math-heavy reasoning suites. But Opus 4.7 closed most of the gap on GPQA Diamond and now leads on the agentic SWE benchmarks. The gap has narrowed enough that for most production workloads, latency and price matter more than the residual benchmark delta.

Can I use both together?

Yes. The most common pattern is a router that sends coding and long-context jobs to Opus 4.7 and reasoning/voice/structured-output jobs to GPT-5.5. Teams using a gateway typically save 20-35% versus single-model routing while improving accuracy on workload-specific evals.

What about safety and refusal rates?

Both are similar at the production tier. Opus 4.7 tends to ask more clarifying questions and refuse less on borderline content; GPT-5.5 is more likely to attempt and add a caveat. Pick based on your domain — legal/medical teams tend to prefer Opus 4.7's more conservative refusals.

Updated May 6, 2026

GPT-5.5 vs Claude Opus 4.7 (May 2026): Side-by-Side Comparison

TL;DR: Claude Opus 4.7 wins for coding, long-context, and agentic workloads. GPT-5.5 wins for reasoning, structured output, and real-time voice. The benchmark gap is small; pick based on workload shape, not the leaderboard.

Spec comparison

Spec	GPT-5.5	Claude Opus 4.7
Input price (per 1M)	$5.00	$5.00
Output price (per 1M)	$30.00	$25.00
Context window	1M tokens	1M tokens
Arena Elo (latest)	1481	1497
Coding Elo	1542	1567
SWE-bench Pro	61.8%	64.3%
MMLU	90.4	91.2
AAII (frontier index)	59	57
Best for	Reasoning, structured output	Coding, long-context, agents

Feature matrix

Capability	GPT-5.5	Opus 4.7
Native tool / function calling	✓	✓
Structured JSON output mode	✓	~
Vision / image input	✓	✓
Computer use (agent)	~	✓
Prompt caching	✓	✓
Batch API (50% off)	✓	✓
Fine-tuning available	✓	✗
Real-time / streaming voice	✓	✗
Extended thinking / reasoning mode	✓	✓
Code execution sandbox	✓	✓
1M+ token context	✓	✓
Document/PDF native parse	~	✓
Top-tier on SWE-bench Pro	~	✓
Top-tier on GPQA Diamond	✓	~
Open weights	✗	✗

Cost analysis (1M I/O tokens)

Workload (input / output)	GPT-5.5	Opus 4.7	Delta
10K in / 1K out (chat)	$0.080	$0.075	Opus -6%
50K in / 5K out (long doc)	$0.400	$0.375	Opus -6%
100K in / 20K out (coding)	$1.100	$1.000	Opus -9%
5K in / 30K out (gen-heavy)	$0.925	$0.775	Opus -16%

Cached input is 90% off on both providers. Batch is 50% off. Real cost can drop 4-6x with both enabled.

When GPT-5.5 wins

GPT-5.5 wins for reasoning-dense workloads, structured output, and real-time voice applications. The AAII score (59 vs 57) reflects an edge on multi-step deductive reasoning, math-heavy chains, and the kind of GPQA-style scientific QA that quant teams care about. GPT-5.5's structured output mode is still the most reliable in production — guaranteed JSON-schema conformance with a measurable lower retry rate than any other frontier model. The Realtime API and voice agents are far more mature than Anthropic's offering, which matters for telephony, customer service, and live tutoring. The fine-tune path is also unique: you can ship a domain-tuned GPT-5.5 today, while Anthropic still does not offer customer fine-tuning of Opus. If your stack already runs on OpenAI SDKs, the migration cost calculus will keep tilting toward GPT-5.5 for adjacent workloads.

When Claude Opus 4.7 wins

Opus 4.7 wins for coding, long-context retrieval, and agentic tool-use loops. The 1567 coding Elo and 64.3% SWE-bench Pro lead are not marginal — they translate to measurably fewer broken PRs in production. Long-context behaviour is the other big differentiator: Opus 4.7 maintains reasoning quality deeper into its 1M-token window than any competitor, which matters for legal review, codebase analysis, and document extraction. Computer-use (the agent that drives a browser/desktop) is genuinely better here than on any other model. Pricing on output is 17% cheaper, which compounds for generation-heavy workloads. And the tokenizer + prompt format are just well-suited to engineering documentation. If you are building a coding agent, a contract analyzer, or any system that lives in the 200K-1M context range, Opus 4.7 is the right default.

The common combination

Most production teams do not pick one. They route per workload class: Opus 4.7 for coding agents and long-context, GPT-5.5 for reasoning and voice, DeepSeek V4 Flash for high-volume classification. A simple cascade pattern — try cheap, escalate on uncertainty, fall back to the right frontier model — saves 20-40% versus single-model strategies. Swfte's router implements this with provider-agnostic failover so a model deprecation does not become an outage. The point is: in May 2026 the gap between Opus 4.7 and GPT-5.5 is small enough that "pick the right one per request" is cheaper than "pick the best one overall."

How to choose

Build a 200-prompt eval set from your real production traffic. Run it on both, compare on quality and cost.
Segment evals by workload class — coding, reasoning, structured output, long-context — and read the per-class winner.
Enable prompt caching on whichever provider hits 70%+ of your traffic. Caching dwarfs base-rate optimization.
For mixed workloads, deploy a router rather than a single-model contract. Pin per-class default models.
Add a regression test that compares both models on the prior month's traffic. Catch silent quality drift.
Re-evaluate every release. The gap moves in months, not years; lock-in costs compound silently.