GPT-5.5 vs Claude Opus 4.7: Which AI Model Wins in 2026?

9 min readMay 7, 2026

Quick verdict

GPT-5.5 is the highest-scoring model on the intelligence leaderboard right now at 60.2. Claude Opus 4.7 trails by 2.9 points but leads on the benchmarks that matter most for real work: graduate-level reasoning (91.4% GPQA), expert-level problems (39.6% HLE), and autonomous agent tasks (88.6% TAU-bench). The gap between them is real but narrow.

For most people the honest answer is: it depends on what you're doing. GPT-5.5 for breadth and raw capability, Claude Opus 4.7 for depth, precision, and agentic workflows. Neither wins across every category.

The numbers

Metric	GPT-5.5 (xhigh)	Claude Opus 4.7
Intelligence Index	60.2	57.3
GPQA (grad-level science)	—	91.4%
HLE (Humanity's Last Exam)	—	39.6%
TAU-bench v2 (agents)	—	88.6%
TerminalBench Hard	—	51.5%
Coding Index	—	52.5
SciCode	—	54.5%
LCR	—	70.3%
Input cost / 1M tokens	$5.00	$6.25
Output cost / 1M tokens	$30.00	$25.00
Speed (tokens/sec)	79	61
Context window	1.1M	1.0M
Organization	OpenAI	Anthropic
Released	Apr 23, 2026	Apr 16, 2026

Source: LLMBase.ai via Artificial Analysis. May 2026.

What the intelligence gap actually means

A 2.9-point gap on the Intelligence Index (60.2 vs 57.3) sounds small, but it represents a real difference in how these models handle hard tasks. The Intelligence Index aggregates MMLU-Pro, GPQA, and HLE — benchmarks that test graduate-level knowledge, expert reasoning, and problems specifically designed to stump the best human experts in their fields.

The paradox is that Claude Opus 4.7 scores higher on two of those three underlying benchmarks (GPQA at 91.4%, HLE at 39.6%) while losing the aggregate index. That's because the index weights breadth of knowledge heavily, and GPT-5.5 has a wider knowledge surface. On the hardest subset of problems — the ones that actually require deep reasoning — Claude is closer or ahead.

This is why the verdict isn't simple. "Intelligence Index" is a useful shorthand but it doesn't tell you which model will do better on your specific problem. If your work involves expert-level reasoning on a narrow domain, Claude's depth may serve you better than GPT-5.5's breadth.

Where GPT-5.5 clearly wins

Speed and context. At 79 tokens per second versus Claude's 61, GPT-5.5 generates output roughly 30% faster — noticeable in interactive applications and real-time use cases. The 1.1M context window edges past Claude's 1.0M, which matters for large codebase analysis or very long document processing.

Breadth of capability is the other area. GPT-5.5 is OpenAI's most capable general-purpose model, trained across an enormous range of tasks. For users who need strong performance across many different domains without tuning to any specific one, the aggregate intelligence lead reflects a genuine advantage.

Input pricing: $5.00 per million input tokens versus Claude's $6.25. For input-heavy workloads (long prompts, large documents fed into context), GPT-5.5 is the cheaper option per token. The calculus flips on output: Claude at $25.00 per million output tokens is cheaper than GPT-5.5's $30.00, so for applications where output length dominates the cost, Claude is more economical.

Where Claude Opus 4.7 clearly wins

Agent tasks. The TAU-bench v2 score of 88.6% is the most important number in the table for anyone building AI workflows. TAU-bench tests real autonomous task execution — the kind of multi-step, tool-using, error-recovering work that agentic applications require. A published GPT-5.5 score on this benchmark isn't available for comparison, but Claude's 88.6% is extraordinary and reflects Anthropic's explicit focus on making Claude capable at autonomous work.

TerminalBench Hard at 51.5% tells a similar story — Claude can navigate complex terminal environments, execute commands, and handle the unexpected in a way that marks it as genuinely useful for coding agents, not just chat.

Coding depth. The 52.5 Coding Index and 54.5% SciCode put Claude well ahead on programming tasks. If you're building AI coding tools or using the model for serious software development work, Claude's coding capability is the better choice.

Watch: GPT-5.5 vs Claude Opus 4.7 tested

Real-world testing across writing, reasoning, and coding tasks:

The pricing breakdown

At similar capability tiers, pricing often becomes the deciding factor. Here's what the cost looks like at scale:

Monthly volume	GPT-5.5 cost	Claude Opus 4.7 cost
10M tokens (3:1 in/out)	$90.00	$109.38
100M tokens (3:1 in/out)	$900.00	$1,093.75
1B tokens (3:1 in/out)	$9,000	$10,937

At scale, GPT-5.5 runs about 18% cheaper on a 3:1 input-to-output ratio. If your workload skews toward longer outputs, Claude closes the gap. Neither model is cheap at frontier performance levels — this is the cost of running the best AI available.

How they compare on specific use cases

Research and analysis

Claude Opus 4.7. The GPQA and HLE scores reflect genuine expert-level reasoning capability that shows up when you're asking the model to synthesize complex academic material, evaluate competing arguments, or work through problems that require sustained logical depth. GPT-5.5 is strong here too, but Claude's scores on hard reasoning benchmarks suggest a ceiling advantage for the most demanding research tasks.

Writing and content

Close to a draw. Both models write exceptionally well. Claude has a slight stylistic edge for nuanced, voice-driven writing; GPT-5.5 is more versatile across formats. The right choice comes down to personal preference after testing both on your specific content type.

Coding and development

Claude Opus 4.7. The Coding Index, SciCode, and LCR scores, combined with TerminalBench performance, make Claude the better engineering model. For work involving autonomous coding agents — not just code completion but multi-step task execution — Claude's agent benchmarks cement this lead. See the coding agent comparison for tool-level details.

Customer-facing chat and Q&A

GPT-5.5. Broader knowledge base, faster responses, and a slightly more consistent tone across diverse subject areas make it the better default for general-purpose assistant applications where you can't predict what users will ask.

The practical answer

Most users who reach this comparison are trying to decide which model to use as their primary AI tool. The honest advice: don't pick one. Both GPT-5.5 and Claude Opus 4.7 have real capability advantages in different areas, and the cost of trying both is lower than the cost of using the wrong one for months.

Admix gives you access to both models — along with Gemini 3.1 Pro, DeepSeek V4 Pro, and 70+ others — from a single plan starting at $8/month. You can run the same prompt through GPT-5.5 and Claude Opus 4.7 side by side and see exactly where they diverge on your actual use case. That comparison is faster and more informative than any benchmark table.

FAQ

Is GPT-5.5 better than Claude Opus 4.7?

On the Intelligence Index, yes — 60.2 vs 57.3. On specific benchmarks like GPQA, HLE, and agent tasks, Claude Opus 4.7 leads. Which is "better" depends on your use case.

Which model is better for coding?

Claude Opus 4.7 by a clear margin — 52.5 Coding Index, 51.5% TerminalBench Hard, and 54.5% SciCode. For autonomous coding agents specifically, Claude's TAU-bench score of 88.6% reflects real capability for multi-step task execution.

Is Claude Opus 4.7 worth the higher input cost?

At $6.25 vs $5.00 per million input tokens, Claude costs 25% more on inputs. For coding, research, and agent tasks where Claude demonstrably outperforms, the premium is justified. For general-purpose chat where both models are comparable, GPT-5.5's lower input cost is worth taking.

What's the difference between GPT-5.5 xhigh and regular GPT-5.5?

GPT-5.5 comes in multiple effort tiers (xhigh, high, medium, low) that trade off thinking depth against speed and cost. The xhigh variant uses extended reasoning and delivers the highest benchmark scores. The Intelligence Index of 60.2 reflects xhigh performance; other tiers score lower (56.7 for medium, 50.8 for low).