GPT-5.5 vs Claude Opus 4.7: Which AI Model Wins in 2026?
A detailed comparison of GPT-5.5 and Claude Opus 4.7 — benchmarks, pricing, coding, reasoning, and real-world performance. Find out which AI model is best for you.
Two flagship models launched within a week of each other in April 2026 — Anthropic’s Claude Opus 4.7 (April 16) and OpenAI’s GPT-5.5 (April 23). Both claim the throne. But which one actually delivers?
We spent the past two weeks testing both models across coding, reasoning, creative writing, and everyday tasks. Here’s what we found.
Key Takeaways
- GPT-5.5 is natively omnimodal (text, image, audio, video) and drastically more token-efficient — 40-72% fewer output tokens on the same tasks.
- Claude Opus 4.7 dominates reasoning-heavy benchmarks and complex multi-file coding (SWE-bench Pro), with a massive vision upgrade and better agentic memory.
- Pricing is close: both charge $5/1M input tokens, but output costs differ ($30 for GPT-5.5 vs $25 for Opus 4.7). GPT-5.5’s token efficiency narrows the real-world gap.
- No clear overall winner — your best pick depends entirely on your workflow.
Benchmark Comparison
Let’s start with hard numbers. Here’s how both models stack up on the benchmarks that matter most in 2026:
| Benchmark | GPT-5.5 | Claude Opus 4.7 | Winner |
|---|---|---|---|
| SWE-bench Verified | 88.7% | 87.6% | GPT-5.5 |
| SWE-bench Pro | 58.6% | 64.3% | Opus 4.7 |
| Terminal-Bench 2.0 | 82.7% | 69.4% | GPT-5.5 |
| GPQA (Science) | 78.2% | 81.4% | Opus 4.7 |
| HLE (Hard Reasoning) | 21.3% | 24.1% | Opus 4.7 |
| OSWorld-Verified | 78.7% | 72.1% | GPT-5.5 |
| CursorBench (Coding) | 62% | 70% | Opus 4.7 |
| BrowseComp | 58.4% | 51.2% | GPT-5.5 |
| CyberGym | 89.3% | 84.7% | GPT-5.5 |
| Tau2-bench Telecom | 98.0% | 95.2% | GPT-5.5 |
The pattern is clear. GPT-5.5 wins on tool-use-heavy, shell-driven, and browsing benchmarks. Opus 4.7 wins on deep reasoning, science, and complex code review tasks.
Winner: Tie — it depends on the type of task.
Coding Performance
This is where most developers care. Both models are elite coders, but they shine in different ways.
GPT-5.5 for Coding
GPT-5.5 takes the #1 spot on SWE-bench Verified (88.7%) and absolutely crushes Terminal-Bench 2.0 (82.7%), which tests command-line-heavy workflows. For developers looking to leverage these models in an IDE, both are available in tools like Cursor and Claude Code. It’s also remarkably concise — generating far fewer tokens to solve the same problems.
If you’re running high-volume automated coding pipelines, GPT-5.5’s token efficiency translates directly into lower costs and faster execution.
Claude Opus 4.7 for Coding
Opus 4.7 leads on SWE-bench Pro (64.3% vs 58.6%), the harder version of the benchmark that requires resolving complex multi-file GitHub issues. It also scores 70% on CursorBench — a 12-point jump over its predecessor Opus 4.6.
Where Opus 4.7 really stands out is long-horizon agentic coding. Give it a complex refactoring task across dozens of files, and it maintains context and consistency better than any model we’ve tested. It introduced task budgets — letting you define token targets for full agentic loops — which makes long-running coding agents more predictable.
Winner: Claude Opus 4.7 for complex, multi-file projects. GPT-5.5 for speed and efficiency on standard tasks.
Reasoning and Science
For tasks that require deep analytical thinking — research, math, logic puzzles, scientific analysis — the models diverge.
Opus 4.7 leads on GPQA (graduate-level science QA) and HLE (hard language evaluation), with consistent advantages on reasoning-heavy benchmarks. Its adaptive thinking mode lets it dynamically allocate more compute to harder problems.
GPT-5.5 holds its own but tends to optimize for efficiency over depth. It’s faster at producing answers, but on the hardest problems, Opus 4.7 is more likely to get it right.
Winner: Claude Opus 4.7 for reasoning depth and accuracy.
Multimodal and Vision
This is where GPT-5.5 makes a strong architectural play.
GPT-5.5 is natively omnimodal — text, images, audio, and video are all processed in a single unified architecture. This isn’t bolt-on multimodality. Everything runs end-to-end in one system, which makes cross-modal tasks smoother and more coherent.
Claude Opus 4.7 made a massive leap in vision. It now supports 3.75MP images (up from 1.15MP), and visual acuity jumped from 54.5% to 98.5%. For image analysis and document understanding, this is a game-changer.
However, Opus 4.7 still doesn’t support audio or video natively. If you need to work with audio transcripts, video analysis, or multimodal workflows across more than text and images, GPT-5.5 is the only choice here.
Winner: GPT-5.5 for overall multimodal breadth. Opus 4.7 for pure image analysis quality.
Pricing Breakdown
Both models charge the same for input tokens, but the real cost story is more nuanced.
| GPT-5.5 | Claude Opus 4.7 | |
|---|---|---|
| Input (per 1M tokens) | $5.00 | $5.00 |
| Output (per 1M tokens) | $30.00 | $25.00 |
| Context Window | 1M tokens | 1M tokens |
| Max Output | — | 128K tokens |
| Token Efficiency | 40-72% fewer tokens | Standard |
On paper, Opus 4.7 is $5 cheaper per million output tokens. But GPT-5.5 generates significantly fewer tokens to accomplish the same tasks — OpenAI claims 40% fewer than GPT-5.4, and independent testing shows up to 72% fewer on certain workloads.
Important note on Opus 4.7: Anthropic kept the same price card as Opus 4.6, but the new tokenizer produces up to 35% more tokens for the same input text. So your actual bill per request can go up even though the rate didn’t change.
Winner: GPT-5.5 for effective cost on high-volume workloads. Opus 4.7 for low-volume or reasoning-heavy tasks where you want longer, more thorough responses.
Agentic Capabilities
Both models are designed for agentic workflows — autonomous task completion with tool use. But they approach it differently.
GPT-5.5 excels at browsing, computer use, and shell-driven tasks. Its OSWorld and Terminal-Bench scores reflect real-world ability to operate software, navigate the web, and chain together command-line tools. The Codex integration with a 400K context window makes it powerful for autonomous coding agents.
Claude Opus 4.7 is built for long-running, reliability-critical agent loops. Its new task budgets give developers fine-grained control over token consumption in agentic workflows. The model also has significantly improved file-system-based memory — agents that maintain scratchpads or structured notes across turns perform noticeably better.
Opus 4.7 also follows instructions more literally, especially at lower effort levels. It won’t silently generalize or infer requests you didn’t make. For production agents where predictability matters, this is a meaningful upgrade.
Winner: Claude Opus 4.7 for reliability and long-horizon agents. GPT-5.5 for breadth of tool-use and browsing.
Who Should Use What?
Choose GPT-5.5 if you:
- Need multimodal capabilities beyond text and images (audio, video)
- Run high-volume coding pipelines where token efficiency = cost savings
- Work primarily in the OpenAI Codex ecosystem
- Build agents that need browsing and computer use
- Want the fastest possible responses with minimal token overhead
Choose Claude Opus 4.7 if you:
- Do complex, multi-file software engineering (refactoring, reviews, migrations)
- Need deep reasoning for research, science, or analysis
- Build long-running agentic workflows that need reliability and predictability
- Work with high-resolution image analysis or document understanding
- Value literal instruction following and don’t want the model adding unsolicited extras
Verdict
| Category | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|
| Coding (standard) | 9/10 | 8.5/10 |
| Coding (complex) | 8/10 | 9.5/10 |
| Reasoning | 8/10 | 9/10 |
| Multimodal | 9.5/10 | 7.5/10 |
| Token Efficiency | 9.5/10 | 7/10 |
| Agentic Reliability | 8/10 | 9.5/10 |
| Value for Money | 8.5/10 | 8/10 |
| Vision Quality | 8/10 | 9.5/10 |
| Overall | 8.6/10 | 8.6/10 |
Yes, it’s a tie. And that’s the honest answer.
GPT-5.5 is the more versatile, efficient, and broadly capable model. Claude Opus 4.7 is the more precise, reliable, and deeper-thinking model. In 2026, there is no single “best AI” — there’s the best AI for your specific use case.
If you can only pick one, ask yourself: do I need breadth and speed, or depth and reliability? Your answer points you to the right model.
FAQ
Is GPT-5.5 better than Claude Opus 4.7?
Not universally. GPT-5.5 wins on multimodal capabilities, token efficiency, and browsing/tool-use benchmarks. Claude Opus 4.7 wins on complex coding, deep reasoning, and agentic reliability. It depends on your use case.
Which model is cheaper?
On the rate card, Opus 4.7 is cheaper for output tokens ($25 vs $30 per million). But GPT-5.5 uses 40-72% fewer tokens on the same tasks, so the effective cost can actually be lower with GPT-5.5 for many workloads.
Can I use both models?
Absolutely, and many teams do. A common pattern in 2026 is using GPT-5.5 for high-volume, multimodal, and browsing-heavy tasks, while routing complex reasoning and code review to Opus 4.7. See our ChatGPT vs Claude comparison for a more consumer-focused breakdown.
Which is better for coding?
Both are elite. GPT-5.5 leads SWE-bench Verified (88.7% vs 87.6%) and Terminal-Bench. Opus 4.7 leads SWE-bench Pro (64.3% vs 58.6%) and CursorBench (70%). For everyday coding, either works great. For complex multi-file engineering, Opus 4.7 has the edge. Check out our best AI coding tools roundup for tools that put these models to work.
Do both models support a 1M token context window?
Yes. Both GPT-5.5 and Claude Opus 4.7 support a 1M token context window through their respective APIs. Opus 4.7 supports up to 128K output tokens, while GPT-5.5 offers a 400K context window specifically in Codex.
Which model is better for building AI agents?
It depends on the type of agent. GPT-5.5 is better for agents that need to browse the web, use a computer, or work across multiple tools. Opus 4.7 is better for long-running, reliability-critical agents that need predictable behavior and strong memory across turns.