The race for frontier AI intelligence has shifted from raw size to reasoning capability. In 2026, two flagship models launched just a week apart define this era: OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.7. Both "think" before they respond and both ship with 1-million-token context windows, but they approach developer workflows, agentic execution, and integration very differently.
The choice between these two platforms is no longer just about who leads the general leaderboard. It is about how you build — whether you need agentic terminal execution and computer use, deep software-engineering refactors, or tool orchestration via the Model Context Protocol (MCP). This guide breaks down their performance across coding, agentic tasks, reasoning, and costs.
By the end of this comparison, you will know exactly when to route your queries to OpenAI's GPT-5.5 and when to rely on Anthropic's Claude Opus 4.7.
Quick Specs Side-by-Side
Feature | OpenAI GPT-5.5 | Claude Opus 4.7 |
|---|---|---|
Thinking Model Type | Automatic agentic reasoning | Adjustable effort levels (low to max, incl. new xhigh) |
Context Window | 1,000,000 tokens | 1M input / 128K output |
Multimodality | Text + Vision input | Text + Vision input |
Pricing (Input per 1M) | $5.00 | $5.00 |
Pricing (Output per 1M) | $30.00 | $25.00 |
Developer Ecosystem | OpenAI API, ChatGPT, Codex | Anthropic API, Claude Code, native MCP, Bedrock/Vertex/Foundry |
Benchmark Scorecard: Code and Agentic Tasks
In standard benchmark evaluations, the two models trade wins depending on whether the test measures deep codebase understanding or multi-step agentic execution.
Benchmark | GPT-5.5 (OpenAI) | Claude Opus 4.7 (Anthropic) | Winner |
|---|---|---|---|
SWE-bench Verified (Coding) | 80.6% | 87.6% | Claude Opus 4.7 (+7.0) |
SWE-bench Pro (Real GitHub issues) | 58.6% | 64.3% | Claude Opus 4.7 (+5.7) |
Terminal-Bench 2.0 (Agentic CLI) | 82.7% | 69.4% | GPT-5.5 (+13.3) |
GPQA Diamond (PhD-level Q&A) | 93.6% | 94.2% | Claude Opus 4.7 (+0.6) |
Coding and Software Engineering: Claude Opus 4.7 leads both SWE-bench Verified (87.6% vs 80.6%) and the harder SWE-bench Pro (64.3% vs 58.6%). It is currently the strongest model for finding and fixing bugs in real, multi-file code repositories, and when paired with Claude Code (Anthropic's agentic CLI) it operates with high autonomy on complex codebases. (Note: OpenAI's system card flags possible memorization on SWE-bench Pro; Anthropic published a decontaminated re-score that holds its lead.)
Agentic and Terminal Execution: GPT-5.5 wins decisively on Terminal-Bench 2.0 (82.7% vs 69.4%) and edges ahead on computer use (OSWorld-Verified 78.7% vs 78.0%). Its strength is planning and executing long, multi-step command-line and tool workflows end to end.
Key Differences for Developers
1. Reasoning Control
Claude Opus 4.7 exposes developer-controlled effort levels — low, medium, high, the new xhigh, and max — plus task budgets (beta), so you can dial reasoning depth up for hard refactors or down for simple tasks to manage cost and latency. GPT-5.5 instead decides automatically how deeply to reason for a given prompt, trading manual control for simplicity.
2. Tool Use and MCP Integration
Anthropic builds native support for the Model Context Protocol (MCP) into its ecosystem, and Opus 4.7 leads on MCP-Atlas (79.1% vs 75.3%) for reliable multi-tool orchestration — reading databases, calling APIs, and running terminal commands. GPT-5.5 counters with strong agentic computer use through Codex and the OpenAI API, making it excellent for autonomous, tool-driven task completion.
3. Vision and UI Design
Both models process visual inputs (Opus 4.7 handles images up to ~3.75 megapixels). Claude Opus 4.7 excels at parsing complex UI layouts, reading scientific charts, and translating mockups into clean, responsive CSS and HTML, making it a favorite for front-end and full-stack development where visual accuracy matters.
Pricing and Token Efficiency
On sticker price the two are close. GPT-5.5 costs $5 per million input tokens and $30 per million output tokens — a price OpenAI roughly doubled from GPT-5.4's $2.50/$15 (the lab estimates a ~20% effective increase after token-efficiency gains). Claude Opus 4.7 holds steady at $5 per million input and $25 per million output, identical to Opus 4.6 — though its updated tokenizer can map the same text to up to 35% more tokens, so measure real traffic. On output, Opus 4.7 is the cheaper rate ($25 vs $30); prompt caching and batch processing cut costs further on both.
How This Connects to Your Website
As developer teams deploy agents powered by GPT-5.5 and Claude Opus 4.7 to automate coding, research, and data gathering, these agents will crawl the web to find documentation, API references, and product specifications.
For these reasoning models to read your website efficiently without burning their token budgets on HTML boilerplate and navigation menus, you need to offer them a clean, machine-readable format. This is exactly what the llms.txt standard achieves.
By placing a curated llms.txt file at your domain root, you ensure that GPT-5.5 and Claude Opus 4.7 agents find the exact pages they need, leading to accurate citations when users ask AI engines about your products or services.
Generate your free llms.txt file in 60 seconds and make your site compatible with the next generation of reasoning agents.
Conclusion
The frontier of AI is no longer a single leaderboard. GPT-5.5 leads in agentic terminal execution, computer use, and long-context retrieval, while Claude Opus 4.7 dominates software-engineering benchmarks, MCP tool orchestration, and offers cheaper output pricing. Rather than choosing one, many production systems use a routing layer to send agentic command-line and research tasks to GPT-5.5, and software refactors plus MCP-heavy tool workflows to Claude Opus 4.7.
No matter which model wins the next benchmark release, the rise of AI agents means your website needs to be machine-readable. Generate your free llms.txt file today to secure your visibility in AI-driven search.
Frequently Asked Questions
What is the difference between GPT-5.5 and Claude Opus 4.7?
GPT-5.5 is optimized for agentic, multi-step terminal and computer-use workflows and long-context retrieval. Claude Opus 4.7 is optimized for software engineering (SWE-bench-style refactors), MCP tool orchestration, and vision, and offers developer-controlled effort levels. Both ship with 1M-token context windows.
How do effort levels work in Claude Opus 4.7?
In the Anthropic API, developers can choose an effort level — low, medium, high, xhigh, or max — and set task budgets to scale the model's "thinking time" up for hard tasks like refactoring code or down for simple tasks like formatting text, controlling cost and latency.
Which model is better for coding?
Claude Opus 4.7 leads the coding benchmarks, scoring 87.6% on SWE-bench Verified versus GPT-5.5's 80.6%, and 64.3% versus 58.6% on the harder SWE-bench Pro. It integrates directly with the Claude Code agentic CLI. GPT-5.5, however, leads agentic terminal execution (82.7% on Terminal-Bench 2.0), so it is stronger for end-to-end command-line automation.
Do these models read llms.txt files?
Yes. Both OpenAI's GPTBot and Anthropic's ClaudeBot crawl llms.txt files to discover and prioritize website content for training and live retrieval features.