Claude Opus 4.7 vs GPT-5.5: Which AI Model Should You Use?

A careful comparison starts with an evidence gap. Claude Opus 4.7 has more published detail in the cited material for software engineering, MCP-style tool use, context and vision, while OpenAI’s GPT-5.5 announcement gives one major official benchmark: 84.9% on GDPval for agents producing well-specified knowledge work across 44 occupations . The practical takeaway is narrower than model-launch hype: try Claude first for coding and tool-heavy agents, try GPT-5.5 for OpenAI-native knowledge-work agents, and benchmark both for design and deep research .

Quick verdict by use case

Use case	Best first trial	Evidence-backed reason
Coding	Claude Opus 4.7	Vellum reports Claude Opus 4.7 at 87.6% on SWE-bench Verified and 64.3% on SWE-bench Pro, while BenchLM ranks it #2 for coding and programming with an average score of 95.3 .
Tool-use agents	Claude Opus 4.7	Vellum reports Claude Opus 4.7 at 77.3% on MCP-Atlas; its direct OpenAI comparison is GPT-5.4 at 68.1%, not GPT-5.5 .
Knowledge-work agents	GPT-5.5	OpenAI reports GPT-5.5 at 84.9% on GDPval, which it describes as testing agents’ ability to produce well-specified knowledge work across 44 occupations .
Deep research	No direct winner	BenchLM lists Claude Opus 4.7 #1 in knowledge and understanding, but the cited GPT-5.5 source does not provide a shared deep-research benchmark; a BrowseComp signal in the source set concerns GPT-5.4, not GPT-5.5 .
Design and UX	No direct winner	The cited evidence emphasizes coding, tool use, knowledge work, context, vision and cyber posture rather than design-specific evaluations .
Context and vision	Claude Opus 4.7	LLM Stats reports a 1M-token context window, 3.3x higher-resolution vision and a new xhigh effort level for Claude Opus 4.7 .
Access	Depends on your stack	Anthropic says developers can use claude-opus-4-7 through the Claude API; an OpenAI developer-community announcement says GPT-5.5 is available in Codex and ChatGPT .

Why the comparison is uneven

Claude has the fuller benchmark trail in the cited material. BenchLM ranks Claude Opus 4.7 #2 overall on its provisional leaderboard with a 97/100 score, Vellum reports detailed software-engineering and MCP-Atlas results, and LLM Stats reports context and vision specifications . Anthropic’s official source in this set confirms that developers can use claude-opus-4-7 through the Claude API .

GPT-5.5 has a different evidence profile. OpenAI’s official announcement supports the GDPval score and cyber-safeguard claims, while the developer-community announcement supports availability in Codex and ChatGPT . In the cited OpenAI material, there is no directly comparable GPT-5.5 SWE-bench, design, vision or named deep-research benchmark to match the Claude-specific data .

That does not mean Claude is automatically better. It means Claude is easier to justify from the available public numbers for coding and tool use, while GPT-5.5 needs to be evaluated on the workflows where OpenAI has published its strongest signal: structured knowledge-work agents .

Coding: start with Claude, but test both on your own repo

For software engineering, Claude Opus 4.7 has the strongest documented case. Vellum reports 87.6% on SWE-bench Verified and 64.3% on SWE-bench Pro, and BenchLM ranks Claude Opus 4.7 #2 in coding and programming benchmarks with an average score of 95.3 .

The caveat is important: Vellum’s direct OpenAI comparison is against GPT-5.4, not GPT-5.5 . That makes Claude the better-supported first model to try for coding, but it does not prove Claude beats GPT-5.5 on every engineering task.

A practical coding evaluation should use real repository work, not generic prompts. Test both models on tasks such as:

• Fixing backlog issues with failing tests.
• Refactoring a complex module without changing behavior.
• Generating tests that catch known edge cases.
• Following your architecture and style constraints.
• Reading build logs, package documentation and CI output without inventing APIs.

Score the results by pass rate, number of review comments, time to accepted pull request, tool-call failures and any hallucinated dependencies.

Agents and tool use: Claude and GPT-5.5 show different strengths

Claude’s strongest agentic signal in the cited sources is tool use. Vellum reports Claude Opus 4.7 at 77.3% on MCP-Atlas, ahead of the GPT-5.4 comparison point at 68.1% . If your agent needs to call tools, inspect external state or coordinate MCP-style workflows, Claude has the clearer public benchmark trail.

GPT-5.5’s strongest official agent signal is GDPval. OpenAI says GDPval tests agents’ ability to produce well-specified knowledge work across 44 occupations and reports GPT-5.5 at 84.9% . That supports giving GPT-5.5 a serious trial for structured professional work, especially if the workflow already runs through ChatGPT or Codex .

The safest split is simple: use Claude as the first benchmark for tool-heavy agents, and use GPT-5.5 as a serious candidate for well-specified professional knowledge-work agents.

Deep research: promising signals, no clean winner

The cited evidence does not settle deep research. BenchLM ranks Claude Opus 4.7 #1 in knowledge and understanding, which supports Claude as a strong general knowledge model . But knowledge ranking is not the same as source-grounded research quality.

One secondary source says GPT-5.4 led Claude Opus 4.7 on BrowseComp web research by 10 points, but that claim is about GPT-5.4, not GPT-5.5 . OpenAI’s official GPT-5.5 source gives the GDPval result for well-specified occupational knowledge work, not a direct Claude-vs-GPT-5.5 deep-research benchmark .

If research quality matters, evaluate both models on the same assignments and grade for source retrieval, citation fidelity, contradiction handling, synthesis quality and refusal to invent unsupported claims.

Design and UX: do not pick a winner from these sources

There is no citation-backed design winner in the provided evidence. The Claude sources focus on coding, tool use, knowledge, context, vision and reasoning-oriented capabilities . The GPT-5.5 official source emphasizes GDPval, cyber safeguards and access rather than UI design, brand systems, product strategy or UX-specific benchmarks .

Design teams should run a practical task suite instead. Useful tests include turning a product requirement into a wireframe specification, critiquing a checkout flow, generating accessible design tokens, writing component documentation and producing alternative UX copy. Score outputs for specificity, accessibility, consistency, usability and whether the model invents constraints.

Context, vision, safety and cost signals

Claude has the more explicit context and vision data in the cited material. LLM Stats reports Claude Opus 4.7 with a 1M-token context window, 3.3x higher-resolution vision and a new xhigh effort level . The same source reports pricing at $5 per million input tokens and $25 per million output tokens, but that pricing comes from a secondary source and should be verified against current vendor pages before procurement .

GPT-5.5 has the clearer official cyber-safety statement in this source set. OpenAI says it is deploying safeguards for GPT-5.5’s level of cyber capability and expanding access to cyber-permissive models . That matters for teams evaluating security, cyber-defense or governed enterprise deployments.

Final recommendation

Choose Claude Opus 4.7 first if your priority is:

• Repository-scale coding, debugging, refactoring or test generation .
• Tool-use agents and MCP-style workflows .
• Long-context or vision-heavy tasks where the reported 1M-token context window and higher-resolution vision are relevant .

Choose GPT-5.5 first if your priority is:

• Workflows already centered on ChatGPT or Codex .
• GDPval-style professional knowledge work across occupations .
• Cyber-sensitive deployments where OpenAI’s stated safeguard posture is a key buying factor .

For everything else, especially design and deep research, run a side-by-side evaluation. The available evidence supports Claude as the first coding and tool-use trial, GPT-5.5 as a serious OpenAI-native knowledge-work trial, and custom testing for categories where the public benchmarks do not yet answer the question .