GPT-5.5 vs. Claude Opus 4.7: work execution or long-context agents?

Quick answer: how to choose

• Choose GPT-5.5 if your work starts with a loose brief. Bloomberg reported GPT-5.5 as a model built to handle tasks with limited instructions.^[1] That makes it a strong candidate for workflows where you want the model to infer the next steps, gather information, analyse it and turn it into something usable.
• Choose GPT-5.5 if the job mixes research, analysis, code and documents. OpenAI explicitly lists coding, online research, information analysis, document and spreadsheet creation, and cross-tool work as target uses.^[3]
• Choose Claude Opus 4.7 if the input is very long. Anthropic’s official page highlights a 1M-token context window.^[26]
• Choose Claude Opus 4.7 if you are building or supervising agents. Its task budgets beta gives Claude a rough token target for a full agentic loop, including thinking, tool calls, tool results and final output.^[13]
• Test both if the work is business-critical. Public positioning is useful, but your own prompts, codebase, documents, tests and cost limits are the real benchmark.

The decision in one table

Where GPT-5.5 looks strongest

GPT-5.5 is the more obvious candidate when you want a model to take a relatively open-ended request and turn it into a finished piece of work. Bloomberg’s framing matters here: GPT-5.5 was reported as a model designed to handle tasks with limited instructions.^[1]

That is a useful fit for everyday professional workflows where the task is not simply answer this question or fix this one function. It might involve researching a market, comparing sources, extracting the main issues, drafting a memo, producing a spreadsheet-style structure, writing code and explaining the result. OpenAI’s own system card places GPT-5.5 in exactly that kind of multi-step, tool-spanning work category.^[3]

In practice, GPT-5.5 is worth testing when the work looks like this:

• Turn a brief into a research plan, findings and a polished document.
• Move between web research, analysis and writing.
• Write or debug code, then produce a clear explanation for non-specialists.
• Create structured outputs such as tables, outlines, spreadsheet-ready data or project notes.
• Handle tasks where the user may not spell out every step in advance.

The key question is not just whether GPT-5.5 gives a good single answer. It is whether it keeps the work moving with fewer hand-holding prompts.

Where Claude Opus 4.7 looks strongest

Claude Opus 4.7 has a different centre of gravity. The most concrete headline is the 1M-token context window on Anthropic’s official page.^[26] For teams working with long specifications, large design documents, extended transcripts or multi-file code context, that can be a decisive feature.

The second major differentiator is task budgets. Anthropic says a task budget gives Claude a rough estimate of how many tokens to target for a full agentic loop, including thinking, tool calls, tool results and final output.^[13] The model sees a running countdown and uses it to prioritise work and finish the task gracefully as the budget is consumed.^[13]

That makes Claude Opus 4.7 especially relevant for workflows where the model is not just replying, but operating over time: reading a large context, deciding what to do next, using tools, processing results and trying to land the task within a budget. Anthropic also says Opus 4.7 is stronger across coding, vision and complex multi-step tasks, with better results across professional knowledge work.^[26]

Claude Opus 4.7 is therefore a natural model to test when the work looks like this:

• Review a large codebase or a long technical specification.
• Run multi-step debugging or implementation tasks.
• Give an agent a long-running assignment with tool use.
• Keep a large amount of context available in one session.
• Explore whether token-budget controls improve reliability and cost discipline in agent workflows.

Coding: which one should developers try first?

Both models make a serious coding pitch. GPT-5.5 includes code writing in OpenAI’s stated target use cases, and Bloomberg reported that Greg Brockman praised its coding ability.^[1][3] Claude Opus 4.7 is described by Anthropic as a model for coding and AI agents, with stronger performance across coding and complex multi-step tasks.^[26]

A practical way to split the decision is to look at what surrounds the code task:

• Pick GPT-5.5 first for code plus research plus explanation. If you want the model to investigate an issue, propose an approach, write code and produce clear documentation from a short brief, GPT-5.5’s positioning is a close match.^[1][3]
• Pick Claude Opus 4.7 first for large-context engineering work. If you need to load extensive project context and ask the model to perform multi-step changes or reviews, Claude’s 1M context window and task budgets are the stronger public signals.^[13][26]
• Run your own coding bake-off for production use. Compare the models on the same repository, same tests, same review criteria and same cost limits. Public descriptions do not replace your CI results, security checks or engineering standards.

Pricing: do not stop at the token sticker price

On listed token prices, the two models look close on input and different on output. OpenAI’s pricing page lists GPT-5.5 input at $5.00 per 1M tokens and cached input at $0.50 per 1M tokens.^[37] The New Stack reported GPT-5.5 output at $30 per 1M tokens and said the API version has a 1M-token context window.^[46]

For Claude Opus 4.7, CloudPrice and OpenRouter list $5 per 1M input tokens and $25 per 1M output tokens.^[25][34] On that narrow comparison, Claude’s listed output price appears lower.^[25][34][46]

But real cost is rarely just input multiplied by output. OpenAI’s API pricing documentation includes separate tool-related pricing for items such as web search, containers and file search.^[36] In agent workflows, the number of loops matters too: tool calls, tool results, intermediate reasoning, final answers and failed retries can all affect the total. Claude Opus 4.7’s task budgets are explicitly designed around the full agentic loop, not only the final answer.^[13]

A fair cost test should track:

1. Input tokens.
2. Output tokens.
3. Cached input usage.
4. Search, file-processing and container charges where relevant.^[36][37]
5. Number of tool calls.
6. Number of agent iterations.
7. Failed runs and reruns.
8. Whether the model completes the task without extra human prompting.

One caution: neither model is a universal winner

The product positioning is not identical. OpenAI is presenting GPT-5.5 as a model for complex professional work across tools.^[3] Anthropic is presenting Claude Opus 4.7 as a long-context hybrid reasoning model for coding and agents.^[26] Those are overlapping markets, but not the same message.

It is also worth being precise about Claude’s place in Anthropic’s own lineup. CNBC reported that Anthropic positioned Claude Opus 4.7 as improved over past models, but less broadly capable than Claude Mythos Preview.^[16] In other words, even a premium Opus model should not automatically be treated as Anthropic’s best option for every possible use case.

A simple evaluation plan before you commit

Before choosing one model for a team or product, run a small but disciplined comparison:

1. Use real work, not toy prompts. Pick tasks such as bug fixes, code reviews, long document summaries, research reports or agent-based workflows.
2. Keep the instructions identical. Give both models the same files, tools, limits and success criteria.
3. Score the deliverable, not the vibe. Look at correctness, missed requirements, clarity, reproducibility, test results and whether the model needed rescue prompts.
4. Measure total cost. Include token usage, cached input, search, file search, containers and reruns.^[36][37]
5. Test long-context cases separately. The New Stack reported that GPT-5.5 has a 1M-token context window in the API but 400,000 tokens in Codex, so the product surface you use may matter.^[46]
6. Test agent loops explicitly. For Claude Opus 4.7, evaluate whether task budgets help the model complete long-running work within the intended token envelope.^[13]

Bottom line

GPT-5.5 is the stronger first bet when you want a general work-execution model: something that can take limited instructions and move through research, analysis, code, documents, spreadsheets and tools.^[1][3]

Claude Opus 4.7 is the stronger first bet when the work depends on long context, multi-step coding, agent execution and explicit token-budget control across an agentic loop.^[13][26]

For most teams, the right answer is not to crown a universal champion. Treat GPT-5.5 as the model to test for broad professional task execution, and Claude Opus 4.7 as the model to test for long-context and agent-heavy work. Then let your own tasks, costs and quality bar decide.