Claude Opus 4.7 vs GPT-5.5: which model should you choose?

That makes the practical question less about which model is universally better and more about where you plan to use it: API deployment, long-context document work, ChatGPT’s built-in tools, or benchmark-led model selection.

Quick verdict

• For API deployment, cost planning and long-context workflows, Claude Opus 4.7 is currently easier to scope. Claude API docs explicitly refer to Opus 4.7, the full 1M-token context window, and the 1.1x multiplier for US-only inference.^[13]
• For work inside ChatGPT, GPT-5.5 has the clearer product fit. OpenAI says GPT-5.5 Thinking supports every current ChatGPT tool, subject to the GPT-5.5 Pro exception.^[5]
• On benchmarks, OpenAI’s published numbers favor GPT-5.5, but they should not be treated as an independent final ruling. OpenAI lists GPT-5.5 at 84.9% on GDPval, while Claude Opus 4.7 has separate third-party coding benchmark listings; these are different source types and should be followed by your own evaluation.^[6][16]

Side-by-side comparison

API pricing and cost planning

For developers, platform teams and procurement buyers, the most important questions are usually mundane: how tokens are charged, whether regional routing changes the bill, and whether the context window is large enough for the intended workload.

Claude Opus 4.7 is clearer on those points. The Claude API pricing documentation says that for Claude Opus 4.7, Claude Opus 4.6 and newer models, specifying US-only inference through inference_geo adds a 1.1x multiplier to all token-pricing categories, including input tokens, output tokens, cache writes and cache reads.^[13] The same documentation says Claude Mythos Preview, Opus 4.7, Opus 4.6 and Sonnet 4.6 include the full 1M-token context window at standard pricing.^[13]

For rough market checking, CloudPrice lists Claude Opus 4.7 as starting at $5.00 per 1M input tokens and $25.00 per 1M output tokens, with a 1.0M context window and up to 128K output tokens.^[18] Because CloudPrice is a third-party aggregator, buyers should treat it as a planning reference and confirm final terms with Anthropic or the provider they actually use.^[13][18]

GPT-5.5 is less clear from the cited API material. OpenAI’s launch page and Help Center support GPT-5.5 as a product and ChatGPT model, but the available OpenAI API/pricing sources do not clearly show GPT-5.5 token pricing.^{[1][2][3][5][6]} Also, do not copy GPT-5 API figures onto GPT-5.5 by assumption: the OpenAI GPT-5 page lists 400K context length, 128K max output tokens and per-token pricing for GPT-5, not GPT-5.5.^[9]

Long context: Claude has the cleaner public spec

Long context matters when you want to load a large codebase, a bundle of contracts, research papers, support-history exports or a multi-step agent trace. It affects prompt strategy, retrieval design, latency, cost and failure modes.

On the evidence available here, Claude Opus 4.7 has the most direct long-context documentation: Claude API docs state that Opus 4.7 includes the full 1M-token context window at standard pricing.^[13] CloudPrice also lists a 1.0M context window and up to 128K output tokens, though that output figure should be verified with the official provider before production use.^[13][18]

For GPT-5.5, the launch and Help Center sources cover product positioning, benchmarks and ChatGPT tool support, but they do not provide an equally clear API context/output specification in the material cited here.^[5][6] If long-context deployment is the deciding factor, Claude Opus 4.7 is easier to design around from public documentation.^[13]

ChatGPT workflows: GPT-5.5 is the better-documented fit

The picture changes if you are not building through an API. Many users care less about model IDs and more about whether the model can use the tools already available inside ChatGPT for research, file work, analysis and multi-step tasks.

Here GPT-5.5 has the clearer support statement. OpenAI’s Help Center says GPT-5.3 Instant and GPT-5.5 Thinking support every tool available in ChatGPT, subject to the GPT-5.5 Pro exception noted there.^[5]

Claude Opus 4.7 has product, API and platform-listing evidence, including Anthropic, Cloudflare Docs and OpenRouter, but those sources do not provide an equivalent statement about ChatGPT-style built-in tool support.^{[12][13][14][15]} If your daily workflow is already anchored in ChatGPT, GPT-5.5 belongs near the top of the shortlist.^[5]

Benchmarks: useful, but read the fine print

OpenAI’s GPT-5.5 launch page lists several comparisons against Claude Opus 4.7. These numbers should be understood as OpenAI-published results, not an independent third-party ruling.^[6]

OpenAI also says GPT-5.5 shows a clear improvement over GPT-5.4 on GeneBench, an evaluation focused on multi-stage scientific data analysis in genetics and quantitative biology.^[6]

Claude Opus 4.7 has its own benchmark signals. WaveSpeed lists Claude Opus 4.7 at 64.3% on SWE-bench Pro and 70% on CursorBench, and says it delivers three times more production tasks resolved.^[16] Those figures may be relevant for coding-agent screening, but they come from a different platform and should not be merged with OpenAI’s benchmark table as if they were one neutral ranking.^[6][16]

Which one should you try first?

API buyers and platform engineering teams

Start with Claude Opus 4.7 if you need to build a defensible estimate quickly. Its public API documentation is clearer on the 1M-token context window, token-pricing categories and the US-only inference multiplier.^[13] That makes it easier to discuss budget, routing, long-context design and procurement risk.

ChatGPT power users and knowledge workers

Start with GPT-5.5 if your work happens inside ChatGPT. The strongest cited evidence is that GPT-5.5 Thinking supports the current ChatGPT toolset, subject to the GPT-5.5 Pro exception.^[5] You should still confirm plan, region and account-level availability before standardizing a workflow.

Coding agents and engineering automation

Test both. OpenAI’s Terminal-Bench, Toolathlon and CyberGym numbers favor GPT-5.5, while WaveSpeed lists Claude Opus 4.7 coding benchmarks such as SWE-bench Pro and CursorBench.^[6][16] For bug fixing, repository migration, CI/CD automation or autonomous coding agents, use your own repositories, test suites, latency targets, failure analysis and human-review cost.

Long documents, large repositories and research packs

Claude Opus 4.7 has the clearer published specification. Claude API docs state that it includes the full 1M-token context window at standard pricing.^[13] CloudPrice’s third-party listing also shows a 1.0M context window and up to 128K output tokens, but production teams should verify provider limits directly.^[13][18]

Pre-adoption checklist

• Confirm the model ID and provider. OpenRouter lists Claude Opus 4.7 as anthropic/claude-opus-4.7; for GPT-5.5, confirm the official model ID, availability and pricing in the OpenAI API or ChatGPT product layer you actually use.^{[1][2][3][15]}
• Do not assume GPT-5 specs equal GPT-5.5 specs. The OpenAI GPT-5 page’s 400K context, 128K max output and token-pricing figures are labeled for GPT-5, not GPT-5.5.^[9]
• Account for routing and billing modifiers. Claude API docs say US-only inference for Opus 4.7 and related models applies a 1.1x multiplier across all token-pricing categories.^[13]
• Run your own evaluation. Vendor benchmarks and third-party model pages are useful for triage, but production selection should compare task success, cost, latency, tool-call reliability, long-context accuracy and human-review effort on your own workload.^[6][16]

Bottom line

Claude Opus 4.7 is the easier model to plan around if your priority is API deployment, transparent long-context documentation and budgetable 1M-context workflows. GPT-5.5 is the more directly documented choice if you live in ChatGPT and want tool-enabled knowledge work inside OpenAI’s product environment.^[5][13]

Neither should be declared the universal winner from the cited material alone. Use Claude Opus 4.7 as the first stop for API, context and cost design; use GPT-5.5 as the first stop for ChatGPT tool workflows; and use your own evaluation set, not a single benchmark table, for final model selection.^{[5][6][13][16]}