Claude Opus 4.7 vs GPT-5.4

What You'll Learn

• How the two official model specifications differ
• Which pricing and context figures are documented
• How reasoning effort, tools, and vision affect selection
• How to run a fair evaluation before switching models

What this Claude versus GPT comparison can prove

A model comparison can establish documented specifications, supported endpoints, listed prices, release dates, and results under named evaluation conditions. It cannot establish that one model is best for every prompt. Output quality depends on the task, prompt design, tool access, context, sampling, effort setting, and human review.

Anthropic announced Claude Opus 4.7 on April 16, 2026 as a generally available model. OpenAI's model documentation lists GPT-5.4 with the default snapshot gpt-5.4-2026-03-05. These are different release records, so a fair comparison must retain the exact model IDs and dates.

The DeepSeek migration guide explains why model labels, aliases, and release stages should not be mixed. The same rule applies here. Compare pinned IDs and settings rather than brand-level impressions.

Claude Opus 4.7: the documented strengths

Anthropic says Opus 4.7 improves software engineering, instruction following, real-world work, vision, and long-running tasks compared with Opus 4.6. The company describes it as its most powerful generally available model at launch, while noting that Claude Mythos Preview remained more broadly capable in some areas and had limited access.

Anthropic also says Opus 4.7 can process images up to 2,576 pixels on the long edge, or about 3.75 megapixels. This matters for dense screenshots, technical diagrams, and visual review. It does not mean every image task will be better. Image detail, prompt clarity, and output requirements still affect the result.

The benchmark methodology guide is useful when reading these claims. A provider announcement can be a primary source for what the company tested, but its result still needs the dataset, harness, and comparison baseline.

GPT-5.4: the documented strengths

OpenAI's GPT-5.4 documentation describes it as a frontier model for complex professional work. The page lists text and image input, text output, a 1,050,000-token context window, and 128,000 maximum output tokens. It supports reasoning effort values of none, low, medium, high, and xhigh, with none as the default.

OpenAI lists Chat Completions, Responses, and Batch as supported endpoints. It also lists structured outputs, function calling, file search, file uploads, image input, web search, and prompt caching. This makes GPT-5.4 a broad option for applications that combine reasoning with hosted tools and structured responses.

OpenAI's March 5 release notes say GPT-5.4 Thinking brings together advances in reasoning, coding, agentic workflows, tools, software environments, spreadsheets, presentations, and documents. The same notes describe an upfront plan in ChatGPT, improved deep web research, longer context handling, and better context-window management.

See the official GPT-5.4 model documentation for current limits, pricing, endpoints, and supported features.

Context window and output limits

Both models target long professional workflows, but the provider documentation expresses their limits differently. OpenAI lists a 1,050,000-token context window and 128,000 maximum output for GPT-5.4. Anthropic's model overview lists a 1M-token context window and 128k maximum output for Opus 4.7.

These figures are not a guarantee that a long prompt will produce a correct answer. Retrieval quality, prompt ordering, tool results, and the model's ability to locate relevant evidence can matter more than the headline limit. Test long inputs with the actual document mix your application sends.

The long-document token guide covers a practical risk. A large context window does not remove the need to measure truncation, output budgets, and incomplete responses.

Pricing and token economics

Anthropic lists Opus 4.7 at $5 per million input tokens and $25 per million output tokens, the same price as Opus 4.6. OpenAI lists GPT-5.4 at $2.50 per million input tokens, $0.25 per million cached input tokens, and $15 per million output tokens.

At list price, GPT-5.4 has the lower input and output rates in these published specifications. That does not automatically make it cheaper in production. A model that needs more retries, longer outputs, or extra tool calls can cost more for the same completed task. Measure cost per successful task rather than cost per token alone.

The coding-agent cost analysis explains why retries, tool errors, and human corrections belong in the operating-cost calculation.

Reasoning and effort controls

Anthropic says Opus 4.7 introduces xhigh effort between high and max. It also launched task budgets in public beta on the Claude Platform API. These controls let developers trade reasoning depth and token use against speed, but the exact behavior should be measured on the selected model and workload.

OpenAI's GPT-5.4 documentation lists none, low, medium, high, and xhigh reasoning effort, with none as the default. The option names are similar enough to invite an unfair comparison, but they are provider-specific controls. Claude high and GPT high do not represent the same amount of internal work.

The reasoning model comparison shows how to separate model identity from reasoning budget. Run both models at matched task objectives and record the setting explicitly.

Tool use and agent workflows

Opus 4.7 is positioned for complex, long-running tasks, and Anthropic says it improves software engineering, tool use, instruction following, and self-verification. Its announcement also describes task budgets and new effort control for agentic work.

GPT-5.4 supports function calling, web search, file search, code-related tools, and the Responses API according to OpenAI's documentation. OpenAI's release notes position it for software environments, documents, spreadsheets, presentations, and agentic workflows.

Tool compatibility is a systems question, not only a model question. Compare tool schemas, permission behavior, retries, parallel calls, error recovery, and audit logs. The edge inference guide explains why provider calls, application controls, and deployment limits should be evaluated together.

Vision and document work

Anthropic reports higher-resolution image support for Opus 4.7 and points to uses such as dense screenshots and technical diagrams. OpenAI lists image input for GPT-5.4 and describes document, spreadsheet, and presentation workflows in its release notes.

For document-heavy work, test extraction accuracy, table handling, page references, chart interpretation, and refusal behavior on missing data. Do not infer vision quality from a text-only benchmark. Keep the same files, instructions, output schema, and review rubric across both models.

What Anthropic's reported evaluations show

Anthropic's announcement includes provider-reported and partner-reported results. One partner reported a 13% lift on a 93-task coding benchmark compared with Opus 4.6. The announcement also cites BigLaw Bench at 90.9% at high effort, CursorBench above 70% compared with 58% for Opus 4.6, and a Finance Agent module score of 0.813 compared with 0.767.

These figures are useful signals about the areas Anthropic tested, but they are not a head-to-head Opus 4.7 versus GPT-5.4 verdict. The announcement says its charts compared against the best reported model versions available through API, and some results are internal or partner evaluations. Treat them as attributed evidence with scope limits.

Which model should a developer choose

Choose Claude Opus 4.7 when your priority is difficult software engineering, long-running agent work, high-resolution image review, or Anthropic's effort and task-budget controls. Confirm the API surface, token behavior, and tool integration on your workload before committing.

Choose GPT-5.4 when you need OpenAI's Responses or Chat Completions ecosystem, structured outputs, function calling, web search, documents, or the lower published token rates. Confirm that the selected reasoning effort and output budget deliver the required accuracy at acceptable latency.

For a mixed application, route tasks by type instead of forcing one model to handle every request. Use a cheaper model for routine transformations, a stronger model for difficult cases, and a fixed evaluator to decide whether the result is acceptable.

How to run a fair head-to-head test

Build a task set from real work rather than promotional examples. Include easy, normal, and difficult cases, plus failures that matter to users. Freeze the prompt, source files, tools, model IDs, effort values, output schema, timeout, retry policy, and reviewer rubric.

Measure task success, factual errors, code-test results, tool errors, latency, output tokens, input tokens, retry count, and cost per accepted deliverable. Run enough repetitions to identify variance. Record the date because provider snapshots, defaults, prices, and limits can change.

The AI tool comparison guide explains why a recommendation should follow observed use-case evidence instead of a generic winner label.

Conclusion: match the model to the work

Claude Opus 4.7 and GPT-5.4 are both documented as high-capability models for demanding work, but their strongest published details differ. Opus 4.7 emphasizes software engineering, long-running tasks, high-resolution vision, and effort control. GPT-5.4 emphasizes professional reasoning, tools, documents, a 1,050,000-token context, broad endpoint support, and lower listed token rates. The correct choice depends on measured success, cost, latency, and integration fit.