Claude Opus 5 Medium vs GPT-5.6 Sol High: Which Model Should Developers Choose?

GPT-5.6 Sol (high) is the stronger default for coding throughput, while Claude Opus 5 (Adaptive Reasoning, Medium Effort) is cheaper and slightly ahead on the intelligence index.

The comparison labels describe settings applied to two official models. Anthropic documents claude-opus-5 as the API model and uses effort: medium to control reasoning depth in the Claude model overview and Opus 5 update notes. OpenAI documents gpt-5.6-sol as the fixed model ID and uses reasoning.effort: high in its GPT-5.6 Sol model page and reasoning guide.

That distinction matters for reproducibility. Neither claude-opus-5-medium nor gpt-5.6-sol-high should be treated as a separate official API model. The practical choice is between two model IDs, two reasoning configurations, and two different cost and tool-use profiles.

Anthropic positions Claude for complex agentic coding, long-running work, document tasks, and visual understanding. OpenAI positions GPT-5.6 Sol for complex professional work, reasoning, coding, and tool-rich applications. Those official descriptions support different deployment strengths, but they do not replace a task-specific pilot.

Data provided by https://artificialanalysis.ai/. See the Artificial Analysis snapshot for the comparison data.

Claude Opus 5 (Adaptive Reasoning, Medium Effort) wins blended cost, while GPT-5.6 Sol (high) leads coding and output speed.

The data snapshot lists Claude Opus 5 with release date 2026-07-24 and GPT-5.6 Sol with release date 2026-07-09. Anthropic still lists Claude Opus 5 in its current model overview, while OpenAI lists GPT-5.6 Sol in its current model directory. The materials therefore show an availability comparison, not a deprecated-model migration problem.

Decision area	Claude Opus 5 Medium	GPT-5.6 Sol High	What it means
Intelligence Index	56.3	55.9	Claude has the slight measured lead
Coding Index	74.3	77.2	GPT has the stronger coding result
Blended price per 1M tokens	$10	$11.25	Claude costs less in the supplied mix
Input price per 1M tokens	$5	$5	Neither has an input-price advantage
Output price per 1M tokens	$25	$30	Claude is cheaper for generated output
Median output speed	54.838	73.648	GPT produces tokens faster
Latency	0.3 seconds	0.3 seconds	The supplied latency result is tied

The vendor narratives are broader than the comparable snapshot. Anthropic cites several evaluations in its official announcement, while OpenAI publishes a separate benchmark set in its GPT-5.6 announcement. The research materials do not establish that those vendor results used the exact medium and high settings here. Treat the snapshot as the cleaner basis for this comparison, then validate the workflow that matters to your product.

GPT-5.6 Sol (high) has the stronger measured coding profile, while Claude Opus 5 (Adaptive Reasoning, Medium Effort) remains nearly tied on general intelligence.

The coding difference is visible in the snapshot: GPT-5.6 Sol scores 77.2, compared with Claude Opus 5 at 74.3. For developers, that gap suggests a higher chance of useful first-pass implementation on coding-heavy evaluations. It does not prove that GPT will win every repository task. Architecture familiarity, tool definitions, test quality, and prompt constraints can reverse a single-task result.

The larger operational difference is output throughput. GPT-5.6 Sol records 73.648 median output tokens per second, while Claude Opus 5 records 54.838. Faster generation can reduce perceived waiting during agent loops, especially when the model must explain a plan, emit patches, and report test results. The supplied latency result is 0.3 seconds for each model, so the data does not identify a first-response advantage. Output speed also does not equal end-to-end completion speed. Tool execution, reasoning tokens, retries, and the number of agent turns can dominate wall-clock time.

Community evidence is split. One Claude user report describes hours of sustained editing, testing, and rework. Other Claude feedback describes over-planning and unnecessary testing. Speed reports also conflict, with one slow-task report and one faster-task report.

GPT feedback has the same uncertainty. The Codex discussion reports slow small changes and over-engineering. A Hacker News report describes irrelevant investigation paths and defensive code, but says lower reasoning effort improved the user’s experience. These reports are useful risk signals, not controlled evidence. No supplied source establishes average success rate, average wall-clock completion, or reliability for the exact high configuration.

Claude Opus 5 (Adaptive Reasoning, Medium Effort) is the cheaper choice in the blended snapshot and for output-heavy API traffic.

The supplied blended price is $10 per 1M tokens for Claude Opus 5 and $11.25 for GPT-5.6 Sol. Input pricing is tied at $5 per 1M tokens. Output pricing creates the clearer difference: Claude is $25 per 1M output tokens, while GPT is $30. That makes Claude the direct price choice for workflows that generate long explanations, patches, reviews, or documents.

The cost conclusion can change at the workflow level. A lower token price becomes less attractive if the model needs extra turns, repeats failed tool calls, or creates code that requires more human correction. Claude users report persistence and repeated rework in long tasks, but other reports describe unnecessary planning and testing. GPT users report over-engineering and long waits in some tasks. The evidence does not show which model produces the lower total cost per accepted change.

Reasoning configuration also affects budgeting. OpenAI states in its reasoning guide that reasoning tokens consume the output budget and can leave a response incomplete when max_output_tokens is too low. Anthropic describes a similar constraint in its Opus 5 update notes, where thinking tokens share the max_tokens ceiling. The same nominal output price can therefore produce different effective costs if one integration regularly needs a larger generation budget.

Caching and service tiers add another variable. Anthropic documents caching options in its pricing page, and OpenAI documents caching and service tiers in its API pricing page. Use the blended figure as a starting point, then measure cost per successful task.

GPT-5.6 Sol (high) is the default pick for coding agents, while Claude Opus 5 (Adaptive Reasoning, Medium Effort) fits cost-sensitive, persistent workflows.

Choose GPT-5.6 Sol (high) when the product depends on coding quality, fast visible generation, and a broad tool surface. OpenAI documents structured outputs, function calling, file search, web search, code execution, hosted shell, computer use, MCP, and tool search on the model page. The coding index of 77.2 and output speed of 73.648 reinforce that choice for repository agents, code transformation, and interactive development.

Choose Claude Opus 5 with medium effort when output cost matters, the task involves long multi-file work, or the application benefits from Anthropic’s documented emphasis on visual input, multilingual work, documents, and multi-agent collaboration. Claude’s blended price is $10, its output price is $25, and its intelligence index is 56.3. A community report also describes sustained work across several hours, though that experience is not a controlled benchmark.

Apply guardrails to either choice. Claude feedback reports ignored instructions, architecture drift, and unrequested changes in separate instruction-following feedback and scope-change feedback. GPT feedback reports similar risks through over-engineering and irrelevant investigation. Build explicit tool permissions, repository checks, test gates, and change summaries into the harness.

The settings also need explicit implementation. Call Claude with claude-opus-5 and medium effort. Call GPT with gpt-5.6-sol and high reasoning effort. Do not infer a winner on reliability, average completion cost, or human correction time from the supplied data. A limited rewrite-task test shows why one task cannot represent general coding performance.

Claude Opus 5 (Adaptive Reasoning, Medium Effort) and GPT-5.6 Sol (high) both need an application-level pilot because end-to-end reliability remains unresolved.

The supplied evidence is strong enough to guide an initial choice on coding score, output speed, and token price. It is not strong enough to answer which model finishes a complete repository task with fewer retries, fewer unwanted changes, or less human review. Official documentation describes reasoning controls and tool capabilities, while community posts describe conflicting experiences. Neither source type supplies a unified success-rate study for these exact configurations.

A useful pilot should keep the repository, task wording, tools, approval rules, and acceptance tests constant. Record accepted changes, failed tool calls, extra turns, incomplete responses, review corrections, and total token usage. Compare the full task outcome rather than judging a single impressive answer. The initial recommendation is GPT for coding throughput and Claude for lower output cost, but the final decision should follow the economics and failure tolerance of the deployed workflow.