Claude Opus 5 (Adaptive Reasoning, High Effort) vs GPT-5.5 (xhigh): The Ultimate Performance & Pricing Comparison

Claude Opus 5 is the better overall starting point for developers who want the strongest shared-snapshot scores at the lower blended price.

Claude Opus 5 leads the supplied comparison on both measured indexes. It scores 58.9 on the Artificial Analysis intelligence index and 76.5 on its coding index. GPT-5.5 scores 54.8 and 74.9. Claude also has the lower blended price, $10 versus $11.25 per 1M blended tokens. Data provided by https://artificialanalysis.ai/.

The practical verdict is narrower than a universal model ranking. The snapshot reports Claude's median output speed at 54.599 tokens per second. GPT-5.5 has no corresponding value. Both models report latency at 0.3 seconds. End-to-end speed therefore remains partly unresolved.

Anthropic positions Claude Opus 5 for complex agentic coding and enterprise work in its official announcement. OpenAI positions GPT-5.5 for complex professional work in its model guidance. These roles overlap, but the buying logic differs. Claude emphasizes autonomous depth. GPT-5.5 emphasizes a broad execution stack. Developers should choose according to orchestration, review, and failure costs.

Claude Opus 5 wins the measured comparison, while GPT-5.5 offers the broader documented OpenAI-native execution surface.

Claude's official model overview documents text and image input, text output, multilingual ability, vision, adaptive reasoning, and availability across several cloud platforms. Its stable API identity is also documented in Anthropic's model versioning guide.

GPT-5.5's model documentation describes Responses API, Chat Completions API, Batch API, streaming, structured output, function calling, file search, web search, image generation, Code Interpreter, Hosted Shell, Computer Use, Skills, and MCP support. That surface can reduce integration work for teams already committed to OpenAI infrastructure.

The comparison therefore has two layers. Claude wins the supplied shared metrics. GPT-5.5 may still win as a platform choice when the surrounding application depends on OpenAI-specific tools, APIs, or execution patterns.

Claude Opus 5 is the safer performance bet for coding and general intelligence, but the available speed evidence does not prove faster user-visible completion.

The coding index favors Claude Opus 5 at 76.5 versus 74.9 for GPT-5.5. That lead supports Claude as the first candidate for repository work, code generation, and complex implementation tasks. It does not equal a coding success rate. The index cannot tell a team how often either model passes its own tests, preserves architecture, or avoids unnecessary edits.

The intelligence index shows a wider visible separation, with Claude at 58.9 and GPT-5.5 at 54.8. That difference may matter more for research, planning, multi-step decisions, and agent supervision than for short code completions. Developers should still validate the exact task distribution. A general index cannot establish production quality for a specialized domain.

The speed evidence is incomplete. Claude reports a median output rate of 54.599 tokens per second. GPT-5.5 has no matching value in the supplied snapshot. Both models report latency at 0.3 seconds. Median output speed measures generation after a response starts. It does not capture tool waits, queueing, retries, approval pauses, or time spent correcting a bad plan.

The official benchmark narratives also resist a simple ranking. Anthropic claims leading results across several evaluations in its Claude Opus 5 announcement, but the announcement does not provide every underlying score. OpenAI publishes a separate benchmark set in its GPT-5.5 announcement, including results from some internal evaluations. Those lists are not a controlled head-to-head test.

Community reports point to different control problems. Developers in a Claude discussion describe verbosity, slow progress, overthinking, and broad edits without enough confirmation. The original poster had not used Opus 5, and commenters did not share a reproducible method. A Hacker News discussion repeats an official visual reconstruction case, so it is not an independent replication.

GPT-5.5 receives positive architecture and debugging feedback in one developer discussion. Another community discussion reports concise or abstract answers and fragile code without strong project constraints. Neither set of anecdotes establishes a stable user-wide ranking.

Claude Opus 5 is cheaper for the blended workload and output-heavy usage, while GPT-5.5 can still win inside an OpenAI-specific workflow.

The snapshot's 3:1 blended measure puts Claude Opus 5 at $10 and GPT-5.5 at $11.25 per 1M tokens. Input pricing is tied at $5. The main unit-price separation comes from output, priced at $25 for Claude and $30 for GPT-5.5. That makes Claude the clear starting point for workloads with substantial generated text or reasoning output.

The advantage narrows in prompt-heavy applications because both models have the same listed input price. It becomes more important when the application produces long answers, detailed patches, or repeated agent traces. The chart shows unit economics, not the cost of reaching a successful result.

A cheaper model can cost more per completed task if it needs extra retries, larger repair prompts, or more human intervention. A higher-priced model can be cheaper operationally if it finishes a complex workflow with fewer failed tool calls. The supplied data does not report retry counts, tool-call success, human review time, or cost per completed task. That evidence gap matters more than a small unit-price difference for high-value workflows.

Reasoning settings can also change the bill. Anthropic explains in its Opus 5 update and thinking documentation that thinking consumes output capacity and can increase delay and cost. Anthropic's effort guide also describes effort as a behavior signal, not a strict token budget.

OpenAI similarly warns that xhigh reasoning can add delay and cost, and can produce unproductive search under weak stopping rules in its GPT-5.5 guidance. Teams should compare the exact serving mode they will deploy. Anthropic documents caching options in its pricing guide, while OpenAI documents Standard, Batch, Flex, Fast, and long-context pricing in its API pricing guide.

Claude Opus 5 is the default pick for autonomous coding and long-running knowledge work, while GPT-5.5 fits tool-rich OpenAI orchestration.

Choose Claude Opus 5 when the model must sustain a complex task, inspect a large working context, make coordinated code changes, or operate across enterprise cloud options. Anthropic explicitly emphasizes agentic coding, enterprise work, visual understanding, research, documents, and multi-agent collaboration in its product announcement. Its current model overview lists the model as available. The fixed model identity described in Anthropic's versioning documentation also favors reproducible deployments.

Choose GPT-5.5 when your application already depends on OpenAI's execution layer. Its documented support for structured outputs, file search, web search, Code Interpreter, Hosted Shell, Computer Use, Skills, and MCP can simplify a tool-heavy product. OpenAI's current model catalogue provides the broader product context, while the dedicated GPT-5.5 documentation defines the model's direct API surface.

Both choices require guardrails. Anthropic documents tool-call behavior changes, adaptive thinking requirements, and migration constraints in the Opus 5 update. OpenAI recommends explicit reuse rules, testing expectations, acceptance criteria, delegation rules, and stopping conditions in its GPT-5.5 guide. Those recommendations show that strong models still need strong orchestration.

Version risk differs as well. Claude's overview currently lists the model as available. GPT-5.5 remains documented and priced, but OpenAI's broader catalogue may shift attention toward newer models. Neither supplied brief contains a formal deprecation notice. A Claude status incident also shows that service events should be evaluated separately from model capability.

Use Claude Opus 5 first for autonomous repository work, deep reasoning, and output-sensitive workloads. Use GPT-5.5 first for OpenAI-native tools, structured application workflows, and teams that value a unified execution surface. Pilot both when failed tasks carry high business or operational cost.

GPT-5.5 is the better choice only when its integration advantages outweigh the evidence gap on measured throughput and task-level cost.

The supplied comparison supports a directional decision, not a universal winner. Claude Opus 5 has higher shared intelligence and coding scores. It also has the lower blended price and the only reported median output-speed value. GPT-5.5 has equal reported latency and a richer documented OpenAI tool surface.

The evidence remains insufficient for several questions developers normally ask. The briefs do not provide comparable coding success rates, tool-call error rates, retry counts, end-to-end completion time, or cost per successful workflow. They also do not provide a reliable distribution of real user experiences. Community reports for both models are useful signals, but they lack consistent test sets and reproducible conditions.

A fair pilot should use the same prompts, repository state, tool permissions, stopping rules, and acceptance tests for both models. Record successful completion, unwanted edits, repair turns, generated output, human review time, and total spend. Test autonomous tasks separately from interactive tasks. Test short responses separately from long-running workflows.

Treat official benchmark claims as evidence about advertised capability. Treat the shared Artificial Analysis snapshot as the strongest direct comparison supplied here. Treat community anecdotes as hypotheses for what your evaluation should inspect.