Claude Opus 5 (Adaptive Reasoning, Medium Effort) vs GPT-4o (Nov '24): The Ultimate Performance & Pricing Comparison

Claude Opus 5 is the stronger documented choice for demanding developer workflows, while GPT-4o is cheaper but difficult to validate as a current production target.

The supplied data gives Claude Opus 5 an Artificial Analysis Intelligence Index of 56.3, compared with 11.2 for GPT-4o. It also records Claude Opus 5 at 54.838 median output tokens per second, while GPT-4o has no supplied output-speed value. Both models show 0.3 seconds of latency in the data brief.

The comparison has an important asymmetry. Anthropic documents Claude Opus 5 as a current model with the stable API identifier claude-opus-5, while the supplied OpenAI model directory does not list gpt-4o or GPT-4o (Nov '24) (Anthropic model overview, OpenAI Models). That does not prove GPT-4o is unavailable. It means the supplied evidence cannot confirm its current lifecycle status, stable alias, or recommended integration path.

For a new application, Claude Opus 5 offers a clearer technical contract. GPT-4o remains attractive when price is the dominant constraint, but its version-specific documentation gap should be treated as an integration risk rather than ignored.

Claude Opus 5 wins on the supplied intelligence evidence and documented agentic positioning, while GPT-4o wins on reported blended-token price.

Claude Opus 5 costs $10 per 1M blended tokens in the data brief, compared with $4.375 for GPT-4o. Its input price is $5 per 1M tokens and its output price is $25 per 1M tokens. GPT-4o is listed at $2.5 for input and $10 for output. The price gap matters most for applications that generate long plans, code patches, test output, or explanations.

Anthropic explicitly positions Claude Opus 5 for complex agentic coding, multi-file development, code review, long-context work, visual understanding, office documents, and multi-agent collaboration (Claude Opus 5 announcement, Opus 5 update notes). The supplied OpenAI material provides only general current-model documentation. It does not provide a GPT-4o-specific capability statement, benchmark result, context window, output limit, release note, or stable alias (OpenAI Models).

The data supports a capability-versus-cost decision, not a universal winner. Claude Opus 5 is the safer selection for complex reasoning and coding workloads. GPT-4o is the economical selection when the task is well-bounded and the deployment team can independently verify access, limits, and behavior.

The coding comparison is incomplete. Claude Opus 5 has a coding index of 74.3, but the data brief provides no comparable GPT-4o coding index. GPT-4o has a math index of 6, but the data brief provides no comparable Claude Opus 5 math index. These missing cells prevent a complete domain-by-domain ranking.

Claude Opus 5 has the stronger measured intelligence result, but the supplied benchmark evidence cannot establish a complete coding or math ranking.

The Intelligence Index is 56.3 for Claude Opus 5 and 11.2 for GPT-4o. That gap suggests a meaningful difference on the measured intelligence workload, especially when a task requires maintaining constraints across several reasoning steps. It does not automatically predict equal gains on every codebase, language, framework, or tool loop.

Claude Opus 5 also has a supplied coding index of 74.3. GPT-4o has no coding index in the data brief, so developers should not describe Claude Opus 5 as a measured coding winner against GPT-4o. The available evidence supports a narrower conclusion: Claude Opus 5 has a strong reported coding result, while the direct comparison is missing.

The two models have the same recorded latency of 0.3 seconds. That tie suggests request-start responsiveness is not the differentiator in this dataset. Claude Opus 5 has a median output speed of 54.838 tokens per second, but GPT-4o has no corresponding supplied value. A full throughput comparison therefore remains unavailable.

Real agent performance also depends on how much work the model chooses to perform. Anthropic says Claude Opus 5 can use adaptive thinking with low, medium, high, xhigh, and max effort settings, and that Claude API and Claude Code default to high (Opus 5 update notes). A medium-effort evaluation must explicitly set effort: "medium"; otherwise, the measured behavior may not match the model label used in this comparison.

Community evidence is mixed rather than conclusive. One user described Claude Opus 5 completing complex tasks over several hours with repeated editing and testing (long-task feedback). Other users reported over-planning, excessive testing, missed work, or unwanted additions (planning feedback, additional planning feedback). These reports lack controlled prompts, project details, and reproducible logs, so they should inform testing rather than replace it.

GPT-4o is cheaper on every supplied token-price measure, but Claude Opus 5 can justify its higher price when it reduces rework or supervision.

The data brief lists GPT-4o at $4.375 per 1M blended tokens, versus $10 for Claude Opus 5. GPT-4o is also cheaper for input at $2.5 versus $5, and for output at $10 versus $25. The price advantage is therefore strongest in workloads with high volume, short prompts, predictable outputs, or limited reasoning requirements.

The chart below the section should be used for the exact price comparison. The operational question is different: how many retries, reviews, tool calls, and human corrections does each model require before a task is accepted? The supplied materials do not provide a controlled cost-per-completed-task study, so no evidence-based break-even point can be stated.

Claude Opus 5 may be economically preferable for repository-wide changes, extended debugging, or code review when one successful run replaces several weaker attempts. Anthropic describes the model as designed for long-horizon agentic coding and multi-file development (Claude Opus 5 announcement). A Reddit user also reported sustained complex task execution, but that account had no reproducible code or quantitative score (long-task feedback).

Claude Opus 5 introduces another cost-control option through prompt caching. Anthropic lists cache-hit pricing at $0.50, with five-minute writes at $6.25 and one-hour writes at $10 (Anthropic pricing). Those prices may improve repeated-context economics, but the supplied evidence does not show cache-hit rates or a workload simulation.

Thinking tokens share the max_tokens total limit with ordinary response tokens (Opus 5 update notes). Teams that fail to budget for reasoning may receive shorter visible answers, create extra follow-up calls, and erase part of the apparent price advantage. GPT-4o could still be the better choice for high-volume automation, but only after its current price and availability are verified independently.

Claude Opus 5 is the default recommendation for complex software agents, while GPT-4o fits cost-sensitive workloads that can tolerate documentation uncertainty.

Choose Claude Opus 5 when the model must inspect several files, maintain a changing plan, run tests, review its own work, or coordinate multiple tool-driven steps. Anthropic documents these scenarios as central use cases, including complex agentic coding, code review, long-context processing, and multi-agent collaboration (Opus 5 update notes). The data brief strengthens that recommendation with an Intelligence Index of 56.3 and a coding index of 74.3.

Choose GPT-4o when request volume and token price dominate the decision, the task has narrow acceptance criteria, and the integration team can verify the exact model endpoint. Its listed blended price is $4.375 per 1M tokens, compared with $10 for Claude Opus 5. GPT-4o may be appropriate for classification, extraction, routine transformation, or bounded assistant interactions, but the supplied research does not contain version-specific evidence for those scenarios.

Use Claude Opus 5 with explicit output and tool-use controls. Anthropic warns that default responses and delivery documents may become longer, with more progress narration, self-verification, and delegation (Opus 5 update notes). Community reports also mention unwanted changes, ignored project instructions, and divergence from architecture documents, although the reports do not establish frequency (instruction feedback, architecture feedback).

Do not select GPT-4o solely because its name is familiar. The supplied OpenAI directory does not list it, and the supplied pricing page does not list its current price (OpenAI Models, OpenAI Pricing). The evidence supports a verification gate before production adoption.

Claude Opus 5 requires more deliberate configuration, while GPT-4o requires more upfront verification because the supplied OpenAI documentation is not version-specific.

Before implementation, confirm the endpoint, model alias, output limits, thinking configuration, tool behavior, and acceptance tests. Claude Opus 5 has documented operational caveats, including visible tool markup when thinking is disabled and a shared token limit for thinking and ordinary output (Opus 5 update notes). GPT-4o has no comparable version-specific limitation evidence in the supplied materials.