Claude Opus 5 (Adaptive Reasoning, Max Effort) vs GPT-5 (high): The Ultimate Performance & Pricing Comparison

Claude Opus 5 is the stronger default for demanding software agents, while GPT-5 is the stronger value choice for cost-sensitive workloads. The supplied Artificial Analysis data gives Claude Opus 5 a coding index of 78 and an intelligence index of 60.7, compared with GPT-5 at 37.8 and 34.7. GPT-5 remains materially cheaper, with a blended price of $3.4375 per 1M tokens versus $10 for Claude Opus 5.\n\nClaude Opus 5 is positioned by Anthropic for complex agentic coding and enterprise work, while OpenAI positions GPT-5 for coding, reasoning, and agentic tasks. Those official descriptions overlap, but the practical choice depends on whether the application values stronger general task performance or lower unit economics. Anthropic describes Claude Opus 5 as a model for complex agentic coding and enterprise work. OpenAI describes GPT-5 as a reasoning model for coding and agentic tasks.\n\nData provided by https://artificialanalysis.ai/

Claude Opus 5 offers the clearer quality advantage for complex coding and broad reasoning, while GPT-5 offers the clearer price advantage and a stronger result in the available math comparison. The Artificial Analysis coding index favors Claude Opus 5 by 40.2 points, and its intelligence index favors Claude Opus 5 by 26 points. GPT-5 is priced at $3.4375 per 1M blended tokens, compared with $10 for Claude Opus 5.\n\nThe score gap matters most when the model must inspect a large codebase, plan across several tools, preserve constraints, and recover from intermediate failures. A stronger coding score does not guarantee fewer production defects, because the supplied benchmark data does not expose task composition, error severity, or operational cost per successful task.\n\nGPT-5 remains attractive for high-volume classification, extraction, routine code assistance, and applications where the price difference dominates. GPT-5 also records a math index of 94.3 in the supplied comparison. Claude Opus 5 has no corresponding math score, so the data cannot establish a complete winner for mathematical workloads.\n\nThe lifecycle picture is asymmetric. Claude Opus 5 is listed as Active, with no official deprecation date in the supplied research. GPT-5 remains callable through its stable alias, but its fixed snapshot is marked Deprecated and the model page recommends a newer generation. Claude Opus 5's lifecycle status appears in Anthropic's deprecation documentation. GPT-5's alias, snapshot status, and replacement guidance appear in the GPT-5 model documentation.

Claude Opus 5 is the safer performance bet for complex coding agents, but GPT-5 may still win narrowly defined mathematical tasks. The supplied Artificial Analysis data shows Claude Opus 5 at 78 on coding and 60.7 on intelligence, while GPT-5 reaches 37.8 and 34.7. The available math result favors GPT-5 at 94.3, but no Claude Opus 5 math result is supplied.\n\nThe coding difference should influence architecture. A model with the higher coding index is more plausible for repository-wide changes, multi-step debugging, and agent loops where each incorrect edit creates additional review work. Developers should still validate this assumption against their own repositories, because the data does not show how either model handles their language mix, test quality, build system, or proprietary conventions.\n\nClaude Opus 5 also has a measured median output speed of 60.088 tokens per second, while the supplied GPT-5 speed field is unavailable. Both models show latency of 0.3 seconds in the data brief. That means the comparison does not support a claim that GPT-5 is faster, even though its lower price may make higher concurrency easier to fund.\n\nClaude Opus 5's adaptive thinking is enabled by default, and Anthropic documents selectable effort levels. Anthropic also warns that disabling thinking can produce malformed tool behavior or exposed internal tags. Anthropic documents adaptive thinking and its parameter constraints here.\n\nGPT-5 supports configurable reasoning effort, verbosity, structured outputs, function calling, and custom tools. Those controls can help developers constrain response shape and reduce unnecessary output, but the supplied research does not provide a controlled comparison of tool-call accuracy or total agent completion time. OpenAI documents GPT-5's reasoning, verbosity, and tool capabilities here.

GPT-5 is the obvious unit-price winner, but Claude Opus 5 can be cheaper at the workflow level when stronger first-pass execution reduces retries and human review. GPT-5 costs $3.4375 per 1M blended tokens, while Claude Opus 5 costs $10. Input pricing is $1.25 for GPT-5 and $5 for Claude Opus 5, while output pricing is $10 and $25 respectively.\n\nThe chart makes the price gap clear, but it cannot show how many attempts a task needs. A low-cost model that requires repeated prompts, corrective patches, extra tests, or manual intervention can consume more engineering time than a higher-priced model that completes the task cleanly. The supplied data does not include cost per successful task, retry rate, or reviewer time, so this workflow-level comparison remains an evidence gap.\n\nGPT-5 is especially compelling when requests are short, predictable, and easy to verify automatically. Its lower input and output prices also reduce the penalty for exploratory calls, broad fan-out, and high-volume background processing. Claude Opus 5 becomes easier to justify when each request carries substantial planning, repository context, or tool orchestration.\n\nClaude Opus 5 supports prompt caching, including a lower price for cache hits, while GPT-5's documentation lists cached input pricing. The research does not provide a matched cache-hit workload, so neither model can be declared cheaper for repeated context without measuring the application's prompt reuse pattern. Anthropic lists Claude Opus 5 standard and caching prices.

Claude Opus 5 is the best first choice for high-risk coding agents, while GPT-5 is the best first choice for economical, tightly scoped automation. Claude Opus 5's coding index of 78 and intelligence index of 60.7 provide the stronger supplied evidence for repository work, planning, and complex agent behavior.\n\nChoose Claude Opus 5 when the model must make coordinated changes across files, reason through ambiguous requirements, use tools over a long sequence, or produce work that is expensive to review. Anthropic documents support across its API and several cloud platforms, which can matter for enterprise procurement and deployment. Anthropic's model overview lists Claude Opus 5's supported modalities and deployment options.\n\nChoose GPT-5 when the workload is dominated by predictable transformations, small debugging tasks, structured extraction, or large request volume. GPT-5's blended price of $3.4375 makes experimentation and throughput less expensive. The available math index of 94.3 also makes GPT-5 worth testing for mathematical tasks, although the absence of a Claude score prevents a complete comparison.\n\nDo not select GPT-5 solely because its stable alias remains callable. The fixed snapshot is marked Deprecated, so teams depending on reproducible behavior need a migration plan. Do not select Claude Opus 5 solely because its benchmark profile is stronger. Community reports describe verbosity, slow-feeling simple tasks, overthinking, and scope expansion, but those reports lack controlled testing. Community feedback on Claude Opus 5 reports both strong complex-task performance and concerns about verbosity. Additional Claude discussion reports disagreement and limited testing methods.\n\nThe practical decision is therefore a two-stage evaluation: use Claude Opus 5 as the quality baseline for complex agent tasks, then test GPT-5 on the same acceptance suite to determine whether its lower cost offsets additional correction work. The supplied research does not contain that application-specific evidence.

Claude Opus 5 deserves the first production trial for complex coding, but GPT-5 deserves a parallel cost and mathematics trial. The evidence supports a quality-versus-price tradeoff rather than a universal winner. Developers should measure successful task completion, correction effort, tool reliability, output length, and lifecycle stability on their own workload.\n\nThe most important unresolved issue is cost per accepted result. The supplied brief provides token prices, benchmark indexes, latency, and one output-speed value, but it does not provide retry rates, review time, or task-level success costs. That missing evidence can reverse the recommendation for a high-volume system.