Claude Opus 4.7 (Non-reasoning, High Effort) vs GPT-5 (high): The Ultimate Performance & Pricing Comparison

Claude Opus 4.7 (Non-reasoning, High Effort) is the stronger measured general-intelligence option, while GPT-5 (high) is the safer economic and integration choice for most developers. The Artificial Analysis Intelligence Index gives Claude Opus 4.7 a score of 42.7 and GPT-5 a score of 34.7. That advantage does not establish a universal Claude win because the available snapshot has no comparable Claude coding or math result. GPT-5 is also clearly cheaper at $3.4375 versus $10 per 1M blended tokens.\n\nThe comparison has an important asymmetry. GPT-5 has a documented API alias, documented parameters, documented tool support, and a documented model page at OpenAI's GPT-5 documentation. The exact Claude configuration lacks a dedicated official capability page or confirmed API identifier in the supplied material, although Anthropic's model overview confirms Claude Opus 4.7 as a product name. Developers should therefore separate measured capability from deployment certainty.

GPT-5 (high) is the better default for production teams that prioritize cost, API clarity, coding evidence, or mathematical performance. GPT-5 costs $3.4375 per 1M blended tokens, compared with $10 for Claude Opus 4.7. Its available Artificial Analysis scores include 37.8 for coding and 94.3 for math. Those results make GPT-5 easier to justify for software maintenance, automated code changes, and quantitative workloads, although the supplied snapshot does not provide a direct Claude comparison for either category.\n\nClaude Opus 4.7 (Non-reasoning, High Effort) is the more attractive candidate for broad reasoning tasks where the Intelligence Index is a meaningful proxy for the workload. Its score of 42.7 exceeds GPT-5's 34.7. The result is useful, but it does not answer whether Claude writes better production code, responds faster, or produces fewer errors in a specific repository. No reliable community evidence was supplied for this exact Claude configuration.\n\nGPT-5's official materials describe a reasoning model for coding, reasoning, and agentic tasks, with function calling, structured outputs, streaming, and configurable reasoning effort documented in GPT-5 for developers. Anthropic's supplied documentation confirms product-level Claude Opus 4.7 information, but not a standalone description for the Non-reasoning, High Effort variant. The practical conclusion is conditional: Claude has the stronger general score, while GPT-5 has the stronger evidence package for implementation.

Claude Opus 4.7 (Non-reasoning, High Effort) leads the available general-intelligence measurement, but GPT-5 (high) has the more useful category coverage for developer selection. Claude's Artificial Analysis Intelligence Index is 42.7, compared with 34.7 for GPT-5. That gap suggests Claude may be worth testing for broad, multi-step work where judgment quality matters more than narrow task specialization. It does not prove that Claude will outperform GPT-5 on debugging, code review, or agent execution.\n\nGPT-5 has a coding index of 37.8 and a math index of 94.3 in the supplied data. These values give engineering teams concrete starting points for coding and quantitative evaluations. The missing Claude values are more important than the visible GPT-5 scores: without matching Claude measurements, the data cannot establish a coding winner or a math winner. Teams should not convert Claude's general-intelligence lead into an unsupported claim about programming quality.\n\nThe two models tie on reported latency at 0.3 seconds. Median output tokens per second are unavailable for both, so the latency figure cannot answer whether either model streams long answers faster. A short initial response and a fast complete answer are different production experiences. Developers building interactive tools should measure time to first useful token, completion time, interruption behavior, and retry rates in their own stack. The supplied evidence supports only a latency tie, not a speed advantage.\n\nGPT-5's official documentation supports text and image input with text output, while audio and video input or output are unsupported according to the GPT-5 model documentation. GPT-5 also supports structured outputs and custom tools according to GPT-5 for developers. No equivalent variant-specific Claude modality or tool description appears in the supplied material, which makes GPT-5 easier to scope during early architecture work.\n\nCommunity evidence does not close the gap. One Reddit author reported that GPT-5 was useful for locating and fixing small bugs, but considered it less complete for full applications and UI generation; commenters also described possible hallucinations or incorrect edits in complex existing codebases. The post was a subjective, non-controlled test, as documented in the Reddit discussion. No similarly reliable community evidence was supplied for the exact Claude variant.

GPT-5 (high) is the clear price winner for the supplied usage mix, but workload shape determines whether that advantage survives production. Its blended price is $3.4375 per 1M tokens, compared with $10 for Claude Opus 4.7. GPT-5 also has the lower input price at $1.25 versus $5, and the lower output price at $10 versus $25. For teams sending similar prompts and receiving similar answers, the economic case for GPT-5 is strong.\n\nClaude Opus 4.7 (Non-reasoning, High Effort) can become disproportionately expensive when outputs are long or when the application repeatedly sends large context. Its output price is $25 per 1M tokens, and Anthropic's pricing documentation says Claude Opus 4.7 uses a newer tokenizer that usually produces about 30% more tokens for the same text. The supplied data does not quantify the resulting bill for a particular workload, so teams should treat the tokenizer effect as a cost risk rather than a fixed multiplier.\n\nGPT-5 can also become the more expensive choice if its higher-quality result reduces less rework than Claude, but the supplied evidence cannot measure that tradeoff. No retry rate, task success rate, completion length, or production usage distribution is provided for either model. A lower token price is therefore not the same as a lower cost per accepted change.\n\nCaching can change the comparison as well. The supplied research brief lists Claude cache prices, but the data brief's blended comparison does not model cache hits or cache writes. Because the article must preserve the supplied comparison rather than invent a workload model, the defensible conclusion is narrower: GPT-5 wins the listed token-price comparison, while total cost of ownership remains unproven for cache-heavy or failure-prone workflows.

GPT-5 (high) is the recommended default for most production developer workloads because its price, documented API surface, and available coding evidence reduce selection risk. Choose GPT-5 when the system needs a confirmed model alias, configurable reasoning effort, structured outputs, function calling, streaming, or custom tools. Those capabilities are documented in GPT-5 for developers and the GPT-5 model documentation.\n\nGPT-5 is especially suitable when the workload is cost-sensitive, coding-heavy, or math-heavy. The available data gives it a coding index of 37.8 and a math index of 94.3, while its blended price is $3.4375 per 1M tokens. The evidence still does not show that GPT-5 produces fewer incorrect edits in a real repository. Teams should add repository-level tests before treating the benchmark signal as a deployment guarantee.\n\nClaude Opus 4.7 (Non-reasoning, High Effort) is worth a targeted evaluation when broad reasoning quality is the primary selection criterion. Its Intelligence Index of 42.7 is higher than GPT-5's 34.7, and that is the strongest measured capability signal in the comparison. Claude may be the better fit for difficult analysis, planning, or judgment-heavy tasks, but the supplied material does not provide variant-specific coding behavior, output speed, or stable failure patterns.\n\nDo not commit to Claude's exact configuration until the vendor confirms the callable model ID, availability on the intended platform, synchronous output limits, and replacement policy. Anthropic's model overview mentions Claude Opus 4.7 support for Message Batches, but that does not establish identical limits for synchronous requests. GPT-5 also carries a version-management warning: OpenAI's model documentation marks the fixed snapshot as Deprecated and recommends GPT-5.6. Teams choosing GPT-5 should use the documented alias strategy and plan migration checks.

Claude Opus 4.7 (Non-reasoning, High Effort) requires more validation before adoption because the supplied evidence does not confirm the exact API identity or variant-specific behavior. GPT-5 (high) requires migration planning because its fixed snapshot is marked Deprecated in the supplied documentation.\n\nThe most important unresolved question is not which score is higher. It is whether the selected model completes the team's real task with acceptable review effort, latency, and cost. The supplied snapshot cannot answer that directly. A short bake-off using representative repositories, structured acceptance tests, and the application's actual prompt and caching pattern is necessary before a final commitment.