Claude Opus 5 Medium vs GPT-5 nano: Which Model Should Developers Choose?

Claude Opus 5 is the safer choice for demanding development work, while GPT-5 nano is the lower-cost option with major evidence gaps. Artificial Analysis reports an Intelligence Index of 56.3 for Claude Opus 5 and 19.9 for GPT-5 nano, while its Math Index is 83.7 for GPT-5 nano. The same dataset reports Claude Opus 5 at 54.838 median output tokens per second, with no corresponding speed value for GPT-5 nano. Both models show 0.3 seconds of reported latency. Data provided by https://artificialanalysis.ai/.

The comparison has an important qualification. Anthropic currently documents Claude Opus 5 as claude-opus-5, with medium reasoning selected through effort: "medium", according to the model overview and Opus 5 update notes. OpenAI’s current model catalog does not list GPT-5 nano, so the GPT-5 nano label in the benchmark dataset cannot be mapped confidently to a currently supported public API model.

Claude Opus 5 offers the stronger documented fit for complex software tasks, but GPT-5 nano is dramatically cheaper in the supplied data. Anthropic positions Claude Opus 5 for complex agentic coding, enterprise work, multi-file development, code review, visual understanding, long-context processing, and multi-agent collaboration in its official announcement. The model overview also documents text and image input, text output, multilingual capability, and access through Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry.

The evidence is asymmetric. Claude has a documented model ID, a documented reasoning control, a listed release date of 2026-07-24, and a current listing without a deprecated or retired label in the supplied research. GPT-5 nano has a supplied release date of 2025-08-07, but its current official catalog status is unresolved. The research found no official GPT-5 nano context limit, output limit, parameter documentation, dedicated benchmark record, or failure-mode documentation.

Artificial Analysis gives Claude Opus 5 the available coding score, 74.3, but provides no GPT-5 nano coding score. It gives GPT-5 nano the available math score, 83.7, but provides no Claude Opus 5 math score. Those missing cells prevent a complete capability ranking. The available intelligence comparison still favors Claude Opus 5, with 56.3 versus 19.9.

For production selection, the practical decision is therefore two-dimensional. Choose Claude when task quality, autonomy, and documented integration matter most. Consider GPT-5 nano only for cost-sensitive workloads where its exact API identity and behavior have already been verified in your own environment.

Claude Opus 5 has the stronger available general-intelligence evidence, but GPT-5 nano may still be useful for narrow mathematical workloads. Artificial Analysis reports an Intelligence Index of 56.3 for Claude Opus 5 versus 19.9 for GPT-5 nano. That gap suggests a meaningful difference in broad task capability, but it does not prove that Claude wins every developer workflow. The dataset has no GPT-5 nano coding score, so it cannot establish a direct coding winner.

Claude’s available Coding Index is 74.3, and the official Opus 5 announcement describes strong results across coding, agentic tasks, automation, and computer-use evaluations. Anthropic does not publish complete reproducible configurations for those claims in the supplied material. The result is useful for positioning, but not a substitute for testing your repository, tools, prompts, and acceptance checks.

The speed evidence also favors caution. Claude Opus 5 records 54.838 median output tokens per second, while GPT-5 nano has no reported value. Both models show 0.3 seconds of latency, so the available data does not establish a latency advantage. Token generation speed and end-to-end completion time are different outcomes, especially for agents that call tools, retry, inspect files, or run tests.

Claude’s adaptive reasoning introduces a tuning tradeoff. The update notes state that thinking is enabled by default and that effort can be set from low through max. Thinking tokens share the max_tokens total, which can reduce visible output unless the limit is adjusted. Developers should therefore evaluate completion quality, tool-call count, and wall-clock task time together.

GPT-5 nano is overwhelmingly cheaper in the supplied pricing data, but its unclear product status makes the apparent saving conditional. Artificial Analysis lists GPT-5 nano at $0.1375 per 1M blended tokens, compared with $10 for Claude Opus 5. It also lists input pricing of $0.05 versus $5 and output pricing of $0.4 versus $25. These values make GPT-5 nano the obvious candidate for high-volume, low-risk requests if the benchmark label maps to a callable model.

The cost conclusion can reverse when a task needs retries, human review, or stronger autonomous execution. A cheap model that produces incomplete patches, misses repository instructions, or requires repeated correction can consume more engineering time than a more expensive model that finishes the task correctly. The supplied research reports community complaints about over-planning, unrequested changes, instruction drift, and unclear explanations, but those reports lack complete prompts, logs, and controlled comparisons. They are risk signals, not measured rates.

Claude’s official pricing page confirms $5 per 1M input tokens and $25 per 1M output tokens. The same research also documents prompt caching, which may change the economics of repeated-context workloads. Claude’s update notes describe a faster research-preview mode with higher prices, but that mode is separate from the medium-effort comparison.

OpenAI’s current pricing page does not list GPT-5 nano in the supplied research. It lists a different nano model, so developers should not transfer that model’s pricing to GPT-5 nano. The missing official price is the central cost risk.

Claude Opus 5 is the better default for complex repository work, while GPT-5 nano belongs in a verified low-cost experiment. Choose Claude Opus 5 for multi-file changes, code review, long-running agents, visual inputs, enterprise integrations, or tasks where the model must inspect, edit, test, and revise across a large working context. Anthropic explicitly targets these workflows in its official release announcement, and the model overview documents the claude-opus-5 API identity and supported platforms.

Use medium effort only when the evaluation is intended to represent this comparison. Anthropic states that medium effort is a setting on the stable model ID, not a separate claude-opus-5-medium API model, in the Opus 5 update notes. This distinction matters for reproducibility, routing, logging, and billing.

Test GPT-5 nano for classification, short transformations, simple extraction, bounded arithmetic, or other workloads where a failure is cheap and easy to detect. The available Math Index of 83.7 makes mathematical evaluation a reasonable test area, but the supplied evidence does not show how that score transfers to production prompts. OpenAI’s model catalog does not currently confirm GPT-5 nano’s public API identity, limits, or current availability.

Do not make GPT-5 nano the unverified foundation of a production integration. First confirm the exact model ID, request parameters, context behavior, output limits, rate limits, and billing source. The research contains no reliable community evidence for GPT-5 nano’s coding experience, speed, or failure patterns. That absence is more important than the low listed price.

A sensible rollout is to use Claude Opus 5 for the quality baseline, then compare GPT-5 nano on a fixed task set with automatic tests and human review. The result should measure successful task completion, correction cycles, tool errors, and total engineering time, not token price alone.

Claude Opus 5 has a verifiable current integration path, while GPT-5 nano has an unresolved version identity. The Anthropic model overview identifies claude-opus-5 for Claude API, anthropic.claude-opus-5 for Amazon Bedrock, and claude-opus-5 for Google Cloud. The supplied research also says Claude Opus 5 remains in Anthropic’s current model list as of 2026-08-05 without a deprecated or retired marker.

GPT-5 nano has no equivalent confirmation in the supplied OpenAI model documentation. The page does not list GPT-5 nano, gpt-5-nano, or gpt-5-nano-2025-08-07. It also does not establish which API parameters, context window, or output maximum apply to the benchmark label. The research therefore cannot answer whether the tested identifier is a live public endpoint, a historical alias, or an internal benchmark mapping.

This difference affects more than procurement. A stable model identifier supports deployment automation, regression testing, incident response, and reproducible evaluations. An uncertain identifier creates a risk that a successful benchmark cannot be recreated in production. Developers should treat GPT-5 nano’s benchmark data as provisional until the endpoint and configuration are independently confirmed.

Claude Opus 5 deserves the initial production benchmark because its identity and behavior are better documented. The supplied evidence supports that starting point through Anthropic’s current model overview, update notes, and release announcement. GPT-5 nano should remain a controlled candidate until its current API status is confirmed through OpenAI’s model catalog.

The unresolved questions are operational rather than cosmetic. Teams should confirm whether the benchmark’s GPT-5 nano identifier is callable, whether its listed price is current, and whether its strong Math Index transfers to their actual tasks. Teams should also test Claude’s tendency toward longer responses, extra validation, and delegated work because those behaviors can affect tool budgets and review effort. The research includes these qualitative concerns from community reports, but it does not provide controlled failure rates.