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

Claude Sonnet 5 is the safer default for developers who need a currently documented model with verified general capability, while GPT-5 nano is attractive mainly because its benchmark record shows strong mathematics and its listed blended price is far lower. Artificial Analysis reports an Intelligence Index of 53.4 for Claude Sonnet 5 and 19.9 for GPT-5 nano, while GPT-5 nano records a Math Index of 83.7. The same dataset reports Claude Sonnet 5 at 89.078 median output tokens per second, with latency at 0.3 seconds for each model. Data provided by https://artificialanalysis.ai/

The comparison has an important asymmetry: Anthropic documents Claude Sonnet 5 as claude-sonnet-5, while OpenAI's current model directory does not list GPT-5 nano or gpt-5-nano. Anthropic's model overview confirms the Claude model's identity and availability details. OpenAI's model directory does not provide equivalent current documentation for GPT-5 nano.

That makes this more than a quality-versus-price decision. It is also a choice between a documented production target and a model whose current endpoint, context limits, parameters, and lifecycle cannot be confirmed from the supplied official sources.

Claude Sonnet 5 offers the stronger verified general-purpose profile, while GPT-5 nano offers the stronger apparent cost position and a high mathematics score with much less documentation certainty. The Artificial Analysis Intelligence Index gives Claude Sonnet 5 a 33.5-point lead over GPT-5 nano, but the dataset does not provide a directly comparable coding score for GPT-5 nano or a directly comparable mathematics score for Claude Sonnet 5. Data provided by https://artificialanalysis.ai/

The missing values are decision-relevant, not cosmetic. A developer cannot infer GPT-5 nano's coding performance from GPT-5 nano's mathematics score, and cannot infer its production limits from the currently listed gpt-5.4-nano model. OpenAI's pricing page explicitly lists gpt-5.4-nano, but that is not evidence for GPT-5 nano. The supplied material therefore supports a confident Claude recommendation, but not a complete capability ranking across every developer workload.

Claude Sonnet 5 is the stronger choice for general coding and agentic work because its available evidence covers intelligence, coding, tool use, and long-running task behavior more directly. Artificial Analysis reports a Coding Index of 71.5 for Claude Sonnet 5, while no comparable GPT-5 nano coding value appears in the dataset. Data provided by https://artificialanalysis.ai/

For a software team, that gap changes how much validation work is required. Claude's documented support for adaptive reasoning, tool use, coding, knowledge work, agentic search, and computer use gives teams a clearer basis for designing workflows. Anthropic's announcement describes those areas as central improvements. Claude's adaptive reasoning is enabled by default, and the effort parameter controls reasoning intensity. Anthropic's effort documentation

GPT-5 nano may still be the better performer for a narrow mathematics task. Its Math Index is 83.7, but the supplied material does not explain the benchmark composition, provide a Claude mathematics score, or provide a reproducible task-level comparison. That evidence is insufficient for claiming that GPT-5 nano is faster, better at coding, or more reliable in production.

The reported latency tie at 0.3 seconds should not be mistaken for equivalent user experience. Claude has a measured median output speed of 89.078 tokens per second, whereas GPT-5 nano has no reported value in the dataset. For streaming interfaces, long completions, and agent traces, missing throughput data is a material unknown.

GPT-5 nano has the clear unit-price advantage, but Claude Sonnet 5 can be cheaper in practice when higher task reliability reduces retries, review time, and orchestration overhead. The reported blended prices are $0.1375 for GPT-5 nano and $4 for Claude Sonnet 5 per 1M blended tokens. Data provided by https://artificialanalysis.ai/

The price gap encourages a sensible architecture: use GPT-5 nano for narrowly bounded, high-volume tasks such as classification, arithmetic checks, or candidate generation, provided the exact endpoint is available and passes evaluation. The 83.7 Math Index makes mathematics a plausible test area, but it does not establish quality for coding, tool calls, structured extraction, or multi-step reasoning.

Claude's economics are more complicated because adaptive reasoning consumes part of the output budget. Anthropic states that max_tokens includes reasoning tokens and final response tokens, so a budget copied from an older non-thinking workload can cause truncation. Anthropic's Sonnet 5 release notes also state that the new tokenizer usually produces about 30% more tokens for the same text, although the exact increase depends on content.

That means a cheaper model can become more expensive if it requires more retries, weaker outputs need human correction, or missing documentation forces a parallel fallback. Teams should compare cost per accepted result, not only cost per token. The supplied materials do not provide retry rates, completion quality at a fixed task level, or total production cost, so the economic winner remains workload-dependent.

Claude Sonnet 5 is the recommended production default when the application needs documented availability, broad coding capability, and controllable reasoning behavior. Anthropic lists the model as directly callable under the stable alias claude-sonnet-5, with text and image input, text output, multilingual capability, and vision support. Anthropic's model overview

Choose Claude Sonnet 5 when:

• The application performs complex coding, repository analysis, tool use, or agentic search.
• The team needs a documented model identity and current lifecycle status.
• Long tasks benefit from adaptive reasoning and adjustable effort.
• The cost of incorrect output is higher than the cost of tokens.

Choose GPT-5 nano only after an availability check and a task-specific evaluation. Its price advantage is substantial, and its Math Index of 83.7 makes it worth testing for mathematical or other tightly constrained workloads. However, OpenAI's current model directory does not list GPT-5 nano, and OpenAI's pricing page does not list gpt-5-nano.

Claude also requires integration changes for some existing clients. Non-default temperature, top_p, and top_k settings return HTTP 400, manual extended thinking returns HTTP 400, and assistant-message prefilling is unsupported. Anthropic's release notes document these constraints. High-risk cybersecurity requests may return HTTP 200 with stop_reason: "refusal", so callers must inspect the response body rather than rely on HTTP status alone. Anthropic's announcement

The practical decision is therefore straightforward: start with Claude for broad production workloads, and introduce GPT-5 nano only where a controlled evaluation proves that its lower token price produces acceptable completed-task cost.

Claude Sonnet 5 is the model with the stronger documented production story, while GPT-5 nano remains an evidence-limited candidate for targeted evaluation. The supplied sources leave several questions unanswered, so teams should treat those gaps as explicit validation work rather than fill them with assumptions.

A useful rollout sequence is:

1. Confirm that the exact model identifier is available in the intended account and region.
2. Test representative prompts with fixed acceptance criteria.
3. Measure retries, truncation, tool-call failures, and human correction.
4. Compare cost per accepted result across the actual workload.

This sequence matters because the sources do not provide a complete head-to-head benchmark. They show Claude's general and coding signals, GPT-5 nano's mathematics signal, Claude's measured output speed, and the reported price difference. They do not show a matched coding benchmark, matched mathematics benchmark, GPT-5 nano throughput, context limits, or production retry rates.