GPT-5.6 Sol (medium) vs GPT-5 nano (high): The Ultimate Performance & Pricing Comparison

GPT-5.6 Sol (medium) is the safer default for demanding developer work, while GPT-5 nano (high) is a low-cost specialist with incomplete current documentation. The available evaluation data gives GPT-5.6 Sol (medium) an Intelligence Index of 53.6, compared with 19.9 for GPT-5 nano (high). GPT-5 nano (high) costs $0.1375 per 1M blended tokens, while GPT-5.6 Sol (medium) costs $11.25. That price gap is large enough to change the architecture of a production system, but the evidence does not establish whether nano can replace Sol on coding, agent reliability, or broad reasoning. Data provided by https://artificialanalysis.ai/

GPT-5.6 Sol (medium) offers the stronger documented choice for complex reasoning and coding, but GPT-5 nano (high) can be economically compelling for narrow, repetitive workloads. OpenAI positions GPT-5.6 Sol as a flagship reasoning model for complex professional work, complex reasoning, and coding. Its official documentation describes support for text and image input, structured outputs, function calling, file search, web search, prompt caching, and several tool integrations. The official OpenAI model directory does not currently list GPT-5 nano, so its present availability, API alias, context limits, and supported parameters remain unverified. The comparison therefore has an asymmetrical evidence base: Sol has official product documentation and community reports, while nano has benchmark and price observations but no verified current official product page. Developers should treat the Intelligence Index gap as directional evidence, not as a complete prediction of application quality. The data shows no coding comparison because GPT-5 nano has no coding score in the supplied snapshot. It also shows no output-speed comparison because nano has no reported median output speed.

GPT-5.6 Sol (medium) is the better-supported option for multi-step engineering work, although the supplied evidence cannot prove a universal coding win. The Artificial Analysis Intelligence Index is 53.6 for Sol and 19.9 for nano, a difference that suggests broader general reasoning headroom for tasks involving planning, interpretation, and changing constraints. GPT-5.6 Sol also has a reported coding index of 76.3, but GPT-5 nano has no coding index in the data snapshot. That missing value matters more than a simple ranking: developers cannot infer nano's repository-level coding reliability from its math score of 83.7. GPT-5 nano may still fit bounded mathematical or classification tasks, yet the available material does not establish how it handles tool use, code edits, debugging, or long-running agents. Both models have a reported latency of 0.3 seconds, but only Sol has a median output speed of 69.865 tokens per second. The absence of nano's speed figure prevents a fair throughput conclusion. Officially, GPT-5.6 Sol supports Responses API, Chat Completions API, Batch API, structured outputs, function calling, and tool use. Community evidence is mixed: one Reddit report describes excessive output and flawed task completion, while comments in the same discussion dispute the testing quality. A Hacker News discussion and its corresponding comment focus on possible speed benefits, but provide no reproducible Sol benchmark. Sol's official guide also warns that safety checks can pause generation and that legitimate dual-use requests may be rejected, especially in sensitive domains: GPT-5.6 usage guide.

GPT-5 nano (high) is dramatically cheaper, but its lower price becomes a liability if weak task completion creates retries, review work, or routing complexity. The blended price is $0.1375 per 1M tokens for nano versus $11.25 for Sol. Nano also costs $0.05 per 1M input tokens and $0.4 per 1M output tokens, compared with $5 and $30 for Sol. Those figures make nano attractive for high-volume extraction, lightweight classification, draft generation, and other tasks with simple acceptance criteria. Sol can still be cheaper at the system level when one successful call replaces several weak attempts, especially for code changes or workflows requiring tools and validation. The supplied data does not provide retry rates, human review costs, task success rates, or token consumption by workload, so no total-cost winner can be calculated. The blended figure also cannot answer whether output-heavy applications favor one model under their actual input-output mix. Developers should measure cost per accepted result, not cost per request. OpenAI's current pricing page does not list GPT-5 nano, and the page's listed nano pricing belongs to GPT-5.4-nano, which cannot be transferred to this comparison. The GPT-5.6 Sol model page additionally documents higher pricing for very large inputs, creating a cost risk for oversized codebases and long document workflows. That risk is material even when Sol's headline capability is valuable.

GPT-5.6 Sol (medium) should be the primary choice for complex coding and agent workflows, while GPT-5 nano (high) should be a constrained low-cost route until its official status is verified. Choose Sol when the model must understand a large set of requirements, call tools, produce structured results, modify code, or recover from ambiguous intermediate states. Its official positioning and available coding index support that role, although the Reddit report shows that production testing should still check verbosity, task progress, and implementation correctness. Choose nano when the task is narrow, repetitive, easy to validate, and cheap failure is acceptable. Its math index of 83.7 may justify a focused evaluation for mathematical workloads, but it does not establish general reasoning or coding performance. A practical routing policy can send simple, high-volume requests to nano and escalate uncertain cases to Sol. That policy remains provisional because OpenAI's model directory does not currently confirm GPT-5 nano as an available model. Developers should also review the OpenAI deprecations directory before committing to a model identifier, because the supplied research could not verify nano's current lifecycle status. The evidence is insufficient to recommend nano for production coding agents, and insufficient to reject it for narrowly scoped workloads. Validate with representative prompts, accepted-result rates, retry volume, and operational review effort before making the route permanent.

GPT-5.6 Sol (medium) is easier to evaluate for production because its official model page documents its identity, interfaces, capabilities, and constraints. GPT-5 nano (high) has a cheaper observed price and useful supplied scores, but its current official availability is unresolved. The key deployment question is therefore not which model looks better in isolation. It is whether the workload needs broad reasoning, coding reliability, and tool support, or whether it can tolerate uncertain capability in exchange for much lower unit cost. Developers should keep the evaluation boundary explicit: the available data confirms no coding score for nano, no output-speed figure for nano, and no official current model listing for nano. Those gaps prevent confident claims about coding agents, throughput, context handling, and lifecycle stability. Sol's documented features do not eliminate application risk. Safety pauses, possible refusals in dual-use domains, large-input pricing, and mixed community feedback still require workload-specific testing. The strongest decision is a conditional one: use Sol where failure is expensive, and test nano only where validation is cheap and deterministic.

GPT-5.6 Sol (medium) is the stronger default for developers who need broad reasoning, coding support, structured outputs, and tool-enabled workflows. GPT-5 nano (high) is better treated as an experimental low-cost route until its current official availability and coding behavior are confirmed.