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

GPT-5.6 Sol (high) is the safer choice for demanding development work, while GPT-5 nano (high) is attractive only when its low reported price outweighs major evidence gaps. Artificial Analysis reports an Intelligence Index of 55.9 for GPT-5.6 Sol and 19.9 for GPT-5 nano, while the available coding comparison does not provide a GPT-5 nano score. Artificial Analysis provides the comparison data used here.

The comparison is unusually asymmetric. OpenAI currently documents GPT-5.6 Sol as a flagship reasoning model for complex professional work, reasoning, and coding. OpenAI’s GPT-5.6 Sol model page identifies the callable model as gpt-5.6-sol, not gpt-5.6-sol-high. By contrast, the current OpenAI model directory does not list GPT-5 nano, so the nano entry cannot be treated as a confirmed current product offering.

That difference matters more than the nominal release dates. GPT-5 nano is cheaper on the supplied data, but developers cannot verify its current API identity, limits, or production support from the cited official pages.

GPT-5.6 Sol (high) offers the stronger documented foundation for high-value engineering tasks, whereas GPT-5 nano (high) offers a dramatic cost advantage without comparable current documentation.

GPT-5.6 Sol is the better default for agents that must understand repositories, plan multi-step changes, investigate unfamiliar systems, or use tools. OpenAI documents text and image input, structured outputs, function calling, file search, web search, code interpreter, hosted shell, computer use, MCP, and related capabilities on the GPT-5.6 Sol model page.

GPT-5 nano may still fit cheap routing, experimentation, or narrow mathematical workloads. Its supplied Math Index is 83.7, but the research does not establish whether that result reflects the same task distribution, API configuration, or production availability that a developer can use today.

GPT-5.6 Sol (high) is the only model in this comparison with supplied coding performance evidence, making it the more defensible choice for repository-level engineering workflows.

The supplied Artificial Analysis results show a clear asymmetry in general intelligence: GPT-5.6 Sol scores 55.9, compared with 19.9 for GPT-5 nano. That gap suggests different deployment roles. Sol is easier to justify for tasks where the model must maintain a broad problem model, choose among tools, and recover from ambiguous requirements. Nano may be useful for constrained subtasks, but the available evidence does not show how it handles code navigation, debugging, edits across files, or agent loops.

The coding evidence is also one-sided. GPT-5.6 Sol has a Coding Index of 77.2, while no corresponding nano coding score is supplied. That does not prove Sol wins every coding task. It means the evidence is insufficient to quantify the coding gap, and a buyer should not convert the missing nano score into a zero.

Sol’s supplied median output speed is 73.648 tokens per second, but nano has no reported value. The equal supplied latency value of 0.3 seconds therefore does not establish equal end-to-end responsiveness. First-token latency, reasoning duration, tool-call delay, queueing, output length, and retry behavior remain unreported for this comparison.

Official guidance adds an important operational constraint. OpenAI explains that reasoning tokens consume the context window and that a low max_output_tokens limit can produce an incomplete response before the visible answer is finished. The reasoning models guide also describes reasoning.effort and its use for complex debugging, planning, and high-value coding. Those controls are documented for Sol, but the nano material does not confirm equivalent support.

Community reports raise risks around Sol, including slow subjective experiences, over-engineering, irrelevant investigation paths, and excessive defensive code. Reddit feedback and Hacker News feedback lack standardized task sets and measurements, so they should guide pilots rather than override the official capability evidence. A separate Hacker News test report covers only one rewrite task and cannot establish general coding behavior.

GPT-5 nano (high) is vastly cheaper on the supplied token prices, but GPT-5.6 Sol (high) can be economically rational when better task completion reduces retries and human correction.

The reported blended price is $0.1375 for nano versus $11.25 for Sol per 1M tokens. That difference makes nano the obvious candidate for high-volume classification, lightweight extraction, short transformations, or early-stage routing. It does not answer the more important production question: how many attempts, tool calls, review cycles, and failed changes does each task require?

A cheaper model becomes more expensive when it produces incomplete repository edits, misses hidden constraints, or requires repeated prompting. The supplied data does not measure success per dollar, tokens per completed task, retry rate, or human review time. Evidence is therefore insufficient to claim that Sol has lower total cost for any particular workflow, or that nano has lower total cost beyond raw token billing.

Sol’s listed input price is $5 and output price is $30 per 1M tokens. Nano’s corresponding values are $0.05 and $0.4. Output-heavy agent workflows expose the largest raw price difference because reasoning and generated patches can consume substantial output budgets. Developers should also account for Sol’s documented pricing boundary: OpenAI’s pricing page says inputs above 272K tokens use the higher long-context price.

That rule matters for codebases, logs, design documents, and long-running sessions. Sol’s model page documents a context window of 1,050,000 tokens, a maximum input of 922,000 tokens, and a maximum output of 128,000 tokens. Large capacity does not mean large context is free. Nano’s corresponding official limits are not found in the current cited documentation, so cost planning for long inputs remains unresolved.

GPT-5.6 Sol (high) should be the default for high-consequence engineering tasks, while GPT-5 nano (high) should be considered an experimental low-cost specialist until its current API status is verified.

Choose GPT-5.6 Sol for:

• Multi-file code changes where requirements are incomplete or distributed across a repository.
• Debugging that requires tracing causes through unfamiliar code and tools.
• Long planning tasks, security-sensitive reviews, and agent workflows where failure creates expensive rework.
• Applications that need documented Responses API, structured outputs, function calling, file search, web search, or computer-use support.

Choose GPT-5 nano only when all of the following are acceptable:

• The task is narrow enough to tolerate weaker general reasoning evidence.
• A developer has independently verified the model endpoint and behavior in the intended environment.
• The workflow has cheap validation, retries, or human review.
• Raw token cost matters more than undocumented capability and support risk.

The strongest practical architecture is a routing policy, not a universal winner. Use a low-cost model for predictable, reversible subtasks, then escalate ambiguous or high-impact cases to Sol. However, the supplied material does not confirm that nano is currently callable, nor does it provide a coding score or median output speed. That uncertainty should be resolved with a small, reproducible pilot before production adoption.

For Sol, use the documented model ID gpt-5.6-sol or the stable alias gpt-5.6, then configure reasoning deliberately. OpenAI’s reasoning guide states that the model identifier and reasoning.effort are separate controls. Do not assume that the comparison label GPT-5.6 Sol (high) is itself an API model ID.

GPT-5.6 Sol (high) is the more supportable production choice because its model identity, capabilities, limits, and reasoning controls are documented by OpenAI.

GPT-5 nano (high) remains a cost-sensitive hypothesis rather than a fully verified product choice. The current source set supports a careful pilot, not a confident claim about availability or coding quality.