GPT-5.5 (medium) vs GPT-5 nano (high): Which Model Should Developers Choose?

GPT-5.5 (medium) is the safer choice for serious development work, while GPT-5 nano (high) is attractive only when its unusually low reported cost can be verified for your deployment. The Artificial Analysis data brief reports an Intelligence Index of 50.4 for GPT-5.5 (medium) and 19.9 for GPT-5 nano (high). It also reports identical median latency of 0.3 seconds for both models, so the available evidence supports a capability and cost decision more strongly than a speed decision.

GPT-5.5 (medium) has a clear official API identity, documented reasoning controls, and stated support for professional coding workflows. OpenAI lists the callable model as gpt-5.5, with medium represented by the reasoning.effort setting rather than a separate model ID, according to the GPT-5.5 model page. GPT-5 nano (high) lacks equivalent current documentation. The OpenAI model directory does not list GPT-5 nano, gpt-5-nano, or gpt-5-nano-2025-08-07.

The practical conclusion is conditional. Choose GPT-5.5 when correctness, tool use, repository context, and operational certainty are central. Consider GPT-5 nano only after confirming the exact endpoint, limits, and price in your own account. Data provided by https://artificialanalysis.ai/.

GPT-5.5 (medium) offers the stronger documented general-purpose profile, whereas GPT-5 nano (high) offers the lower reported token cost with substantially weaker evidence about current availability. The data brief places GPT-5.5 at 50.4 on the Artificial Analysis Intelligence Index and GPT-5 nano at 19.9. It reports GPT-5.5 at 71.5 on the Artificial Analysis Coding Index, while no comparable nano coding score is provided. GPT-5 nano has a reported Artificial Analysis Math Index score of 83.7, but GPT-5.5 has no corresponding math score in the brief.

That asymmetry matters more than a simple leaderboard. The two models were not measured across the same disclosed set of indexes in the supplied comparison. The available numbers therefore support a stronger general capability signal for GPT-5.5, but they do not establish a coding winner through a direct apples-to-apples score or a general math winner. The GPT-5.5 model page also does not publish GPT-5.5 benchmark scores or test methods, so the benchmark evidence remains useful but incomplete.

GPT-5.5 is documented as a reasoning model for coding and professional work. It supports text and image input, text output, several OpenAI APIs, structured outputs, function calling, file search, web search, prompt caching, and additional tools. GPT-5 nano has no model-specific official page in the supplied materials. Its exact context window, output limit, API parameters, tool support, and failure modes are not confirmed.

For an engineering team, GPT-5.5 is the default evaluation candidate. GPT-5 nano is a cost experiment, not a safe assumption about a currently supported production model.

GPT-5.5 (medium) is the better-supported performance choice for complex coding and agent workflows, but the supplied evidence does not prove that it is faster or universally better. The data brief reports identical median latency of 0.3 seconds for GPT-5.5 and GPT-5 nano. No median output-tokens-per-second value is available for either model. A developer should therefore avoid treating the comparison as a throughput benchmark.

The stronger signal comes from task coverage. GPT-5.5 reaches 71.5 on the reported Artificial Analysis Coding Index, while GPT-5 nano has no coding score in the supplied data. GPT-5 nano reaches 83.7 on the reported Math Index, while GPT-5.5 has no math score. These results suggest that specialization and evaluation coverage may matter more than a single overall ranking. They do not show that nano is better for production mathematics, because the materials do not disclose the test method, task mix, or comparable GPT-5.5 result.

GPT-5.5’s official feature surface fits applications that need a model to inspect files, call functions, return structured data, search the web, or operate through tools. Those capabilities are listed on the GPT-5.5 model page. A repository agent may benefit from that documented integration surface even when raw latency is tied.

GPT-5 nano has a more serious evidence gap. The supplied materials contain no reliable community report with a reproducible task set, sample size, speed measurement, or coding error rate. The absence of evidence is not evidence of failure, but it prevents confident sizing of retries, review effort, and escalation paths. Test both models on your own workload before assigning nano to autonomous code changes.

GPT-5 nano (high) is the clear reported token-cost winner, but GPT-5.5 (medium) may be cheaper overall when quality reduces retries, human review, or tool loops. The data brief reports a blended price of $0.1375 per 1M tokens for GPT-5 nano and $11.25 for GPT-5.5. It also reports input prices of $0.05 and $5, and output prices of $0.4 and $30, respectively. Those figures make nano appealing for high-volume classification, routing, extraction, or low-risk drafting, provided the endpoint is actually available.

Token price alone does not determine application cost. A weaker model can require more repair prompts, additional validation calls, longer tool chains, or manual intervention. The supplied materials do not provide retry rates, task success rates, error rates, or total workflow cost for either model, so no reliable break-even point can be calculated. Developers should measure cost per accepted result rather than cost per request.

GPT-5.5 also has a long-context pricing risk. The GPT-5.5 model page and OpenAI pricing page state that inputs above 272K tokens trigger higher per-session pricing under Standard, Batch, and Flex. That rule can change the economics of large repository analysis, long document review, and repeated context injection. Prompt caching may help some workloads, but the materials do not provide a workload-specific savings estimate.

GPT-5 nano’s apparent bargain has a separate operational risk. The OpenAI pricing page lists gpt-5.4-nano, not gpt-5-nano. Developers must verify that the reported nano price corresponds to a callable endpoint, because substituting a newer nano model would change the comparison.

GPT-5.5 (medium) should be the default pick for production coding agents and complex professional workflows, while GPT-5 nano (high) should be tested as a narrowly scoped economy tier. GPT-5.5 is officially documented as a reasoning model for coding and professional work, and OpenAI identifies gpt-5.5 as the direct model ID. The relevant setting is reasoning.effort: "medium", not a model named gpt-5-5-medium, as documented on the GPT-5.5 model page.

Pick GPT-5.5 when the model must understand a large codebase, coordinate tools, produce structured outputs, or make changes that are costly to review. Its documented context window is 1,050,000 tokens and its maximum output is 128,000 tokens, although the data brief does not provide a comparable nano context or output value. Use the official API identity and test the exact reasoning configuration you intend to ship.

Pick GPT-5 nano only for workloads where low token cost dominates and the task can be validated cheaply. Suitable candidates include simple routing, narrow extraction, repetitive transformations, and candidate generation before a stronger model reviews the result. Do not infer nano’s limits or tools from gpt-5.4-nano; the supplied research explicitly warns against transferring those properties.

The final recommendation remains provisional because GPT-5 nano is absent from the current OpenAI model directory, and its dedicated pricing is absent from the current OpenAI pricing page. The OpenAI deprecations list does not provide enough evidence to establish a complete lifecycle status for nano. Confirm availability, limits, and billing in a live account before committing architecture.

A community post describes GPT-5.5 inside Claude Code as useful for multi-repository work, file editing, and terminal-style agent workflows, but the Reddit report provides no reproducible benchmark, sample size, or speed method. Treat it as anecdotal workflow evidence, not proof of production superiority.

GPT-5.5 (medium) should enter evaluation first when a team needs a documented model contract, because GPT-5 nano’s current API identity and limits are not confirmed. The available evidence favors a staged decision: validate operational facts, test representative tasks, then compare accepted-result cost.

GPT-5.5 also requires explicit attention to configuration. The official model page describes medium as a reasoning effort value, so application code should distinguish the model ID from its reasoning setting. GPT-5 nano requires the opposite discipline: do not assume that a historical name maps to a current endpoint. These details affect deployment reliability before model quality is even measured.

The most important missing evidence concerns real task outcomes. The materials do not establish coding error rates, output throughput, retry behavior, or a direct math comparison. A short internal test should measure successful task completion, review time, tool-call count, retry count, and accepted-result cost for the exact prompts and constraints your product uses.