GPT-5.5 (high) vs GPT-5 nano (high): The Ultimate Performance & Pricing Comparison

GPT-5.5 (high) is the safer default for demanding development work, while GPT-5 nano (high) is a radically cheaper specialist option with incomplete documentation. The available Artificial Analysis data gives GPT-5.5 (high) a 53.1 Intelligence Index score and GPT-5 nano (high) a 19.9 score. GPT-5 nano (high) does, however, record an 83.7 Math Index score, so the comparison is not a simple ranking across every task. Both models show 0.3 seconds of median latency in the supplied snapshot, while neither has a reported median output speed. Pricing creates the sharpest practical divide: GPT-5 nano (high) costs $0.1375 per 1M blended tokens, compared with $11.25 for GPT-5.5 (high). Developers should therefore treat the choice as a reliability-versus-unit-cost decision, not as a contest with one universal winner. Official OpenAI documentation currently describes GPT-5.5 as an active model, but the supplied research could not confirm an active listing, API alias, or dedicated documentation for GPT-5 nano. GPT-5.5 model page and OpenAI Models provide the relevant official status evidence.

GPT-5.5 (high) offers the stronger general-purpose evidence, but GPT-5 nano (high) can be economically compelling where task scope is narrow and verification is cheap. The Artificial Analysis Intelligence Index favors GPT-5.5 (high) at 53.1 versus 19.9, a large difference for applications that must interpret ambiguous requirements, coordinate tools, or maintain context across a complex workflow. The supplied data does not provide a coding score for GPT-5 nano (high), so no evidence-based coding winner can be declared from this comparison. GPT-5 nano (high) leads on the available Math Index with 83.7, but that result should not be generalized into a broader claim about software engineering or agent reliability. Introducing GPT-5.5 positions GPT-5.5 around complex professional work, coding, tool-oriented agents, long-context retrieval, computer operation, knowledge work, and research. OpenAI’s GPT-5.5 usage guide also emphasizes explicit success criteria, stopping conditions, tool rules, and verification for long-running tasks. That guidance matters because the more autonomous the workflow, the more expensive an incorrect intermediate decision becomes. For production systems, GPT-5.5 (high) is the defensible baseline. GPT-5 nano (high) should enter through measured, bounded workloads with strong fallback and validation paths.

GPT-5.5 (high) is better supported for broad engineering and agentic work, while GPT-5 nano (high) has a narrow but notable mathematical signal. The Artificial Analysis Intelligence Index difference is 33.2 points, which suggests that GPT-5.5 (high) is more suitable when a task combines planning, interpretation, tool selection, and error recovery. A general intelligence score does not guarantee success on a specific repository, but it is more relevant to multi-step developer workflows than a single specialist score. GPT-5 nano (high)’s 83.7 Math Index score makes it worth testing for constrained numerical reasoning, formula transformation, or verification subroutines. It does not establish that the model can manage a codebase, follow a repository workflow, or safely edit unrelated files. The coding comparison is explicitly incomplete because the supplied data has no GPT-5 nano (high) coding score. Developers should not fill that gap with the model’s math result. GPT-5.5’s official announcement reports vendor-published results across coding, tool use, browsing, computer operation, and mathematical tasks, but those results are not independent reproductions. Introducing GPT-5.5 is therefore useful for understanding intended capability coverage, not for replacing a task-specific evaluation. The latency data shows a tie at 0.3 seconds, but output speed is unavailable for both models. That means the supplied evidence cannot determine which model feels faster during long responses, streaming generation, or tool-heavy runs. The practical performance question is consequently about correctness, recovery, and verification cost rather than response latency alone.

GPT-5 nano (high) minimizes token spend, but GPT-5.5 (high) may be cheaper at the workflow level when it prevents retries, repairs, and human review. The blended price is $0.1375 per 1M tokens for GPT-5 nano (high) and $11.25 for GPT-5.5 (high), making nano attractive for high-volume calls, classification-like steps, and low-risk transformations. That price advantage matters only if the smaller model completes the job within the first attempt and produces outputs that downstream systems can trust. A model that requires repeated prompting, additional validation calls, or manual correction can erase its nominal savings. The supplied research does not provide failure rates, retry rates, token usage distributions, or human-review costs, so no break-even calculation is justified. Input and output pricing also create different economics. GPT-5 nano (high) costs $0.05 per 1M input tokens and $0.4 per 1M output tokens, while GPT-5.5 (high) costs $5 and $30 respectively. Output-heavy workflows therefore expose a larger absolute price gap than input-heavy workflows. Developers should route simple, bounded work to nano only after measuring acceptance rates. GPT-5.5 (high) is easier to justify when each failed attempt can modify code, trigger tools, consume reviewer time, or delay a deployment. OpenAI’s API pricing documents current listed pricing, but it does not establish a price for GPT-5 nano (high). The official pricing page instead lists another nano model, so the supplied Artificial Analysis price should be treated as the comparison dataset’s value, not as confirmed current OpenAI billing guidance.

GPT-5.5 (high) should be the primary choice for repository-scale coding, tool-using agents, and ambiguous professional tasks. OpenAI’s GPT-5.5 usage guide recommends the Responses API for reasoning, tool calls, and multi-turn state, and documents controls for reasoning effort and response verbosity. Those controls support a deliberate production design, although higher reasoning effort does not automatically improve results. OpenAI specifically warns that conflicting instructions, weak stopping conditions, or overly open tool access can produce overthinking, unnecessary searching, or lower quality. GPT-5.5 (high) also has documented support for structured outputs, function calling, file search, web search, image input, code execution, computer use, MCP, and related tools on its model page. That breadth makes it a better fit for systems where the model must coordinate several capabilities. GPT-5.5 model page records that documented capability surface.

GPT-5 nano (high) should be considered for tightly specified subtasks, especially where the application can validate every output and the low blended price materially changes scale economics. The model’s 83.7 Math Index score supports testing in mathematical or numerical components, but the missing coding score prevents a confident recommendation for software generation. Do not promote it to a central coding agent based on price or math performance alone. The current official directory does not list GPT-5 nano, and the research found no reliable community evidence for its coding behavior, speed, or failure patterns. That uncertainty is itself a selection risk. Use a shadow evaluation, strict output schemas, bounded permissions, and a fallback to GPT-5.5 (high) before assigning nano production authority. Community reports about GPT-5.5 include large-file architecture, instruction drift, regressions, and premature completion, but the reports lack reproducible test methods. The Reddit discussion and OpenAI Developer Community discussion are signals for operational safeguards, not stable benchmark evidence.

GPT-5 nano (high) cannot be selected confidently for general development because its current API identity, limits, and coding evidence are unresolved. The official OpenAI Models page does not list GPT-5 nano, gpt-5-nano, or a dedicated snapshot in the supplied research. The same page provides only general statements about current model capabilities, which cannot be assigned specifically to GPT-5 nano (high). The official OpenAI API Pricing page also does not list gpt-5-nano. It lists a different nano model, and the research explicitly warns against transferring that model’s price or capability to GPT-5 nano. The supplied material also contains no context-window value, maximum-output value, output-speed value, or dedicated limitation list for GPT-5 nano (high). These omissions prevent a complete deployment comparison.

GPT-5.5 (high) has stronger documentation, but its production behavior still depends on instruction design and verification. The API Changelog documents the model’s release and caching limitation, while the usage guide explains that prompt design and tool constraints affect reliability. The evidence does not answer whether GPT-5.5 (high) wins on a developer’s private repository, nor whether GPT-5 nano (high) can outperform it on a narrow internal task. The correct next step is a matched evaluation using representative prompts, fixed acceptance criteria, failure capture, retry accounting, and reviewer effort. Until that evidence exists, the recommendation should remain conditional: GPT-5.5 (high) for broad responsibility, GPT-5 nano (high) for bounded experiments and validated low-risk work.

GPT-5.5 (high) is the safer starting point when a developer needs one model to cover broad reasoning and tool-oriented work. The available evidence is stronger for that role because OpenAI documents its model identity, capabilities, usage guidance, and intended professional scenarios. GPT-5 nano (high) remains interesting for narrow, heavily validated subtasks, especially mathematical ones, but its current official listing and coding evidence are missing. The questions below focus on decisions that the supplied benchmark and research material cannot answer by simple score comparison.