GPT-5 vs GPT-5.2: Which Model Should Developers Choose?

GPT-5.2 (xhigh) leads the available benchmark data, but GPT-5 is the safer production choice because its API identity, pricing, and capabilities are officially documented. The data brief gives GPT-5.2 an Artificial Analysis Intelligence Index score of 42.2, compared with 34.7 for GPT-5. GPT-5.2 also scores 99 on the Artificial Analysis Math Index, while GPT-5 scores 94.3. Those results suggest a meaningful advantage for difficult reasoning and mathematical work. They do not establish a complete product advantage. The data brief contains no GPT-5.2 coding score, output-speed result, context-window value, or verified official API price. OpenAI’s current model directory does not list GPT-5.2, gpt-5-2, or xhigh. By contrast, OpenAI documents GPT-5 as an API model with the stable alias gpt-5, a fixed snapshot, reasoning controls, tool support, and published pricing in its developer announcement and model documentation. The practical decision is therefore not simply a score comparison. It is a tradeoff between GPT-5.2’s stronger reported intelligence and math results and GPT-5’s substantially stronger evidence for deployability.

GPT-5 offers the stronger evidence base for shipping an OpenAI integration, while GPT-5.2 offers the stronger reported reasoning results. GPT-5 has an Artificial Analysis Coding Index score of 37.8, but the data brief does not provide a corresponding GPT-5.2 coding score. That missing value prevents a defensible conclusion about which model is better for software engineering. GPT-5.2 leads the available intelligence comparison at 42.2 versus 34.7 and the math comparison at 99 versus 94.3. The gap matters for workloads that reward multi-step reasoning, mathematical reliability, or difficult analysis. It does not automatically predict better repository edits, UI generation, agent persistence, or tool-use reliability. OpenAI positions GPT-5 for coding, reasoning, and agentic tasks in its developer announcement. The same announcement documents function calling, structured outputs, streaming, and custom tools. The GPT-5 model page also documents text and image input, text output, and the reasoning_effort options minimal, low, medium, and high. GPT-5.2 has no equivalent model-specific official documentation in the supplied research. Its current status must therefore be treated as uncertain, even though its data-brief scores are higher. For a new application, verification of the model ID should come before any quality or cost benchmark.

GPT-5.2 has the stronger measured result in the supplied intelligence and math comparisons, but GPT-5 has the broader documented evidence across developer workflows. The Artificial Analysis data reports GPT-5.2 at 42.2 on its Intelligence Index and 99 on its Math Index. GPT-5 reaches 34.7 and 94.3 on those same measures. The data also reports GPT-5 at 37.8 on the Coding Index, while GPT-5.2 has no value. That asymmetry is central to model selection. A higher general intelligence result can support harder planning and analysis, yet it cannot substitute for coding evidence when the application primarily edits, tests, or reviews software. OpenAI reports GPT-5 at 74.9% on SWE-bench Verified, 88% on Aider polyglot, 96.7% on τ²-bench telecom, and 69.6% on Scale MultiChallenge in its developer announcement. The announcement states that the SWE-bench result excluded 23 problems that could not reliably pass in OpenAI’s infrastructure, and that the Aider evaluation used high reasoning effort. These qualifications make the results useful but not universal. No supplied source provides an equivalent official benchmark profile for GPT-5.2. Developers should avoid turning the available GPT-5.2 advantage into a claim that it is better at every engineering task.

GPT-5.2 appears better for the measured reasoning and math tasks, while GPT-5 remains the only model with supplied coding evidence from both independent and official sources. The Artificial Analysis comparison places GPT-5.2 at 42.2 versus GPT-5 at 34.7 on its Intelligence Index. It also places GPT-5.2 at 99 versus GPT-5 at 94.3 on its Math Index. In a production system, that pattern could matter for requirements analysis, constraint-heavy planning, mathematical transformations, and verification steps. The likely benefit is fewer reasoning failures in tasks where the model must maintain several conditions at once. The result does not prove lower latency, better tool execution, or safer code changes. Both models have a reported median latency of 0.3 seconds, so the supplied data gives no speed winner. Median output tokens per second are unavailable for both models. GPT-5 has official support for function calling, structured outputs, streaming, and custom tools, according to the developer announcement and model documentation. GPT-5 also accepts text and image input but does not support audio or video input or output, according to the model documentation. GPT-5.2 lacks model-specific official capability documentation in the supplied research. Developers therefore have evidence for a reasoning advantage, but not enough evidence to assess its complete agent loop.

GPT-5 is easier to evaluate in coding workflows, but the supplied community evidence warns that strong small-task performance may not transfer to large application work. A Reddit author reported that GPT-5 was useful for locating and fixing small bugs in an existing production application. The same author judged its full application and UI generation output to be brief, with weaker completion and design detail. The post describes a subjective test in Cursor involving a React Native application, not a controlled benchmark. Other comments reported value for narrow debugging and modification tasks, while warning about hallucinations or incorrect changes in complex existing codebases. These observations come from the same Reddit discussion, and they should be treated as directional rather than representative. No reliable community report supplied in the research evaluates GPT-5.2 (xhigh) with a reproducible coding method. That absence is more important than the label xhigh, because the label does not establish an official model identity or a verified engineering behavior. For repository agents, the selection question is whether the model can inspect local conventions, make narrow edits, run checks, and recover from failures. The supplied materials do not answer that question for GPT-5.2. Teams should run their own task set before replacing a documented GPT-5 workflow with GPT-5.2.

GPT-5 is cheaper on every supplied price measure, but the lower token rate only saves money when its extra work does not erase the price advantage. The data brief lists GPT-5 at $3.4375 and GPT-5.2 at $4.8125 per 1M blended tokens. It also lists GPT-5 input at $1.25 and GPT-5.2 input at $1.75 per 1M tokens. Output pricing is $10 for GPT-5 and $14 for GPT-5.2 per 1M tokens. The chart below this section shows the complete comparison, so the more useful question is what causes actual spend. A model that solves a task in fewer attempts can be cheaper despite a higher token price. Conversely, a stronger reasoning score can encourage longer prompts, more verification, or repeated agent loops. The supplied materials do not report token consumption, retry rates, cache behavior, or task success cost for GPT-5.2. They therefore cannot show whether its higher benchmark results produce a lower cost per completed task. GPT-5’s official model documentation confirms the listed GPT-5 input, cached-input, and output prices. The research did not find a current official GPT-5.2 price. Developers should treat the Artificial Analysis GPT-5.2 price as comparison data, not as a confirmed OpenAI billing rate.

GPT-5 can be the more expensive operational choice if a reasoning gap causes repeated attempts, manual review, or failed downstream actions. GPT-5.2’s available Intelligence Index result is 42.2, compared with 34.7 for GPT-5, and its Math Index result is 99, compared with 94.3. Those scores suggest a possible reduction in errors for demanding reasoning tasks, but the research does not connect them to production token usage or completion rates. A financial comparison must therefore use cost per successful outcome, not price per token alone. The data brief supplies no success-cost measurement. GPT-5.2 also lacks a verified official price in the supplied sources, which creates a separate procurement risk. A team cannot build a reliable budget or quota policy around an unconfirmed model listing. GPT-5 has a known published price and a documented API alias, although the model documentation marks the fixed snapshot gpt-5-2025-08-07 as Deprecated and describes GPT-5 as a previous-generation model. That status may create migration work later. The cost decision therefore has two uncertainties: GPT-5 may require more task attempts, while GPT-5.2 may not be directly billable or stable under the supplied official documentation. Both risks require a small production-shaped pilot.

GPT-5 is the default recommendation for teams that need a documented API contract, while GPT-5.2 is a candidate for verified reasoning-heavy experiments. Choose GPT-5 for a production integration that depends on a confirmed model alias, published price, tool calling, structured outputs, streaming, or custom grammar-constrained tools. OpenAI documents those capabilities in its developer announcement and model documentation. Choose GPT-5.2 only after the team verifies that gpt-5-2 is callable in its target account, confirms billing, and measures its behavior on representative tasks. The current OpenAI model directory does not list GPT-5.2, gpt-5-2, or xhigh. The OpenAI pricing page also does not list gpt-5-2. GPT-5.2 is attractive for mathematical analysis, difficult planning, and other tasks aligned with its available scores of 42.2 and 99. GPT-5 remains more defensible for coding because it has a supplied Coding Index score of 37.8 and official coding-oriented positioning, while GPT-5.2 has no comparable coding value. Neither model has a supplied output-speed winner, since both show 0.3 seconds latency. The final recommendation is conditional: ship GPT-5 when operational certainty matters most, and test GPT-5.2 when measured reasoning quality justifies unresolved availability and pricing questions.

GPT-5 should remain the baseline until GPT-5.2 passes identity, billing, coding, and task-success checks in the target environment. Start with the current application’s real prompts, tool schemas, repositories, and review gates. Compare completed-task rate, invalid tool calls, rollback frequency, human correction, and total tokens. The supplied research provides no GPT-5.2 evidence for those measures, so a local evaluation is necessary. Keep GPT-5 as the control because its official documentation identifies the model and its supported API behavior. Test GPT-5.2 against the same workload rather than comparing isolated answers. A reasoning advantage is valuable only if it survives the full workflow. The fixed GPT-5 snapshot’s Deprecated status also means teams should separate two decisions: whether GPT-5 is the safer current baseline, and whether a migration plan is needed for long-term maintenance. OpenAI’s model documentation recommends a newer model family than GPT-5, but the supplied materials do not establish a documented replacement mapping for GPT-5.2. That is why this comparison supports a staged pilot instead of an unconditional upgrade. The evidence favors GPT-5.2 on available quality indicators and GPT-5 on integration certainty.

GPT-5 remains the verifiable reference point, while GPT-5.2 requires direct checks before production selection. The supplied research leaves several questions unanswered, including whether the gpt-5-2 identifier works in the target account, what official billing applies, and whether its reasoning advantage transfers to coding and agent workflows. The absence of evidence is itself a selection factor. Developers should document these unknowns in the evaluation plan and avoid presenting GPT-5.2 as a confirmed OpenAI product configuration. The OpenAI Models page and OpenAI Pricing page are the relevant official references for current availability and listed pricing. GPT-5’s documented limits also matter: the model documentation says fine-tuning and predicted outputs are unsupported, and its modality support excludes audio and video. These constraints can eliminate GPT-5 for specific architectures regardless of benchmark quality. The comparison is therefore strongest as a decision framework, not as proof that one model dominates every workload.