GPT-5.2 (medium) vs o3: Which OpenAI Model Should Developers Choose?

GPT-5.2 (medium) is the stronger measured capability choice, while o3 is the cheaper and better-documented performance choice in this snapshot.

The data favors GPT-5.2 (medium) on the Artificial Analysis Intelligence Index, at 38 versus 30.4 for o3. It also leads on the Artificial Analysis Math Index, at 96.7 versus 88.3. Those results make GPT-5.2 (medium) the more convincing candidate for tasks where reasoning quality or mathematical reliability drives the product outcome. Artificial Analysis provided the comparison data.

The commercial decision is less straightforward. o3 costs $3.5 per 1M blended tokens, compared with $4.8125 for GPT-5.2 (medium). o3 also has a reported median output speed of 128.056 tokens per second, while GPT-5.2 (medium) has no reported value in the supplied data. Both models have a latency value of 0.3 seconds.

The largest risk is not a benchmark gap. The current OpenAI Models directory does not list either model by the names evaluated here, and the current OpenAI Pricing page does not list either model’s current price. Treat the comparison as a selection signal, not proof of present API availability.

GPT-5.2 (medium) offers the better measured quality profile, but o3 offers the clearer efficiency case for workloads dominated by generated output.

Decision factor	GPT-5.2 (medium)	o3	Selection meaning
Intelligence Index	38	30.4	GPT-5.2 (medium) has the stronger aggregate capability signal
Math Index	96.7	88.3	GPT-5.2 (medium) is the stronger candidate for math-heavy reasoning
Blended price per 1M tokens	$4.8125	$3.5	o3 has the lower blended cost
Input price per 1M tokens	$1.75	$2	GPT-5.2 (medium) is cheaper for input-heavy traffic
Output price per 1M tokens	$14	$8	o3 is cheaper when responses consume most of the budget
Latency	0.3 seconds	0.3 seconds	The supplied data shows a tie
Median output speed	Not reported	128.056 tokens per second	o3 has the only reported throughput value

The practical winner depends on what the application pays for. A solver, analyst, or coding assistant may benefit more from GPT-5.2 (medium)'s higher evaluation scores than from o3’s lower output price. A high-volume workflow that produces long responses may prefer o3 because output tokens cost $8 rather than $14 per 1M tokens.

The input side reverses that price advantage. GPT-5.2 (medium) costs $1.75 per 1M input tokens, compared with $2 for o3. Applications that repeatedly send large documents, conversation histories, or retrieved context should model input volume separately instead of relying on the blended figure.

The official evidence remains incomplete. The OpenAI Models page provides general information about current models and does not establish that these exact model identifiers remain callable. The supplied research also found no reliable community tests, dedicated release announcement, or model-specific technical report for either comparison target.

GPT-5.2 (medium) has the stronger measured reasoning profile, while o3 has the only reported output-throughput result.

The most important capability difference appears in the evaluation indices. GPT-5.2 (medium) scores 38 on the Artificial Analysis Intelligence Index, compared with 30.4 for o3. Its Math Index is 96.7, compared with 88.3 for o3. These are not merely cosmetic differences for developers. A higher reasoning signal can reduce the number of retries, corrective prompts, validation passes, and human reviews required by a workflow.

That advantage matters most when an incorrect answer creates downstream work. Examples include generating a migration plan, checking a mathematical derivation, producing structured analysis, or writing code that must satisfy several constraints at once. The supplied data does not identify individual benchmark tasks, so it cannot prove that GPT-5.2 (medium) wins every coding, research, or production scenario. It does support treating GPT-5.2 (medium) as the better first candidate for quality-sensitive evaluation.

The speed evidence points in a different direction. o3 has a reported median output rate of 128.056 tokens per second. GPT-5.2 (medium) has no reported median output rate in the data. That missing value prevents a fair throughput comparison. It would be unsafe to infer that GPT-5.2 (medium) is slower simply because its value is absent.

The latency values are equal at 0.3 seconds, but latency alone does not describe the full user experience. Time to first token, sustained generation rate, response length, retries, and tool-call behavior can change perceived speed. The supplied materials do not provide those details. Developers should therefore test representative prompts, especially if the product streams long answers or triggers multiple model calls.

The documentation gap makes performance planning harder. The current OpenAI Models directory does not provide model-specific context limits, output limits, parameters, or dedicated benchmark results for either target. Developers cannot safely design around those missing constraints from the supplied sources.

o3 is the lower-cost default for blended and output-heavy traffic, while GPT-5.2 (medium) can be cheaper when input volume dominates.

The blended comparison favors o3 at $3.5 per 1M tokens versus $4.8125 for GPT-5.2 (medium). That figure is useful for an initial budget, but it hides the most important operational variable: the ratio between input and output tokens. The output prices are $8 for o3 and $14 for GPT-5.2 (medium), so long answers, code generation, explanations, and multi-step analysis can make GPT-5.2 (medium) materially more expensive even when request counts are identical.

Input-heavy systems reverse the direct price comparison. GPT-5.2 (medium) costs $1.75 per 1M input tokens, while o3 costs $2. A retrieval system that repeatedly sends large source packs may therefore spend less on GPT-5.2 (medium)'s input processing. The cheaper input rate does not automatically make the full request cheaper, because the answer still contributes output cost.

Quality can also change effective cost. If GPT-5.2 (medium)'s higher evaluation results reduce retries, repair prompts, manual checking, or fallback calls, its higher listed price may buy a lower cost per accepted result. The supplied data does not include error rates, retry rates, task completion rates, or production usage distributions. That means no reliable total-cost conclusion can be calculated beyond the listed token prices.

The strongest cost case for o3 is a predictable, output-heavy workload with modest quality sensitivity and stable access. The strongest cost case for GPT-5.2 (medium) is an input-heavy or quality-sensitive workload where fewer correction cycles matter. Developers should compare cost per completed task, not only cost per 1M blended tokens.

The official pricing evidence also needs caution. The current OpenAI Pricing page does not list either exact model identifier in the supplied research. The data snapshot supplies comparison prices, but it does not confirm that those prices remain directly purchasable today.

GPT-5.2 (medium) is the better quality-first pick, while o3 is the better efficiency-first pick if access is confirmed.

Choose GPT-5.2 (medium) for applications where reasoning quality has a direct effect on user trust or operational cost. Good candidates include mathematical assistance, complex planning, code generation with multiple constraints, document analysis, and workflows where an answer must pass review before release. Its Intelligence Index of 38 and Math Index of 96.7 provide the stronger measured case in this comparison.

Choose o3 for workloads that generate many tokens, need the lower blended price, or benefit from a reported output rate of 128.056 tokens per second. Examples include high-volume drafting, streaming interfaces, and automated transformations where the response is long but the acceptance criteria are relatively simple. Its $8 output price per 1M tokens also gives it a clear advantage for response-heavy traffic.

Use a two-stage evaluation before committing either model. First, verify that the exact identifier can be called in the intended account and endpoint. Second, run representative tasks with acceptance tests that measure correctness, retry frequency, response length, latency, and total tokens. The supplied research does not provide stable aliases, current endpoints, context windows, output limits, or model-specific API parameters for either target.

The evidence does not support a confident claim about production stability, migration direction, or long-term support. The current OpenAI Models directory does not list the evaluated names, and the research found no reliable community reports or dedicated official release material. If availability cannot be confirmed, neither model should become a hard-coded production dependency.

The final decision is therefore conditional. Pick GPT-5.2 (medium) when accepted-answer quality is the scarce resource. Pick o3 when output cost and measured throughput matter more. Keep the choice behind a configurable model setting until official availability is verified.

GPT-5.2 (medium) is the stronger starting point for quality-sensitive testing, while o3 is the stronger starting point for cost-sensitive testing.

The supplied sources do not answer several production questions directly. They do not establish a current context window, maximum output, stable alias, endpoint, or confirmed availability for either model. They also do not provide reliable community measurements of coding quality, failure modes, or operational behavior. Those unknowns should be treated as release-blocking checks for applications that need predictable deployment.

The data still supports a useful test order. Start with GPT-5.2 (medium) for tasks where mathematical or general reasoning quality matters. Start with o3 for long-output or high-volume tasks where token economics matter. Then validate the winning candidate against real prompts and real acceptance criteria before changing production traffic.

The comparison data is attributed to Artificial Analysis. Official product and pricing verification should use the linked OpenAI documentation rather than relying on the snapshot alone.