GPT-5.2 (medium)

GPT-5.2 (medium) looks compelling for mathematical workloads, but its undocumented product status weakens the case for general production adoption. Artificial Analysis places the model fourth among 265 models on its Math Index, with a score of 96.7. That is the clearest positive signal in the available evidence. The same model ranks 77 of 578 on the broader Intelligence Index, with a score of 38. Those two positions describe a focused profile rather than a uniformly dominant one.

The evaluation data supports choosing GPT-5.2 (medium) for tasks where mathematical correctness is central. It does not support assuming equally strong performance across coding, research, agentic work, or general reasoning. The data brief contains no dedicated coding score, no output-throughput measurement, and no official technical report for this model.

The product evidence is also incomplete. OpenAI’s current model directory does not list GPT-5.2 (medium) or gpt-5-2-medium as a dedicated entry. OpenAI’s pricing documentation also does not list the model. Developers therefore have a measurable benchmark signal, but not a complete deployment contract.

For evaluation teams, that combination matters. GPT-5.2 (medium) may be worth testing behind an abstraction layer. It is harder to recommend as a default model for a new system until availability, context limits, API parameters, and support status are confirmed.

GPT-5.2 (medium) is best treated as a high-performing math candidate, not a verified general-purpose production default. Its Math Index position is unusually strong, while its broader Intelligence Index position is much less distinctive. The gap between those rankings should shape the test plan.

Decision area	What the evidence supports	What remains uncertain
Mathematical tasks	Strong candidate, because the model ranks 4 of 265 on the Math Index at 96.7	Transfer to a specific application is untested
General reasoning	Usable only with task-specific validation, because the model ranks 77 of 578 on the Intelligence Index at 38	Coding and agent performance are not supplied
Responsiveness	A 0.3s time to first token is available in the data brief	Output tokens per second are not reported
Procurement	The blended price is available from the supplied data	OpenAI’s public pricing page does not list the model
Platform risk	The model should be tested with fallback routing	Official availability and successor status are not confirmed

OpenAI’s model documentation describes current model capabilities at a general level, including text and image inputs, text output, multilingual ability, and vision. That page does not establish that every described capability applies to GPT-5.2 (medium). Developers should separate model-level benchmark evidence from platform-level guarantees.

The practical conclusion is narrow but useful: shortlist GPT-5.2 (medium) for mathematics-heavy workflows, then require an availability check and task-specific acceptance tests before committing to it.

GPT-5.2 (medium) deserves serious testing for mathematics-heavy workflows, but its broad benchmark position does not justify universal adoption. A rank of 4 of 265 on the Artificial Analysis Math Index places it near the front of that measured field. In practical terms, that makes the model a credible candidate for symbolic manipulation, quantitative explanation, equation solving, and mathematical verification. The benchmark does not prove that every answer will be correct, nor does it reveal how the model behaves with ambiguous requirements, long codebases, external tools, or production-specific constraints.

The broader Intelligence Index gives a different signal. GPT-5.2 (medium) ranks 77 of 578 at 38. That position is sufficient to keep the model in consideration for general reasoning, but it is not a strong reason to choose the model over every nearby option. A developer building a mixed workload should expect the math advantage to matter most when the request contains a clear quantitative core. The advantage may matter less for writing, retrieval, orchestration, or user-interface tasks.

The neighboring data also suggests that benchmark ties need context. GLM-5-Turbo and GPT-5.6 Luna (medium) each show an Intelligence Index score of 38.1, while GPT-5.2 (medium) shows 38. The supplied data does not establish meaningful differences among these models for a particular application. MiniMax-M2.7 also shows 38 on that index and includes a coding score of 52.6, but GPT-5.2 (medium) has no comparable coding score in the brief. Claude Opus 4.6 (Non-reasoning, High Effort) and Gemini 3 Flash Preview (Reasoning) each show 37.8, with Gemini also showing a Math Index score of 97.

Latency changes the user experience, but it does not resolve the larger evidence gap. GPT-5.2 (medium) has a reported 0.3s time to first token. Output tokens per second are unavailable, so the data cannot establish whether long answers stream quickly or finish slowly. This distinction matters for coding assistants, interactive tutoring, and agent loops.

The most important missing evidence is official and behavioral. OpenAI provides no dedicated entry for this model in its model directory, and the brief found no reliable community posts with disclosed testing methods. There is therefore no supported claim about context window, maximum output, API parameters, tool behavior, failure modes, or coding experience. A responsible evaluation should test those properties directly rather than infer them from the Math Index.

GPT-5.2 (medium) can be economically sensible for high-value mathematical work, but its supplied price is difficult to justify for undifferentiated general usage. The data brief lists a blended price of $4.8125 per 1M tokens, with input priced at $1.75 per 1M tokens and output priced at $14 per 1M tokens. The output rate is the main commercial constraint because verbose answers, repeated reasoning, and tool-heavy workflows can increase spend quickly.

The price should be judged against workload shape, not against the model’s name. A service that uses short prompts and concise mathematical answers may capture the model’s strongest evidence without generating excessive output. A broad assistant that handles routine classification, summarization, retrieval, and simple drafting may pay for capabilities it does not need. The supplied neighboring models show lower blended prices, including GPT-5.6 Luna (medium) at $0.45 and MiniMax-M2.7 at $0.525. Those figures make GPT-5.2 (medium) a poor default if the application has no demonstrated need for its mathematical profile.

Cost can also flip when error reduction has a measurable business value. If a better mathematical answer prevents manual review, failed calculations, or expensive downstream retries, the higher output price may be acceptable. The data brief does not provide task-level accuracy, token consumption, or failure costs, so that business case cannot be proven from the supplied evidence alone.

Commercial risk is separate from token economics. OpenAI’s current pricing page does not list gpt-5-2-medium, and the research brief found no official confirmation that the model remains directly callable. Developers should verify the endpoint, billing behavior, aliases, quota treatment, and deprecation policy before forecasting spend.

The sensible cost test is a measured workload replay. Compare total task cost, correction effort, and fallback frequency against a cheaper neighboring model. Do not select GPT-5.2 (medium) because its headline price looks moderate. Select it only if the Math Index profile produces a business result that cheaper options cannot match.

GPT-5.2 (medium) is worth a controlled pilot for mathematics-first products, but developers should avoid making it an unverified system dependency. The recommendation follows from two facts: the model ranks 4 of 265 on the Math Index, and OpenAI’s public documentation does not provide a dedicated model entry or price listing for it.

Choose GPT-5.2 (medium) when the product needs quantitative reasoning, mathematical explanation, or a second-pass mathematical checker. Keep prompts focused, constrain output where possible, and measure correction rates on representative tasks. The 0.3s time to first token also makes interactive testing reasonable, although missing output-throughput data prevents a complete responsiveness judgment.

Use a different default when the workload is mostly routine text, general application logic, high-volume generation, or coding. The broader Intelligence Index rank of 77 of 578 is not enough to establish a general advantage. Lower-priced neighboring models may offer a better operating margin, especially when the application can tolerate small quality differences. Their benchmark values are useful as reference points, not as proof of superiority for a specific workflow.

A production decision should pass three gates:

1. Confirm that the model is callable through an official, stable path. OpenAI’s model directory currently does not provide that confirmation.
2. Run a task suite covering correctness, refusal behavior, formatting, context handling, retries, and tool integration. The research brief contains no reliable model-specific failure study.
3. Compare total cost and human correction effort with at least one cheaper alternative. The official pricing page does not currently document this model’s commercial terms.

Until those gates pass, GPT-5.2 (medium) belongs in a benchmarked candidate pool, not at the center of a new architecture.

GPT-5.2 (medium) is a specialized candidate whose strongest evidence is mathematical ranking, while its production identity remains insufficiently documented. Developers should answer the following questions before integrating it.