Quick answer
GPT-5.2 is an OpenAI model for complex professional work, and it is available through the OpenAI API. The documented API model ID is gpt-5.2; the listed snapshot is gpt-5.2-2025-12-11. OpenAI’s API model page describes GPT-5.2 as a previous flagship. It may suit work that benefits from its documented capabilities or an existing integration; for a new deployment, compare it with current alternatives using your tasks and budget. (developers.openai.com)
OpenAI’s launch announcement said GPT-5.2 Instant, Thinking, and Pro were rolling out in ChatGPT and that the variants were available in the API. The API model page documents gpt-5.2 and the dated snapshot as model identifiers; don’t assume a ChatGPT label is itself a distinct API ID without checking the current model documentation. (openai.com)
What GPT-5.2 is good for
OpenAI positioned GPT-5.2 for professional knowledge work, including creating spreadsheets and presentations, writing code, understanding images and long contexts, using tools, and handling complex multi-step projects. These are workload areas to evaluate, not a guarantee of better results on your particular application. (openai.com)
OpenAI’s launch benchmarks offer context, but benchmark scores are not production success rates. It reported GPT-5.2 Thinking at 70.9% wins or ties on GDPval tasks spanning 44 occupations, 55.6% on public SWE-Bench Pro, 80.0% on SWE-bench Verified, and 92.4% on GPQA Diamond without tools. OpenAI also reported 100.0% on AIME 2025 without tools and 88.7% on CharXiv Reasoning with Python. These are OpenAI-reported results; compare models only when evaluation setups, tools, settings, and tasks match the decision you need to make. (openai.com)
Workload-based choice
- Complex reasoning, coding, or document analysis: GPT-5.2 is a reasonable candidate to benchmark, particularly if an existing workflow already uses it. Measure whether it completes your actual tasks accurately enough to justify its cost and latency.
- Image input with text output: The model page lists text and image input, with text output. It lists audio and video as unsupported for GPT-5.2. If your application depends on audio or video, choose a model whose current documentation explicitly supports those inputs. (
developers.openai.com) - Structured or tool-assisted applications: The model page lists function calling and structured outputs as supported. Check the applicable API documentation and test the exact tool flow, schema, and failure handling your application needs. (
developers.openai.com) - New applications: Compare GPT-5.2 with current alternatives instead of assuming a previous flagship remains the best fit. Choose based on the quality, speed, and budget your workload requires.
GPT-5.2 API specifications
The OpenAI API model page lists these details for GPT-5.2:
| Item | Documented detail |
|---|---|
| API model ID | gpt-5.2 |
| Snapshot | gpt-5.2-2025-12-11 |
| Supported API interfaces listed | Responses (v1/responses) and Chat Completions (v1/chat/completions) |
| Context window | 400,000 tokens |
| Maximum output | 128,000 tokens |
| Input | Text and images |
| Output | Text |
| Reasoning effort | none (default), low, medium, high, xhigh |
| Listed features | Streaming, function calling, structured outputs |
| Listed as unsupported | Audio input, audio output, video input, fine-tuning, predicted outputs |
These are model-page specifications; check the current documentation for the specific model and interface before deployment. API rate limits depend on your usage tier. The 400,000-token context window is the documented API limit—not a third-party benchmark result. (developers.openai.com)
GPT-5.2 API pricing
The OpenAI model page lists API token prices per 1 million tokens:
- Input: $1.75
- Cached input: $0.175
- Output: $14.00
These are API rates, not ChatGPT subscription prices. Check the current official model and pricing pages before budgeting or deployment because rates and terms can change. (developers.openai.com)
Worked estimate: Suppose one request uses 100,000 uncached input tokens and generates 20,000 output tokens. At the listed rates, input costs 0.1 × $1.75 = $0.175 and output costs 0.02 × $14 = $0.28, for an estimated $0.455 per request. This hypothetical excludes cached input, retries, tool charges, and any other applicable charges. Actual cost depends on token use and request pattern.
A third-party article quoted GPT-5.2 input pricing alongside a competitor price expressed in a different, unclear token unit. That is not a reliable like-for-like comparison. Compare current official rates on equivalent input, cached-input, and output units, then estimate cost using your expected workload. (developers.openai.com)
A practical production-selection checklist
Before choosing GPT-5.2 for a production integration:
- Confirm the model identifier and interface. Use the documented API ID or a pinned snapshot if you need consistent behavior across deployments. (
developers.openai.com) - Check modality and feature fit. Verify the inputs, outputs, tools, and structured-output behavior your product requires; don’t infer API support from a ChatGPT model label. (
developers.openai.com) - Test representative tasks. Compare task success, factual or schema errors, latency, and human-review needs on the same sample set and settings. This is an evaluation plan, not a test performed for this article.
- Estimate cost from actual token use. Include input, cached input where applicable, output, and relevant tool usage. A lower input rate alone does not establish lower total cost.
- Compare a current alternative. GPT-5.2 remains a documented API option, but its model page calls it a previous flagship. Benchmark it against current models before committing to a new build. (
developers.openai.com)
Bottom line: GPT-5.2 has documented API availability, model identifiers, supported text-and-image input, a 400,000-token context window, and published per-token prices. It may fit workloads that benefit from its documented capabilities or an existing integration, but it is a previous flagship. Make the choice using current documentation and a like-for-like evaluation of your quality, latency, and cost requirements.