GPT-5.2: Features, Pricing, and Coding Use
GPT-5.2 is an OpenAI reasoning model for professional work, coding, and structured problem solving. It accepts text and images, returns text, and lets API users adjust reasoning effort. It is a previous-generation frontier model. Its price, 400K context, and model ID may still fit an existing workload.
GPT-5.2 vs GPT-5.4 vs GPT-5.5
The three models differ most clearly in context capacity and token price. GPT-5.4 and GPT-5.5 expand the context window to 1.05M tokens, while all three allow up to 128K output tokens. Current model selection has moved to GPT-5.6, which makes this comparison more useful for maintaining or migrating an existing integration than for treating these three as the complete model catalog.
| Selection factor | GPT-5.2 | GPT-5.4 | GPT-5.5 |
|---|---|---|---|
| Context window | 400K tokens | 1.05M tokens | 1.05M tokens |
| Maximum output | 128K tokens | 128K tokens | 128K tokens |
| Input per 1M tokens | $1.75 | $2.50 | $5.00 |
| Output per 1M tokens | $14.00 | $15.00 | $30.00 |
| Reasoning effort | None to xhigh | None to xhigh | None to xhigh |
Choose with representative prompts, not version numbers alone. A 1.05M window matters only when the application can use that extra context without filling it with irrelevant files, duplicated logs, or stale requirements.
GPT-5.2 Cost Comparison
For standard token rates, this model costs less than GPT-5.4 and GPT-5.5 on both input and output. Its cached-input rate is $0.175 per million tokens, compared with $0.25 for GPT-5.4 and $0.50 for GPT-5.5.
That does not make it the lowest-cost OpenAI option across the current catalog. Smaller and newer cost-focused models can have different price and capability profiles. Estimate a complete task rather than one request: include retries, tool calls, cached context, reasoning output, and the engineering time needed to review the result.
Coding Performance and Evaluation
The model supports the Responses API, Chat Completions, Batch, streaming, function calling, and structured outputs. It is separate from GPT-5.2-Codex, a coding-specific model line, so Codex-specific claims should not be assigned to the gpt-5.2 endpoint.
Use a fixed repository snapshot and acceptance test to compare models and reasoning levels:
- Bug repair: "Reproduce the failing test, identify the smallest safe change, apply it, and show the passing test."
- Refactor: "Split this module without changing its public API, then run the relevant unit and type checks."
- Migration plan: "Map dependencies, flag rollback points, and stop for approval before changing files."
Track task success, regression count, latency, input and output tokens, and human review time. A coding result is useful only when it survives the same tests and review rules used for human changes.
Use Cases for GPT-5.2
This model can still make sense when an application already uses its snapshot, needs up to 400K context, or relies on its combination of reasoning controls and structured outputs. Candidate workloads include code analysis, document synthesis, migration planning, function-calling workflows, and batch classification with nontrivial reasoning.
It is a weaker fit when the task requires more than 400K context, a lower-cost high-volume tier, audio input or output, video input, fine-tuning, or a feature documented only for a different model. Text and image input are supported; audio, video, fine-tuning, and predicted outputs are not.
GPT-5.2 API Pricing and Access
The base API rate is $1.75 per million input tokens, $0.175 per million cached input tokens, and $14 per million output tokens. Tool-specific charges can be separate. The model supports none, low, medium, high, and xhigh reasoning effort, with none as the documented default.
For repeatable behavior, the dated snapshot is gpt-5.2-2025-12-11. Test the snapshot and alias separately before migration, and confirm account limits for the endpoint you plan to use.
Checking GPT-5.2 Access in Verdent
This model is not listed in Verdent's current built-in model catalog. Open the current built-in lineup and confirm the ID before assigning a task. Verdent also documents OpenAI BYOK configuration, but the available models still depend on what the provider and selector expose to your account.
Verify the Model ID Before Reusing an Integration
Start with the built-in catalog, then verify any BYOK model in the selector before moving a production workflow. Keep the repository task, reasoning level, and acceptance tests fixed when comparing a replacement.
Check enabled Verdent model IDs Configure an OpenAI key in Verdent
Frequently Asked Questions
Is GPT-5.2 still a current frontier model?
No. GPT-5.2 is a previous-generation frontier model. New projects should compare it with GPT-5.6. The gpt-5.2 model remains listed in OpenAI's API documentation.
What are the context and output limits?
GPT-5.2 has a 400,000-token context window and supports up to 128,000 output tokens. Practical usage also depends on account limits, latency, and the relevance of the supplied context.
How much does the API cost?
The listed standard rate is $1.75 per million input tokens, $0.175 per million cached input tokens, and $14 per million output tokens. Separate tool charges may apply.
Is GPT-5.2 built into Verdent?
It is not listed in Verdent's current built-in catalog. Check the model catalog first, then inspect the OpenAI BYOK selector if you need this exact model ID.