OpenAI o3 at a glance
OpenAI describes o3 as a reasoning model for complex, multi-step tasks involving text, code, and images. Its model-page information highlights math, science, coding, visual reasoning, technical writing, and instruction-following. That same information marks o3 Deprecated and says it was succeeded by GPT-5. The snapshot o3-2025-04-16 is also marked deprecated.
Practical takeaway: don’t make o3 the default for a new production model selection based on its capability description alone. Compare current alternatives, including the named successor, against your workload. The status shown in the referenced model-page information is not a migration requirement, nor does it establish that GPT-5 is best for every use case. Confirm current availability and any migration guidance before making a decision. [OpenAI API model page]
When o3 fits—and when it does not
The documentation’s task descriptions make o3 a candidate to evaluate for work that requires multi-step reasoning over text, code, or images—for example, a technical analysis that combines a written problem with a diagram. These descriptions indicate intended strengths; they are not guarantees of accuracy or benchmark results for your specific application.
| Documented characteristic | What it means for selection |
|---|---|
| Text input and output | Suitable to evaluate for text-based tasks. |
| Image input; no image output listed | Can be evaluated on visual inputs, but don’t select it to generate images. |
| Audio and video not supported | Rule it out if those inputs are required in the documented model interface. |
| 200,000-token context window; 100,000 maximum output tokens | Consider whether your inputs and requested outputs fit within the listed limits. The page does not explain whether settings affect them. |
| June 1, 2024 knowledge cutoff | Don’t rely on model knowledge alone for facts that need to be current. |
| Reasoning: “Highest”; speed: “Slowest” | Treat these as qualitative labels from the page, not measured latency or a guarantee that o3 will be slowest for your workload. |
If quality or response time is important to your decision, test representative tasks with the models you are considering. The available documentation does not provide task-specific accuracy results or latency measurements.
Production API fit and constraints
The model page lists Chat Completions (v1/chat/completions) and Responses (v1/responses). It marks streaming, function calling, and structured outputs as supported, and fine-tuning and predicted outputs as not supported. It also notes reasoning-token support but gives no configuration or behavior details in the captured information. [OpenAI API model page]
These feature labels help narrow an evaluation; they are not a complete integration guide. The page information does not provide authentication instructions, request or response examples, error handling, or endpoint-specific implementation guidance. Although it lists other API paths, that list alone does not establish that every endpoint is compatible with o3. Confirm compatibility and production requirements in the current documentation for the endpoint you plan to use. This guide is not an API setup tutorial.
API pricing and operating limits
The referenced model-page information lists the following text-token prices per 1 million tokens. It uses dollar signs; the information available here does not establish the applicable currency, region, or effective date.
| Price category | Standard API price shown | Batch API price shown |
|---|---|---|
| Input | $2.00 | $1.00 |
| Cached input | $0.50 | $0.25 |
| Output | $8.00 | $4.00 |
The standard and Batch API prices appear in separate price blocks; don’t treat them as interchangeable. The page also says token use or other model-specific metrics determine pricing. Verify current rates and applicable terms before estimating production spend.
Illustrative calculation: assuming 1 million input tokens and 100,000 output tokens at the listed standard rates, the token charge would be $2.80: $2.00 for input plus $0.80 for output. If all 1 million input tokens qualified for the listed cached-input rate, the calculation would be $1.30: $0.50 plus $0.80. These are arithmetic examples using the listed rates—not a quote or a prediction of how much of a real request will be cached. The equivalent calculation using the listed Batch prices would be $1.40 for uncached input and output. Confirm current prices and eligibility before relying on any estimate.
The captured rate-limit table shows these values:
| Tier | RPM | TPM | Batch queue limit |
|---|---|---|---|
| Free | Not supported | — | — |
| Build | 5,000 | 450,000 | 1,350,000 |
| Launch | 10,000 | 2,000,000 | 200,000,000 |
| Grow | 10,000 | 30,000,000 | 5,000,000,000 |
RPM means requests per minute and TPM means tokens per minute. The referenced information says limits depend on usage tier and can increase as API usage and spending increase. It does not specify the batch queue limit’s time unit, and these values should not be assumed to apply to a particular account today. Check the current limits for your organization and intended tier before planning throughput.
Selection takeaway
For an existing o3 integration, assess whether its documented text-and-image reasoning, feature support, price structure, and limits still fit the workload—and confirm the model remains available for your use. For a new selection, its listed deprecated status is a reason to evaluate current alternatives rather than defaulting to o3; it is not, by itself, a mandate to migrate. In either case, verify current status, endpoint compatibility, prices, and account-specific limits, then test the workload if quality or latency will determine the choice.