GPT-5.2 vs o3
GPT-5.2 (2025) and o3 (2025) are frontier-tier reasoning models from OpenAI. GPT-5.2 ships a 256K-token context window, while o3 ships a 128K-token context window. On SWE-bench Verified, GPT-5.2 leads by 8.3 pts. On pricing, o3 costs $1/1M input tokens versus $1.75/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
o3 is ~75% cheaper at $1/1M; pay for GPT-5.2 only for coding workflow support.
Specs
Pricing and availability
| GPT-5.2 | o3 | |
|---|---|---|
| Input price | $1.75/1M tokens | $1/1M tokens |
| Output price | $14/1M tokens | $4/1M tokens |
| Providers |
Capabilities
| GPT-5.2 | o3 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GPT-5.2 | o3 |
|---|---|---|
| SWE-bench Verified | 80.0 | 71.7 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has GPT-5.2 at 80 and o3 at 71.7, with GPT-5.2 ahead by 8.3 points. The largest visible gap is 8.3 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: GPT-5.2, multimodal input: GPT-5.2, function calling: GPT-5.2, and tool use: GPT-5.2. Both models share reasoning mode, structured outputs, and code execution, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GPT-5.2 lists $1.75/1M input and $14/1M output tokens, while o3 lists $1/1M input and $4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o3 lower by about $3.52 per million blended tokens. Availability is 2 providers versus 3, so concentration risk also matters.
Choose GPT-5.2 when coding workflow support and larger context windows are central to the workload. Choose o3 when coding workflow support, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, GPT-5.2 or o3?
GPT-5.2 supports 256K tokens, while o3 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.2 or o3?
o3 is cheaper on tracked token pricing. GPT-5.2 costs $1.75/1M input and $14/1M output tokens. o3 costs $1/1M input and $4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.2 or o3 open source?
GPT-5.2 is listed under Proprietary. o3 is listed under Unknown. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GPT-5.2 or o3?
GPT-5.2 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.2 or o3?
GPT-5.2 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-5.2 and o3?
GPT-5.2 is available on Replicate API and OpenRouter. o3 is available on OpenAI API, OpenRouter, and OpenAI Batch API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.