GPT-5.3-Codex vs o3 Mini
GPT-5.3-Codex (2026) and o3 Mini (2025) are agentic coding models from OpenAI. GPT-5.3-Codex ships a not-yet-sourced context window, while o3 Mini ships a not-yet-sourced context window. On pricing, o3 Mini costs $1.1/1M 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 Mini is ~59% cheaper at $1.1/1M; pay for GPT-5.3-Codex only for coding workflow support.
Specs
| Released | 2026-02-05 | 2025-03-31 |
| Context window | — | — |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Unknown |
| Knowledge cutoff | - | 2025-04 |
Pricing and availability
| GPT-5.3-Codex | o3 Mini | |
|---|---|---|
| Input price | $1.75/1M tokens | $1.1/1M tokens |
| Output price | $14/1M tokens | $4.4/1M tokens |
| Providers |
Capabilities
| GPT-5.3-Codex | o3 Mini | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: GPT-5.3-Codex and reasoning mode: o3 Mini. Both models share function calling, tool use, 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-5.3-Codex lists $1.75/1M input and $14/1M output tokens, while o3 Mini lists $1.1/1M input and $4.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o3 Mini lower by about $3.33 per million blended tokens. Availability is 1 providers versus 3, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support are central to the workload. Choose o3 Mini 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which is cheaper, GPT-5.3-Codex or o3 Mini?
o3 Mini is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. o3 Mini costs $1.1/1M input and $4.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or o3 Mini open source?
GPT-5.3-Codex is listed under Proprietary. o3 Mini 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 multimodal input, GPT-5.3-Codex or o3 Mini?
GPT-5.3-Codex 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.
Which is better for reasoning mode, GPT-5.3-Codex or o3 Mini?
o3 Mini has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, GPT-5.3-Codex or o3 Mini?
Both GPT-5.3-Codex and o3 Mini expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GPT-5.3-Codex and o3 Mini?
GPT-5.3-Codex is available on OpenRouter. o3 Mini is available on OpenRouter, OpenAI Batch API, and Azure OpenAI. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.