GPT-5.3-Codex vs o3 Deep Research
GPT-5.3-Codex (2026) and o3 Deep Research (2026) are agentic coding models from OpenAI. GPT-5.3-Codex ships a 400K-token context window, while o3 Deep Research ships a 200K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
GPT-5.3-Codex is safer overall; choose o3 Deep Research when vision-heavy evaluation matters.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2026-01-01 |
| Context window | 400K | 200K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | o3 Deep Research |
|---|---|---|
| Input price | $1.75/1M tokens | - |
| Output price | $14/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GPT-5.3-Codex | o3 Deep Research |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: o3 Deep Research and code execution: GPT-5.3-Codex. Both models share vision, reasoning mode, function calling, and tool use, 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.
Pricing coverage is uneven: GPT-5.3-Codex has $1.75/1M input tokens and o3 Deep Research has no token price sourced yet. Provider availability is 2 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-5.3-Codex when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose o3 Deep Research when vision-heavy evaluation 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, GPT-5.3-Codex or o3 Deep Research?
GPT-5.3-Codex supports 400K tokens, while o3 Deep Research supports 200K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GPT-5.3-Codex or o3 Deep Research open source?
GPT-5.3-Codex is listed under Proprietary. o3 Deep Research is listed under Proprietary. 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-5.3-Codex or o3 Deep Research?
Both GPT-5.3-Codex and o3 Deep Research expose vision. 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.
Which is better for multimodal input, GPT-5.3-Codex or o3 Deep Research?
o3 Deep Research 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 Deep Research?
Both GPT-5.3-Codex and o3 Deep Research expose reasoning mode. 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 Deep Research?
GPT-5.3-Codex is available on OpenRouter and OpenAI API. o3 Deep Research is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.