GPT-5.3-Codex vs o3 Deep Research
GPT-5.3-Codex (2026) and o3 Deep Research (2025) compare a coding-specialized model against a standalone API model. GPT-5.3-Codex ships a 400k-token context window, while o3 Deep Research ships a 200k-token context window. On pricing, GPT-5.3-Codex costs $1.75/1M input tokens versus $10/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.3-Codex is coding-specialized model, while o3 Deep Research is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | o3 Deep Research |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | RAG, Agents, and Long context |
| Context window | 400k | 200k |
| Cheapest output | $14/1M tokens | $40/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has the lower cheapest tracked output price at $14/1M tokens.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Code execution and Computer use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- o3 Deep Research uniquely exposes Multimodal in local model data.
- Local decision data tags o3 Deep Research for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
o3 Deep Research
$18,000
Cheapest tracked route/tier: Vercel AI Gateway
Estimated monthly gap: $13,100. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- o3 Deep Research is $26/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution and Computer use before moving production traffic.
- o3 Deep Research adds Multimodal in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.3-Codex is $26/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Code execution and Computer use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-10-10 |
| Context window | 400k | 200k |
| Parameters | — | — |
| Architecture | Decoder Only | Decoder Only |
| License | Proprietary | Proprietary |
| Openness | Proprietary | Proprietary |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2025-08 | 2024-06 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | o3 Deep Research |
|---|---|---|
| Input price | $1.75/1M tokens | $10/1M tokens |
| Output price | $14/1M tokens | $40/1M 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 |
| IDE integration | No | No |
| Computer use | Yes | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available 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.
For cost, GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider, while o3 Deep Research lists $10/1M input and $40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-5.3-Codex lower by about $13.57 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support, larger context windows, and lower input-token cost 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.
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.
Which is cheaper, GPT-5.3-Codex or o3 Deep Research?
GPT-5.3-Codex is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. o3 Deep Research costs $10/1M input and $40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
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.
Where can I run GPT-5.3-Codex and o3 Deep Research?
GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. o3 Deep Research is available on Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.