Gemini Deep Research vs GPT-5.3-Codex
Gemini Deep Research (2024) and GPT-5.3-Codex (2026) compare a standalone API model against a coding-specialized model. Gemini Deep Research ships a 128k-token context window, while GPT-5.3-Codex ships a 400k-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Gemini Deep Research is standalone API model, while GPT-5.3-Codex is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Gemini Deep Research | GPT-5.3-Codex |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | tool-calling agents | custom coding agents, code generation, and tool loops |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 128k | 400k |
| Cheapest output | - | $14/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Gemini Deep Research for RAG, Agents, and Long context.
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Vision, Reasoning, and Code execution in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemini Deep Research
Unavailable
No complete token price in local provider data
GPT-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemini Deep Research and GPT-5.3-Codex; plan for SDK, billing, or endpoint changes.
- GPT-5.3-Codex adds Vision, Reasoning, and Code execution in local capability data.
- No overlapping tracked provider route is sourced for GPT-5.3-Codex and Gemini Deep Research; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Reasoning, and Code execution before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-11 | 2026-02-05 |
| Context window | 128k | 400k |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Openness | Proprietary | Proprietary |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-01 | 2025-08 |
Pricing and availability
| Pricing attribute | Gemini Deep Research | GPT-5.3-Codex |
|---|---|---|
| Input price | - | $1.75/1M tokens |
| Output price | - | $14/1M tokens |
| Providers |
Capabilities
| Capability | Gemini Deep Research | GPT-5.3-Codex |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | Yes |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-5.3-Codex, reasoning mode: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share function calling, tool use, and structured outputs, 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: Gemini Deep Research has no token price sourced yet and GPT-5.3-Codex has $1.75/1M input tokens. Provider availability is 1 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemini Deep Research when provider fit are central to the workload. Choose GPT-5.3-Codex when coding workflow support, larger context windows, 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. 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, Gemini Deep Research or GPT-5.3-Codex?
GPT-5.3-Codex supports 400k tokens, while Gemini Deep Research supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemini Deep Research or GPT-5.3-Codex open source?
Gemini Deep Research is listed under Proprietary. GPT-5.3-Codex 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, Gemini Deep Research or GPT-5.3-Codex?
GPT-5.3-Codex 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 reasoning mode, Gemini Deep Research or GPT-5.3-Codex?
GPT-5.3-Codex 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, Gemini Deep Research or GPT-5.3-Codex?
Both Gemini Deep Research and GPT-5.3-Codex 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 Gemini Deep Research and GPT-5.3-Codex?
Gemini Deep Research is available on Google AI Studio. GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-10. Data sourced from public model cards and provider documentation.