Code Davinci 001 vs Gemini Deep Research Preview
Code Davinci 001 (2021) and Gemini Deep Research Preview (2026) are agentic coding models from OpenAI and Google DeepMind. Code Davinci 001 ships a not-yet-sourced context window, while Gemini Deep Research Preview ships a 1M-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.
Gemini Deep Research Preview is safer overall; choose Code Davinci 001 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Code Davinci 001 | Gemini Deep Research Preview |
|---|---|---|
| Decision fit | Coding | RAG, Agents, and Long context |
| Context window | — | 1M |
| Cheapest output | - | $12/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 001 for Coding.
- Gemini Deep Research Preview has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini Deep Research Preview has broader tracked provider coverage for fallback and procurement flexibility.
- Gemini Deep Research Preview uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Gemini Deep Research Preview for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Code Davinci 001
Unavailable
No complete token price in local provider data
Gemini Deep Research Preview
$4,600
Cheapest tracked route: Google AI Studio
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Code Davinci 001 and Gemini Deep Research Preview; plan for SDK, billing, or endpoint changes.
- Gemini Deep Research Preview adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for Gemini Deep Research Preview and Code Davinci 001; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-07-01 | 2026-04-21 |
| Context window | — | 1M |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | 2025-01 |
Pricing and availability
| Pricing attribute | Code Davinci 001 | Gemini Deep Research Preview |
|---|---|---|
| Input price | - | $2/1M tokens |
| Output price | - | $12/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 001 | Gemini Deep Research Preview |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Gemini Deep Research Preview, multimodal input: Gemini Deep Research Preview, function calling: Gemini Deep Research Preview, tool use: Gemini Deep Research Preview, and structured outputs: Gemini Deep Research Preview. Both models share the core language-model surface, 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: Code Davinci 001 has no token price sourced yet and Gemini Deep Research Preview has $2/1M input tokens. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Code Davinci 001 when coding workflow support are central to the workload. Choose Gemini Deep Research Preview when vision-heavy evaluation 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.
FAQ
Is Code Davinci 001 or Gemini Deep Research Preview open source?
Code Davinci 001 is listed under Proprietary. Gemini Deep Research Preview 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, Code Davinci 001 or Gemini Deep Research Preview?
Gemini Deep Research Preview 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.
Which is better for multimodal input, Code Davinci 001 or Gemini Deep Research Preview?
Gemini Deep Research Preview 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 function calling, Code Davinci 001 or Gemini Deep Research Preview?
Gemini Deep Research Preview has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Code Davinci 001 or Gemini Deep Research Preview?
Gemini Deep Research Preview has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Code Davinci 001 and Gemini Deep Research Preview?
Code Davinci 001 is available on the tracked providers still being sourced. Gemini Deep Research Preview is available on Google AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.