Code Davinci 001 vs Gemini 2.5 Pro
Code Davinci 001 (2021) and Gemini 2.5 Pro (2025) are agentic coding models from OpenAI and Google DeepMind. Code Davinci 001 ships a not-yet-sourced context window, while Gemini 2.5 Pro 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. The goal is to make the tradeoff clear before deeper testing.
Gemini 2.5 Pro is safer overall; choose Code Davinci 001 when coding workflow support matters.
Specs
| Released | 2021-07-01 | 2025-06-17 |
| Context window | — | 1M |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | 2025-01 |
Pricing and availability
| Code Davinci 001 | Gemini 2.5 Pro | |
|---|---|---|
| Input price | - | $1.25/1M tokens |
| Output price | - | $10/1M tokens |
| Providers | - |
Capabilities
| Code Davinci 001 | Gemini 2.5 Pro | |
|---|---|---|
| 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 vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, tool use: Gemini 2.5 Pro, structured outputs: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. 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 2.5 Pro has $1.25/1M input tokens. Provider availability is 0 tracked routes versus 3. 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 2.5 Pro when coding workflow support 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 2.5 Pro open source?
Code Davinci 001 is listed under Proprietary. Gemini 2.5 Pro 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 2.5 Pro?
Gemini 2.5 Pro 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 2.5 Pro?
Gemini 2.5 Pro 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 2.5 Pro?
Gemini 2.5 Pro 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 2.5 Pro?
Gemini 2.5 Pro 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 2.5 Pro?
Code Davinci 001 is available on the tracked providers still being sourced. Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.