Code Davinci 001 vs Nano Banana (Gemini 2.5 Flash Image)
Code Davinci 001 (2021) and Nano Banana (Gemini 2.5 Flash Image) (2025) are agentic coding models from OpenAI and Google DeepMind. Code Davinci 001 ships a not-yet-sourced context window, while Nano Banana (Gemini 2.5 Flash Image) ships a 33K-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.
Nano Banana (Gemini 2.5 Flash Image) is safer overall; choose Code Davinci 001 when coding workflow support matters.
Specs
| Released | 2021-07-01 | 2025-04-01 |
| Context window | — | 33K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Code Davinci 001 | Nano Banana (Gemini 2.5 Flash Image) | |
|---|---|---|
| Input price | - | $0.3/1M tokens |
| Output price | - | $30/1M tokens |
| Providers | - |
Capabilities
| Code Davinci 001 | Nano Banana (Gemini 2.5 Flash Image) | |
|---|---|---|
| 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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Code Davinci 001 has no token price sourced yet and Nano Banana (Gemini 2.5 Flash Image) has $0.3/1M 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 Nano Banana (Gemini 2.5 Flash Image) when provider fit 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
Is Code Davinci 001 or Nano Banana (Gemini 2.5 Flash Image) open source?
Code Davinci 001 is listed under Proprietary. Nano Banana (Gemini 2.5 Flash Image) is listed under Unknown. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Code Davinci 001 and Nano Banana (Gemini 2.5 Flash Image)?
Code Davinci 001 is available on the tracked providers still being sourced. Nano Banana (Gemini 2.5 Flash Image) is available on Google AI Studio, GCP Vertex AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 001 over Nano Banana (Gemini 2.5 Flash Image)?
Nano Banana (Gemini 2.5 Flash Image) is safer overall; choose Code Davinci 001 when coding workflow support matters. If your workload also depends on coding workflow support, start with Code Davinci 001; if it depends on provider fit, run the same evaluation with Nano Banana (Gemini 2.5 Flash Image).
What is the main difference between Code Davinci 001 and Nano Banana (Gemini 2.5 Flash Image)?
Code Davinci 001 and Nano Banana (Gemini 2.5 Flash Image) differ most on context, provider coverage, capabilities, or pricing depending on the data currently sourced. Use the specs table first, then validate the model behavior with your own prompts.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.