Code Davinci 002 vs Nano Banana Pro (Gemini 3 Pro Image Preview)
Code Davinci 002 (2021) and Nano Banana Pro (Gemini 3 Pro Image Preview) (2025) are agentic coding models from OpenAI and Google DeepMind. Code Davinci 002 ships a not-yet-sourced context window, while Nano Banana Pro (Gemini 3 Pro Image Preview) ships a 66K-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 Pro (Gemini 3 Pro Image Preview) is safer overall; choose Code Davinci 002 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Code Davinci 002 | Nano Banana Pro (Gemini 3 Pro Image Preview) |
|---|---|---|
| Decision fit | Coding | General |
| Context window | — | 66K |
| Cheapest output | - | $120/1M tokens |
| Provider routes | 0 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 002 for Coding.
- Nano Banana Pro (Gemini 3 Pro Image Preview) has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nano Banana Pro (Gemini 3 Pro Image Preview) has broader tracked provider coverage for fallback and procurement flexibility.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Code Davinci 002
Unavailable
No complete token price in local provider data
Nano Banana Pro (Gemini 3 Pro Image Preview)
$31,600
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 002 and Nano Banana Pro (Gemini 3 Pro Image Preview); plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Nano Banana Pro (Gemini 3 Pro Image Preview) and Code Davinci 002; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-08-16 | 2025-09-01 |
| Context window | — | 66K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Code Davinci 002 | Nano Banana Pro (Gemini 3 Pro Image Preview) |
|---|---|---|
| Input price | - | $2/1M tokens |
| Output price | - | $120/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 002 | Nano Banana Pro (Gemini 3 Pro Image Preview) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
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 002 has no token price sourced yet and Nano Banana Pro (Gemini 3 Pro Image Preview) has $2/1M 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 002 when coding workflow support are central to the workload. Choose Nano Banana Pro (Gemini 3 Pro Image Preview) 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.
FAQ
Is Code Davinci 002 or Nano Banana Pro (Gemini 3 Pro Image Preview) open source?
Code Davinci 002 is listed under Proprietary. Nano Banana Pro (Gemini 3 Pro Image Preview) 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 002 and Nano Banana Pro (Gemini 3 Pro Image Preview)?
Code Davinci 002 is available on the tracked providers still being sourced. Nano Banana Pro (Gemini 3 Pro Image Preview) 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 002 over Nano Banana Pro (Gemini 3 Pro Image Preview)?
Nano Banana Pro (Gemini 3 Pro Image Preview) is safer overall; choose Code Davinci 002 when coding workflow support matters. If your workload also depends on coding workflow support, start with Code Davinci 002; if it depends on provider fit, run the same evaluation with Nano Banana Pro (Gemini 3 Pro Image Preview).
What is the main difference between Code Davinci 002 and Nano Banana Pro (Gemini 3 Pro Image Preview)?
Code Davinci 002 and Nano Banana Pro (Gemini 3 Pro Image Preview) 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: 2026-05-11. Data sourced from public model cards and provider documentation.