Code Davinci 001 vs DeepSeek V3.2
Code Davinci 001 (2021) and DeepSeek V3.2 (2025) are agentic coding models from OpenAI and DeepSeek. Code Davinci 001 ships a not-yet-sourced context window, while DeepSeek V3.2 ships a 160K-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.
DeepSeek V3.2 is safer overall; choose Code Davinci 001 when coding workflow support matters.
Specs
| Released | 2021-07-01 | 2025-01-01 |
| Context window | — | 160K |
| Parameters | — | 671B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Code Davinci 001 | DeepSeek V3.2 | |
|---|---|---|
| Input price | - | $0.26/1M tokens |
| Output price | - | $0.42/1M tokens |
| Providers | - |
Capabilities
| Code Davinci 001 | DeepSeek V3.2 | |
|---|---|---|
| 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 structured outputs: DeepSeek V3.2 and code execution: DeepSeek V3.2. 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 DeepSeek V3.2 has $0.26/1M input tokens. Provider availability is 0 tracked routes versus 4. 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 DeepSeek V3.2 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. 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 DeepSeek V3.2 open source?
Code Davinci 001 is listed under Proprietary. DeepSeek V3.2 is listed under Open Source. 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 structured outputs, Code Davinci 001 or DeepSeek V3.2?
DeepSeek V3.2 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for code execution, Code Davinci 001 or DeepSeek V3.2?
DeepSeek V3.2 has the clearer documented code execution signal in this comparison. If code execution 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 DeepSeek V3.2?
Code Davinci 001 is available on the tracked providers still being sourced. DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 001 over DeepSeek V3.2?
DeepSeek V3.2 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 coding workflow support, run the same evaluation with DeepSeek V3.2.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.