LLM ReferenceLLM Reference

Code Davinci 002 vs DeepSeek R1 Zero

Code Davinci 002 (2021) and DeepSeek R1 Zero (2025) are agentic coding models from OpenAI and DeepSeek. Code Davinci 002 ships a not-yet-sourced context window, while DeepSeek R1 Zero ships a 128K-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 R1 Zero is safer overall; choose Code Davinci 002 when coding workflow support matters.

Specs

Released2021-08-162025-01-20
Context window—128K
Parameters—671B, 37B Active
Architecturedecoder onlymixture of experts
LicenseProprietaryOpen Source
Knowledge cutoff--

Pricing and availability

Code Davinci 002DeepSeek R1 Zero
Input price--
Output price--
Providers--

Pricing not yet sourced for either model.

Capabilities

Code Davinci 002DeepSeek R1 Zero
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 reasoning mode: DeepSeek R1 Zero. 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 002 has no token price sourced yet and DeepSeek R1 Zero has no token price sourced yet. Provider availability is 0 tracked routes versus 0. 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 DeepSeek R1 Zero when reasoning depth 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 002 or DeepSeek R1 Zero open source?

Code Davinci 002 is listed under Proprietary. DeepSeek R1 Zero 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 reasoning mode, Code Davinci 002 or DeepSeek R1 Zero?

DeepSeek R1 Zero has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

When should I pick Code Davinci 002 over DeepSeek R1 Zero?

DeepSeek R1 Zero 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 reasoning depth, run the same evaluation with DeepSeek R1 Zero.

What is the main difference between Code Davinci 002 and DeepSeek R1 Zero?

Code Davinci 002 and DeepSeek R1 Zero 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

Model pages
Labs and families
Related comparisons

Last reviewed: 2026-04-18. Data sourced from public model cards and provider documentation.