Code Davinci 002 vs Llama 3.1 405B Instruct
Code Davinci 002 (2021) and Llama 3.1 405B Instruct (2024) are agentic coding models from OpenAI and AI at Meta. Code Davinci 002 ships a not-yet-sourced context window, while Llama 3.1 405B Instruct 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.
Llama 3.1 405B Instruct is safer overall; choose Code Davinci 002 when coding workflow support matters.
Specs
| Released | 2021-08-16 | 2024-07-23 |
| Context window | — | 128K |
| Parameters | — | 405B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Code Davinci 002 | Llama 3.1 405B Instruct | |
|---|---|---|
| Input price | - | $2.4/1M tokens |
| Output price | - | $2.4/1M tokens |
| Providers | - |
Capabilities
| Code Davinci 002 | Llama 3.1 405B Instruct | |
|---|---|---|
| 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: Llama 3.1 405B Instruct. 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 Llama 3.1 405B Instruct has $2.4/1M input tokens. Provider availability is 0 tracked routes versus 11. 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 Llama 3.1 405B Instruct 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 002 or Llama 3.1 405B Instruct open source?
Code Davinci 002 is listed under Proprietary. Llama 3.1 405B Instruct 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 002 or Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct 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.
Where can I run Code Davinci 002 and Llama 3.1 405B Instruct?
Code Davinci 002 is available on the tracked providers still being sourced. Llama 3.1 405B Instruct is available on OctoAI API, Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 002 over Llama 3.1 405B Instruct?
Llama 3.1 405B Instruct 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 Llama 3.1 405B Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.