Code Davinci 002 vs Llama 3.2 90B Instruct
Code Davinci 002 (2021) and Llama 3.2 90B Instruct (2025) are agentic coding models from OpenAI and AI at Meta. Code Davinci 002 ships a not-yet-sourced context window, while Llama 3.2 90B Instruct ships a not-yet-sourced 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 3.2 90B Instruct is safer overall; choose Code Davinci 002 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Code Davinci 002 | Llama 3.2 90B Instruct |
|---|---|---|
| Decision fit | Coding | Classification and JSON / Tool use |
| Context window | — | — |
| Cheapest output | - | $1.8/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 002 for Coding.
- Llama 3.2 90B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.2 90B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.2 90B Instruct for Classification and JSON / Tool use.
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
Llama 3.2 90B Instruct
$1,530
Cheapest tracked route: AWS Bedrock
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 Llama 3.2 90B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.2 90B Instruct adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.2 90B Instruct and Code Davinci 002; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-08-16 | 2025-09-01 |
| Context window | — | — |
| Parameters | — | — |
| Architecture | decoder only | - |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Code Davinci 002 | Llama 3.2 90B Instruct |
|---|---|---|
| Input price | - | $1.35/1M tokens |
| Output price | - | $1.8/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 002 | Llama 3.2 90B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 3.2 90B 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 3.2 90B Instruct has $1.35/1M input tokens. Provider availability is 0 tracked routes versus 1. 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 3.2 90B 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 3.2 90B Instruct open source?
Code Davinci 002 is listed under Proprietary. Llama 3.2 90B Instruct is listed under Proprietary. 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 3.2 90B Instruct?
Llama 3.2 90B 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 3.2 90B Instruct?
Code Davinci 002 is available on the tracked providers still being sourced. Llama 3.2 90B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 002 over Llama 3.2 90B Instruct?
Llama 3.2 90B 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 3.2 90B Instruct.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.