Code Davinci 001 vs Llama 3.2 1B Instruct
Code Davinci 001 (2021) and Llama 3.2 1B Instruct (2024) compare a coding-specialized model against a standalone API model. Code Davinci 001 ships a not-yet-sourced context window, while Llama 3.2 1B Instruct ships a 128k-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Code Davinci 001 is coding-specialized model, while Llama 3.2 1B Instruct is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Code Davinci 001 | Llama 3.2 1B Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | provider-routed production |
| Decision fit | Coding | Coding, RAG, and Long context |
| Context window | — | 128k |
| Cheapest output | - | $0.20/1M tokens |
| Provider routes | 0 tracked | 7 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 001 for Coding.
- Llama 3.2 1B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.2 1B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.2 1B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Code Davinci 001
Unavailable
No complete token price in local provider data
Llama 3.2 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
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 001 and Llama 3.2 1B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.2 1B Instruct adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.2 1B Instruct and Code Davinci 001; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-07-01 | 2024-09-25 |
| Context window | — | 128k |
| Parameters | — | 1.23B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | Code Davinci 001 | Llama 3.2 1B Instruct |
|---|---|---|
| Input price | - | $0.03/1M tokens |
| Output price | - | $0.20/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 001 | Llama 3.2 1B 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | 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 1B 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 001 has no token price sourced yet and Llama 3.2 1B Instruct has $0.03/1M input tokens. Provider availability is 0 tracked routes versus 7. 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 Llama 3.2 1B 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 001 or Llama 3.2 1B Instruct open source?
Code Davinci 001 is listed under Proprietary. Llama 3.2 1B Instruct is listed under Llama 3 Community. 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 Llama 3.2 1B Instruct?
Llama 3.2 1B 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 001 and Llama 3.2 1B Instruct?
Code Davinci 001 is available on the tracked providers still being sourced. Llama 3.2 1B Instruct is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, NVIDIA NIM, and Bitdeer AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 001 over Llama 3.2 1B Instruct?
Treat this as a product-type comparison: Code Davinci 001 is coding-specialized model, while Llama 3.2 1B Instruct is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive. If your workload also depends on coding workflow support, start with Code Davinci 001; if it depends on provider fit, run the same evaluation with Llama 3.2 1B Instruct.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.