Code Davinci 001 vs Llama 4 Scout 17B-16E Instruct
Code Davinci 001 (2021) and Llama 4 Scout 17B-16E Instruct (2025) compare a coding-specialized model against a standalone API model. Code Davinci 001 ships a not-yet-sourced context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-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 4 Scout 17B-16E 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 4 Scout 17B-16E Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding | Coding, RAG, and Agents |
| Context window | — | 10m |
| Cheapest output | - | $0.30/1M tokens |
| Provider routes | 0 tracked | 12 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Davinci 001 for Coding.
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
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 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
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 4 Scout 17B-16E Instruct; plan for SDK, billing, or endpoint changes.
- Llama 4 Scout 17B-16E Instruct adds Vision, Multimodal, and Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 4 Scout 17B-16E Instruct and Code Davinci 001; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-07-01 | 2025-04-05 |
| Context window | — | 10m |
| Parameters | — | 109B (17B active) |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | Llama 4 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2024-08 |
Pricing and availability
| Pricing attribute | Code Davinci 001 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | - | $0.08/1M tokens |
| Output price | - | $0.30/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Davinci 001 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| 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 vision: Llama 4 Scout 17B-16E Instruct, multimodal input: Llama 4 Scout 17B-16E Instruct, and structured outputs: Llama 4 Scout 17B-16E 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 4 Scout 17B-16E Instruct has $0.08/1M input tokens. Provider availability is 0 tracked routes versus 12. 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 4 Scout 17B-16E Instruct when vision-heavy evaluation 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.
FAQ
Is Code Davinci 001 or Llama 4 Scout 17B-16E Instruct open source?
Code Davinci 001 is listed under Proprietary. Llama 4 Scout 17B-16E Instruct is listed under Llama 4 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 vision, Code Davinci 001 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Code Davinci 001 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Code Davinci 001 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E 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 4 Scout 17B-16E Instruct?
Code Davinci 001 is available on the tracked providers still being sourced. Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Davinci 001 over Llama 4 Scout 17B-16E Instruct?
Treat this as a product-type comparison: Code Davinci 001 is coding-specialized model, while Llama 4 Scout 17B-16E 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 vision-heavy evaluation, run the same evaluation with Llama 4 Scout 17B-16E Instruct.
Continue comparing
Last reviewed: 2026-06-07. Data sourced from public model cards and provider documentation.