Code Cushman 001 vs Llama 3.1 70B Instruct
Code Cushman 001 (2021) and Llama 3.1 70B Instruct (2024) compare a coding-specialized model against a standalone API model. Code Cushman 001 ships a not-yet-sourced context window, while Llama 3.1 70B 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 Cushman 001 is coding-specialized model, while Llama 3.1 70B 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 Cushman 001 | Llama 3.1 70B 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 | - | $0.40/1M tokens |
| Provider routes | 0 tracked | 13 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Code Cushman 001 for Coding.
- Llama 3.1 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 70B 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 Cushman 001
Unavailable
No complete token price in local provider data
Llama 3.1 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Code Cushman 001 and Llama 3.1 70B Instruct; plan for SDK, billing, or endpoint changes.
- Llama 3.1 70B Instruct adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.1 70B Instruct and Code Cushman 001; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2021-11-03 | 2024-07-23 |
| Context window | — | 128k |
| Parameters | — | 70B |
| 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 Cushman 001 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | - | $0.40/1M tokens |
| Output price | - | $0.40/1M tokens |
| Providers | - |
Capabilities
| Capability | Code Cushman 001 | Llama 3.1 70B 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 3.1 70B 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 Cushman 001 has no token price sourced yet and Llama 3.1 70B Instruct has $0.40/1M input tokens. Provider availability is 0 tracked routes versus 13. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Code Cushman 001 when coding workflow support are central to the workload. Choose Llama 3.1 70B 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 Cushman 001 or Llama 3.1 70B Instruct open source?
Code Cushman 001 is listed under Proprietary. Llama 3.1 70B 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 Cushman 001 or Llama 3.1 70B Instruct?
Llama 3.1 70B 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 Cushman 001 and Llama 3.1 70B Instruct?
Code Cushman 001 is available on the tracked providers still being sourced. Llama 3.1 70B Instruct is available on Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Code Cushman 001 over Llama 3.1 70B Instruct?
Treat this as a product-type comparison: Code Cushman 001 is coding-specialized model, while Llama 3.1 70B 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 Cushman 001; if it depends on provider fit, run the same evaluation with Llama 3.1 70B Instruct.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.