Llama 3.1 Swallow 70B Instruct vs text-davinci
Llama 3.1 Swallow 70B Instruct (2025) and text-davinci (2022) are compact production models from Tokyo Institute of Technology and OpenAI. Llama 3.1 Swallow 70B Instruct ships a 4K-token context window, while text-davinci ships a 4K-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 Swallow 70B Instruct is safer overall; choose text-davinci when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Swallow 70B Instruct | text-davinci |
|---|---|---|
| Decision fit | General | General |
| Context window | 4K | 4K |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Swallow 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Use text-davinci when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 Swallow 70B Instruct
Unavailable
No complete token price in local provider data
text-davinci
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Swallow 70B Instruct and text-davinci; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for text-davinci and Llama 3.1 Swallow 70B Instruct; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2022-01-27 |
| Context window | 4K | 4K |
| Parameters | 70B | 175B |
| Architecture | decoder only | decoder only |
| License | 1 | Unknown |
| Knowledge cutoff | 2023 | 2021-06 |
Pricing and availability
| Pricing attribute | Llama 3.1 Swallow 70B Instruct | text-davinci |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Swallow 70B Instruct | text-davinci |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Llama 3.1 Swallow 70B Instruct has no token price sourced yet and text-davinci has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Swallow 70B Instruct when provider fit and broader provider choice are central to the workload. Choose text-davinci when provider fit 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
Which has a larger context window, Llama 3.1 Swallow 70B Instruct or text-davinci?
Llama 3.1 Swallow 70B Instruct supports 4K tokens, while text-davinci supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 Swallow 70B Instruct or text-davinci open source?
Llama 3.1 Swallow 70B Instruct is listed under 1. text-davinci is listed under Unknown. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Llama 3.1 Swallow 70B Instruct and text-davinci?
Llama 3.1 Swallow 70B Instruct is available on NVIDIA NIM. text-davinci is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Swallow 70B Instruct over text-davinci?
Llama 3.1 Swallow 70B Instruct is safer overall; choose text-davinci when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 Swallow 70B Instruct; if it depends on provider fit, run the same evaluation with text-davinci.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.