Llama Guard 3 1B vs text-davinci
Llama Guard 3 1B (2024) and text-davinci (2022) are compact production models from AI at Meta and OpenAI. Llama Guard 3 1B ships a 128K-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. The goal is to make the tradeoff clear before deeper testing.
Llama Guard 3 1B fits 32x more tokens; pick it for long-context work and text-davinci for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama Guard 3 1B | text-davinci |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | Long context and Classification | General |
| Context window | 128K | 4K |
| Cheapest output | $0.10/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama Guard 3 1B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama Guard 3 1B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama Guard 3 1B for Long context and Classification.
- 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 route or tier on this page.
Llama Guard 3 1B
$105
Cheapest tracked route/tier: Fireworks AI
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 Guard 3 1B and text-davinci; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for text-davinci and Llama Guard 3 1B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2022-01-27 |
| Context window | 128K | 4K |
| Parameters | 1B | 175B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | 2023-12 | 2021-06 |
Pricing and availability
| Pricing attribute | Llama Guard 3 1B | text-davinci |
|---|---|---|
| Input price | $0.10/1M tokens | - |
| Output price | $0.10/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Llama Guard 3 1B | 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 |
| 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 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 Guard 3 1B has $0.10/1M input tokens 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 Guard 3 1B when long-context analysis, larger context windows, 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 Guard 3 1B or text-davinci?
Llama Guard 3 1B supports 128K 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 Guard 3 1B or text-davinci open source?
Llama Guard 3 1B is listed under Open Source. 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 Guard 3 1B and text-davinci?
Llama Guard 3 1B is available on Fireworks AI. 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 Guard 3 1B over text-davinci?
Llama Guard 3 1B fits 32x more tokens; pick it for long-context work and text-davinci for tighter calls. If your workload also depends on long-context analysis, start with Llama Guard 3 1B; if it depends on provider fit, run the same evaluation with text-davinci.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.