Llama 3.2 NV EmbedQA 1B v1 vs text-davinci
Llama 3.2 NV EmbedQA 1B v1 (2024) and text-davinci (2022) are compact production models from NVIDIA AI and OpenAI. Llama 3.2 NV EmbedQA 1B v1 ships a 512-token context window, while text-davinci ships a 4k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
text-davinci fits 8x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v1 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.2 NV EmbedQA 1B v1 | text-davinci |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | General |
| Context window | 512 | 4k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 NV EmbedQA 1B v1 has broader tracked provider coverage for fallback and procurement flexibility.
- text-davinci has the larger context window for long prompts, retrieval packs, or transcript analysis.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.2 NV EmbedQA 1B v1
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 3.2 NV EmbedQA 1B v1 and text-davinci; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for text-davinci and Llama 3.2 NV EmbedQA 1B v1; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-08 | 2022-01-27 |
| Context window | 512 | 4k |
| Parameters | 1B | 175B |
| Architecture | encoder | decoder only |
| License | 1 | Unknown |
| Knowledge cutoff | - | 2021-06 |
Pricing and availability
| Pricing attribute | Llama 3.2 NV EmbedQA 1B v1 | text-davinci |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.2 NV EmbedQA 1B v1 | 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 3.2 NV EmbedQA 1B v1 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 3.2 NV EmbedQA 1B v1 when provider fit and broader provider choice are central to the workload. Choose text-davinci when long-context analysis and larger context windows 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 3.2 NV EmbedQA 1B v1 or text-davinci?
text-davinci supports 4k tokens, while Llama 3.2 NV EmbedQA 1B v1 supports 512 tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.2 NV EmbedQA 1B v1 or text-davinci open source?
Llama 3.2 NV EmbedQA 1B v1 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 3.2 NV EmbedQA 1B v1 and text-davinci?
Llama 3.2 NV EmbedQA 1B v1 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 3.2 NV EmbedQA 1B v1 over text-davinci?
text-davinci fits 8x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v1 for tighter calls. If your workload also depends on provider fit, start with Llama 3.2 NV EmbedQA 1B v1; if it depends on long-context analysis, run the same evaluation with text-davinci.
Continue comparing
Last reviewed: 2026-05-06. Data sourced from public model cards and provider documentation.