Llama 3.2 NV EmbedQA 1B v1 vs Llama 3 70B Instruct
Llama 3.2 NV EmbedQA 1B v1 (2024) and Llama 3 70B Instruct (2024) are compact production models from NVIDIA AI and AI at Meta. Llama 3.2 NV EmbedQA 1B v1 ships a 512-token context window, while Llama 3 70B Instruct ships a 8K-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 70B Instruct fits 16x 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 | Llama 3 70B Instruct |
|---|---|---|
| Decision fit | General | Coding, Classification, and JSON / Tool use |
| Context window | 512 | 8K |
| Cheapest output | - | $0.4/1M tokens |
| Provider routes | 1 tracked | 17 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Llama 3.2 NV EmbedQA 1B v1 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Llama 3 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3 70B Instruct for Coding, Classification, and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 NV EmbedQA 1B v1
Unavailable
No complete token price in local provider data
Llama 3 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 3 70B Instruct adds Structured outputs in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-08 | 2024-04-18 |
| Context window | 512 | 8K |
| Parameters | 1B | 70B |
| Architecture | encoder | decoder only |
| License | 1 | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.2 NV EmbedQA 1B v1 | Llama 3 70B Instruct |
|---|---|---|
| Input price | - | $0.4/1M tokens |
| Output price | - | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.2 NV EmbedQA 1B v1 | Llama 3 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 |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 3 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: Llama 3.2 NV EmbedQA 1B v1 has no token price sourced yet and Llama 3 70B Instruct has $0.4/1M input tokens. Provider availability is 1 tracked routes versus 17. 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 are central to the workload. Choose Llama 3 70B Instruct when long-context analysis, larger context windows, 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
Which has a larger context window, Llama 3.2 NV EmbedQA 1B v1 or Llama 3 70B Instruct?
Llama 3 70B Instruct supports 8K 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 Llama 3 70B Instruct open source?
Llama 3.2 NV EmbedQA 1B v1 is listed under 1. Llama 3 70B Instruct is listed under Open Source. 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, Llama 3.2 NV EmbedQA 1B v1 or Llama 3 70B Instruct?
Llama 3 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 Llama 3.2 NV EmbedQA 1B v1 and Llama 3 70B Instruct?
Llama 3.2 NV EmbedQA 1B v1 is available on NVIDIA NIM. Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 NV EmbedQA 1B v1 over Llama 3 70B Instruct?
Llama 3 70B Instruct fits 16x 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 Llama 3 70B Instruct.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.