Llama Guard 3 1B vs Llama 3 70B Instruct
Llama Guard 3 1B (2024) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama Guard 3 1B ships a not-yet-sourced context window, while Llama 3 70B Instruct ships a 8K-token context window. On pricing, Llama Guard 3 1B costs $0.1/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama Guard 3 1B is ~300% cheaper at $0.1/1M; pay for Llama 3 70B Instruct only for provider fit.
Specs
| Released | 2024-09-25 | 2024-04-18 |
| Context window | — | 8K |
| Parameters | 1B | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama Guard 3 1B | Llama 3 70B Instruct | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.4/1M tokens |
| Output price | $0.1/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Llama Guard 3 1B | Llama 3 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
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.
For cost, Llama Guard 3 1B lists $0.1/1M input and $0.1/1M output tokens, while Llama 3 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 3 1B lower by about $0.3 per million blended tokens. Availability is 1 providers versus 18, so concentration risk also matters.
Choose Llama Guard 3 1B when provider fit and lower input-token cost are central to the workload. Choose Llama 3 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.
FAQ
Which is cheaper, Llama Guard 3 1B or Llama 3 70B Instruct?
Llama Guard 3 1B is cheaper on tracked token pricing. Llama Guard 3 1B costs $0.1/1M input and $0.1/1M output tokens. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama Guard 3 1B or Llama 3 70B Instruct open source?
Llama Guard 3 1B is listed under Open Source. 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 Guard 3 1B 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 Guard 3 1B and Llama 3 70B Instruct?
Llama Guard 3 1B is available on Fireworks AI. 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 Guard 3 1B over Llama 3 70B Instruct?
Llama Guard 3 1B is ~300% cheaper at $0.1/1M; pay for Llama 3 70B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama Guard 3 1B; if it depends on provider fit, run the same evaluation with Llama 3 70B Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.