Llama 3.2 1B Instruct vs Llama 3 70B Instruct
Llama 3.2 1B Instruct (2024) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama 3.2 1B Instruct ships a 128K-token context window, while Llama 3 70B Instruct ships a 8K-token context window. On MMLU PRO, Llama 3 70B Instruct leads by 37.4 pts. On pricing, Llama 3.2 1B Instruct costs $0.03/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B Instruct is ~1381% cheaper at $0.03/1M; pay for Llama 3 70B Instruct only for provider fit.
Specs
| Released | 2024-09-25 | 2024-04-18 |
| Context window | 128K | 8K |
| Parameters | 1.23B | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B Instruct | Llama 3 70B Instruct | |
|---|---|---|
| Input price | $0.03/1M tokens | $0.4/1M tokens |
| Output price | $0.2/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B Instruct | Llama 3 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B Instruct | Llama 3 70B Instruct |
|---|---|---|
| MMLU PRO | 20.0 | 57.4 |
| HumanEval | 28.1 | 72.6 |
| Massive Multitask Language Understanding | 49.3 | 82.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3.2 1B Instruct at 20 and Llama 3 70B Instruct at 57.4, with Llama 3 70B Instruct ahead by 37.4 points; HumanEval has Llama 3.2 1B Instruct at 28.1 and Llama 3 70B Instruct at 72.6, with Llama 3 70B Instruct ahead by 44.5 points; Massive Multitask Language Understanding has Llama 3.2 1B Instruct at 49.3 and Llama 3 70B Instruct at 82, with Llama 3 70B Instruct ahead by 32.7 points. The largest visible gap is 44.5 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint is close: both models cover structured outputs. 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.
For cost, Llama 3.2 1B Instruct lists $0.03/1M input and $0.2/1M 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 3.2 1B Instruct lower by about $0.32 per million blended tokens. Availability is 5 providers versus 18, so concentration risk also matters.
Choose Llama 3.2 1B Instruct when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Llama 3.2 1B Instruct or Llama 3 70B Instruct?
Llama 3.2 1B Instruct supports 128K tokens, while Llama 3 70B Instruct supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 3.2 1B Instruct or Llama 3 70B Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. Llama 3.2 1B Instruct costs $0.03/1M input and $0.2/1M 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 3.2 1B Instruct or Llama 3 70B Instruct open source?
Llama 3.2 1B Instruct 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 3.2 1B Instruct or Llama 3 70B Instruct?
Both Llama 3.2 1B Instruct and Llama 3 70B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 3.2 1B Instruct and Llama 3 70B Instruct?
Llama 3.2 1B Instruct is available on OpenRouter, Fireworks AI, NVIDIA NIM, Bitdeer AI, and AWS Bedrock. 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 1B Instruct over Llama 3 70B Instruct?
Llama 3.2 1B Instruct is ~1381% cheaper at $0.03/1M; pay for Llama 3 70B Instruct only for provider fit. If your workload also depends on long-context analysis, start with Llama 3.2 1B Instruct; if it depends on provider fit, run the same evaluation with Llama 3 70B Instruct.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.