Llama 3.2 1B vs Llama 3 70B Instruct
Llama 3.2 1B (2024) and Llama 3 70B Instruct (2024) are compact production models from AI at Meta. Llama 3.2 1B ships a 128K-token context window, while Llama 3 70B Instruct ships a 8K-token context window. On HumanEval, Llama 3 70B Instruct leads by 44.5 pts. On pricing, Llama 3.2 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 3.2 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 | 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 | 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 3.2 1B | Llama 3 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3.2 1B | Llama 3 70B Instruct |
|---|---|---|
| HumanEval | 28.1 | 72.6 |
| Massive Multitask Language Understanding | 54.2 | 82.0 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3.2 1B 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 at 54.2 and Llama 3 70B Instruct at 82, with Llama 3 70B Instruct ahead by 27.8 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 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 3.2 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 3.2 1B lower by about $0.3 per million blended tokens. Availability is 1 providers versus 18, so concentration risk also matters.
Choose Llama 3.2 1B 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 or Llama 3 70B Instruct?
Llama 3.2 1B 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 or Llama 3 70B Instruct?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 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 3.2 1B or Llama 3 70B Instruct open source?
Llama 3.2 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 3.2 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 3.2 1B and Llama 3 70B Instruct?
Llama 3.2 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 3.2 1B over Llama 3 70B Instruct?
Llama 3.2 1B is ~300% cheaper at $0.1/1M; 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; 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.