Llama 3 70B Instruct vs Llama 3.1 70B Instruct
Llama 3 70B Instruct (2024) and Llama 3.1 70B Instruct (2024) are compact production models from AI at Meta. Llama 3 70B Instruct ships a 8K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On HumanEval, Llama 3.1 70B Instruct leads by 11.5 pts. On pricing, Llama 3 70B Instruct costs $0.4/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Pick Llama 3.1 70B Instruct for coding; Llama 3 70B Instruct is better when provider fit matters more.
Specs
| Released | 2024-04-18 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | 70B | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3 70B Instruct | Llama 3.1 70B Instruct | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.4/1M tokens |
| Output price | $0.4/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Llama 3 70B Instruct | Llama 3.1 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Llama 3 70B Instruct | Llama 3.1 70B Instruct |
|---|---|---|
| HumanEval | 72.6 | 84.1 |
| Massive Multitask Language Understanding | 82.0 | 86.0 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3 70B Instruct at 72.6 and Llama 3.1 70B Instruct at 84.1, with Llama 3.1 70B Instruct ahead by 11.5 points; Massive Multitask Language Understanding has Llama 3 70B Instruct at 82 and Llama 3.1 70B Instruct at 86, with Llama 3.1 70B Instruct ahead by 4 points. The largest visible gap is 11.5 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 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Llama 3.1 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 70B Instruct lower by about $0 per million blended tokens. Availability is 18 providers versus 11, so concentration risk also matters.
Choose Llama 3 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Llama 3.1 70B Instruct 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.
FAQ
Which has a larger context window, Llama 3 70B Instruct or Llama 3.1 70B Instruct?
Llama 3.1 70B 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 70B Instruct or Llama 3.1 70B Instruct?
Llama 3 70B Instruct is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Llama 3.1 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 70B Instruct or Llama 3.1 70B Instruct open source?
Llama 3 70B Instruct is listed under Open Source. Llama 3.1 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 70B Instruct or Llama 3.1 70B Instruct?
Both Llama 3 70B Instruct and Llama 3.1 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 70B Instruct and Llama 3.1 70B Instruct?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 70B Instruct over Llama 3.1 70B Instruct?
Pick Llama 3.1 70B Instruct for coding; Llama 3 70B Instruct is better when provider fit matters more. If your workload also depends on provider fit, start with Llama 3 70B Instruct; if it depends on long-context analysis, run the same evaluation with Llama 3.1 70B Instruct.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.