Curiosity · AI Model
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct was Meta's July 2024 mid-size flagship — a 70B dense transformer that defined the open-source production baseline for a year. It remains widely deployed where 3.3 has not been re-qualified, and is the reference point most fine-tunes and benchmarks cite.
Model specs
- Vendor
- Meta
- Family
- Llama 3
- Released
- 2024-07
- Context window
- 128,000 tokens
- Modalities
- text
- Input price
- $0.23/M tok
- Output price
- $0.4/M tok
- Pricing as of
- 2026-04-20
Strengths
- Open weights under Llama 3 community license — commercial use permitted
- Broad ecosystem support — vLLM, TGI, SGLang, Ollama, llama.cpp
- 128K context window with strong needle-in-haystack retrieval
- Huge community of fine-tunes (Hermes, Nous, Dolphin, etc.)
Limitations
- Superseded by Llama 3.3 70B for new deployments — same footprint, better quality
- No native multimodality — vision requires Llama 3.2 Vision variants
- Trails Llama 4 Maverick and DeepSeek V3 on reasoning
- Community license excludes some revenue-cap scenarios
Use cases
- Production chat and assistant deployments on-prem
- Base model for domain-specific fine-tunes
- RAG pipelines with 128K context for document Q&A
- Multilingual applications across eight supported languages
Benchmarks
| Benchmark | Score | As of |
|---|---|---|
| MMLU | ≈83% | 2024-07 |
| HumanEval | ≈80% | 2024-07 |
| MATH | ≈68% | 2024-07 |
Frequently asked questions
Should I use Llama 3.1 70B or Llama 3.3 70B?
For new work, pick Llama 3.3 70B — same hardware footprint and API shape, better post-training. Llama 3.1 70B remains fine for legacy deployments where requalification isn't yet justified.
What is Llama 3.1 70B's context window?
128,000 tokens. The 3.1 release was where Meta extended Llama from 8K to 128K — a major shift that enabled document-Q&A workflows on open weights.
Is Llama 3.1 70B still competitive?
It remains a strong general model, but on recent benchmarks both Llama 3.3 70B and MoE models like DeepSeek V3 and Llama 4 outperform it. Choose it when compatibility with existing fine-tunes matters.
Sources
- Meta — Introducing Llama 3.1 — accessed 2026-04-20
- Hugging Face — meta-llama/Llama-3.1-70B-Instruct — accessed 2026-04-20