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Llama 3.1 8B InstructOPEN
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors.
Prices update automatically — checked daily against provider list prices.
- $0.06 per 1M tokens, assuming 3 input for 1 output the convention used by Artificial Analysis
- cheaper than 87% of the 191 open-weight models tracked here
- 1 of 12 Meta models tracked here, from cheapest
Benchmarks & performance
Independent benchmark scores for Llama 3.1 8B Instruct, measured by Artificial Analysis. Higher is better.
For the same budget, Llama 3.1 8B Instruct delivers 42 times more capability. Open weights also mean you can self-host it and pay nothing per token.
Models at this level cost $0.26 per 1M tokens (median of 29) — this one costs $0.06.
Cheaper and better on this index: Solar Pro 4 · Ling-3.0-flash · gpt-oss-20b
About this model
Llama 3.1 8B Instruct is an open-weight AI model by Meta. You can download and self-host it for free; the prices below are hosted-API list prices, tracked daily, for when you prefer convenience over self-hosting.
Frequently asked questions
What is Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is an AI language model from Meta. It is open-weight: you can download it and run it on your own hardware, for free. It scores 7.4 on the Artificial Analysis intelligence index.
Is Llama 3.1 8B Instruct free?
The weights are free and open — you can self-host Llama 3.1 8B Instruct and pay nothing per token. If you prefer a hosted API, list prices are $0.05 per million input tokens and $0.08 per million output tokens.
What is Llama 3.1 8B Instruct good at?
Independent benchmarks from Artificial Analysis give it GPQA 25.9%, MMLU-Pro 47.6%, Humanity's Last Exam 5.3%, Long Context Reasoning 18%, LiveCodeBench 11.6%, SciCode 13.2%, MATH-500 51.9%, AIME 7.7%, AIME 2025 4.3%, IFBench 28.6%, τ²-Bench 16.4%, Terminal-Bench 1.5%, Terminal-Bench Hard 0.8%. It is particularly used for code generation.
Can I self-host Llama 3.1 8B Instruct?
Yes. Llama 3.1 8B Instruct has open weights, so you can download it and run it on your own GPU or server with tools like Ollama, vLLM or llama.cpp — with no per-token cost.