Llama 3.1 8B Instruct

Llama 3.1 8B Instruct

Quick answer: Llama 3.1 8B Instruct is Meta's July 2024 small instruction-tuned open-weight model, scoring 84.5% on GSM8K, 73.0% on MMLU, and 48.3% on MMLU-Pro. With a 128K context window under Apache 2.0, it is the most widely deployed open 8B model — a common first choice for low-cost server-side inference.

At a Glance

Where Llama 3.1 8B leads

• Apache 2.0 — fully permissive commercial use
• 84.5% GSM8K — strong math for an 8B model
• 128K context window
• Extremely cost-efficient inference at 8B scale
• Most widely deployed open 8B model — extensive ecosystem support
• Strong fine-tuning base (numerous Llama 3.1 8B derivatives)

Where it lags

• 48.3% MMLU-Pro — below 14B+ models on academic knowledge
• Text-only: no vision support
• Superseded by Llama 3.2 11B (adds vision) and later models
• March 2024 knowledge cutoff

Best for: Cost-optimised server inference; batch processing; fine-tuning foundation; first open-weight model for new deployments that later scale to 70B.

What Llama 3.1 8B Instruct Is

Llama 3.1 8B Instruct (released July 23, 2024) is the smallest instruction-tuned model in Meta's Llama 3.1 family, designed for efficient server-side deployment. At Apache 2.0, it is the most permissively licensed model in the Llama 3.1 family (the 70B and 405B use the more restrictive Llama 3.1 License).

Its 84.5% GSM8K score is impressive at 8B — reflecting Meta's training improvements in Llama 3.1. Combined with a 128K context window, it handles long documents at a fraction of the cost of larger models.

The model became the reference 8B open-weight model throughout 2024, with extensive ecosystem support across all major inference frameworks (vLLM, Ollama, llama.cpp, etc.) and served as the base for many fine-tuned derivatives.

Specifications

Pricing

Llama 3.1 8B Instruct is available as open weights under Apache 2.0 — free for self-hosting and commercial use without restrictions. The most cost-efficient option across hosted providers due to its small size.

Context Window

Llama 3.1 8B Instruct supports a 128,000-token context window — approximately 90 pages of text.

Public Benchmark Scores

Llama 3.1 8B vs Alternatives

Llama 3.1 8B vs Phi-4: Phi-4 (14B) achieves 70.4% MMLU-Pro vs 8B's 48.3% at ~2× the parameters — better knowledge, but significantly higher inference cost. For maximum quality per token at 8B scale, Llama 3.1 8B is the standard.

Related Models & Benchmarks

• Compare: Llama 3.2 3B Instruct · Llama 3.1 70B Instruct · Phi-4
• Benchmarks: GSM8K · MMLU · MMLU-Pro

Frequently Asked Questions

Specs from Meta's official Llama 3.1 announcement (July 2024) and Benchgen evaluations. Last updated 2026-07-24.