ModelRefs / Llama 3.1 8B by Meta — Benchmarks, Pricing & Review (2026)

Llama 3.1 8B by Meta — Benchmarks, Pricing & Review (2026)

Llama 3.1 8B (Meta): Llama 3.1 8B by Meta.. 128K context. Pricing: from $0.00005/1K in. Specs, benchmarks and code examples.

What this reference supports

Llama 3.1 8B is the smaller text model in Meta's Llama 3.1 release. Its size makes local and edge-adjacent experiments more practical than larger family members, but suitability still depends on the exact artifact, quantization, runtime, language, safety controls, and task quality threshold.

Llama 3.1 8B is attributed to Meta in ModelRefs' canonical registry. Tracked modalities: Text. Primary use cases considered on ModelRefs: Local or resource-constrained language experiments; Fine-tuning, classification, extraction, and bounded assistants.

This ModelRefs profile is Provisional and pending review — decision-support material, not a final or universal ranking. Confirm current behavior, access, pricing, limits, licensing, and lifecycle in Meta's own documentation, and evaluate Llama 3.1 8B on representative workloads before implementation.

Benchmark & Evaluation

ModelRefs currently has partial, narrow benchmark coverage for Llama 3.1 8B. Treat the available benchmark evidence as one input to the decision, not a guarantee that Llama 3.1 8B is the strongest option for your workload, and evaluate it on representative workloads before selecting it.

  • Provider-reported benchmark results should be interpreted with methodology, dataset, prompting, tool, sampling, and recency limitations in mind.
  • G.8 reviewed Meta's Llama 3.1 release and research evidence for the 8B variant. Runtime, quantization, fine-tuning, and prompt-template changes require separate evaluation.
  • Central benchmark eligibility remains withheld because no sourced canonical score record is registered.

Implementation considerations

  • Select base or instruction-tuned artifacts and match the tokenizer and chat template.
  • Measure quality loss under quantization and define fallback or escalation for unsupported tasks.
  • Model artifacts support licensed self-managed deployment.
  • Managed and device runtimes can alter precision, context behavior, and supported features.

Risks and limitations

  • Model artifacts do not provide a managed production service; operators own serving, security, monitoring, evaluation, and incident response.
  • Quantization, prompt templates, runtime versions, hardware, and fine-tuning can materially change observed behavior.

Source coverage

This reference is Provisional. Model behavior, access, pricing, limits, and lifecycle can change; verify the linked provider documentation and run task-specific evaluations before implementation.

Known coverage gaps:

  • Device- and quantization-specific quality evidence plus canonical run metadata are incomplete.
  • Current managed-host version coverage is not attached.

Sources

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Llama 3.1 8B by Meta — Benchmarks, Pricing & Review (2026).