ModelRefs / Llama 3.1 70B by Meta — Benchmarks, Pricing & Review (2026)

Llama 3.1 70B by Meta — Benchmarks, Pricing & Review (2026)

Llama 3.1 70B (Meta): Llama 3.1 70B by Meta.. 128K context. Pricing: from $0.00059/1K in. Specs, benchmarks and code examples.

What this reference supports

Llama 3.1 70B is a Meta open-weight text model in the Llama 3.1 family. It offers a smaller infrastructure target than the 405B release while retaining the family's multilingual, coding, tool-use, and long-input scope, subject to the release license and deployment stack.

Llama 3.1 70B is attributed to Meta in ModelRefs' canonical registry. Tracked modalities: Text. Primary use cases considered on ModelRefs: Self-managed assistants, coding, and multilingual applications; Fine-tuning and controlled private-deployment experiments.

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 70B on representative workloads before implementation.

Benchmark & Evaluation

ModelRefs currently has partial, narrow benchmark coverage for Llama 3.1 70B. Treat the available benchmark evidence as one input to the decision, not a guarantee that Llama 3.1 70B 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 70B variant. Family and reference-runtime results do not transfer to every precision, quantization, prompt template, or serving stack.
  • Central benchmark eligibility remains withheld because no sourced canonical score record is registered.

Implementation considerations

  • Choose base versus instruction-tuned artifacts and preserve the documented prompt template.
  • Evaluate precision, quantization, throughput, memory, safety, and task quality on the exact runtime.
  • Model artifacts support licensed self-managed deployment.
  • Third-party hosts add their own model versions, regions, controls, and rates.

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:

  • Independent runtime and quantization comparisons plus canonical run metadata are incomplete.
  • Managed-host version mapping 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 70B by Meta — Benchmarks, Pricing & Review (2026).