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Llama 3.1 8B Instruct

Published Meta context window
128,000 tokens

Official pricing status

Provider-hosted OpenRouter pricing added for Llama 3.1 8B Instruct. This is labelled provider pricing, not Meta official creator pricing.

Compare models

Same per-1M input/output rates as above. Enter your monthly token volumes to compare bills.

Llama 3.1 8B Instruct

Input: 1M × $0.02 = $0.02
Output: 1M × $0.03 = $0.03
Total
$0.05

GPT-4o

Input: 1M × $2.50 = $2.50
Output: 1M × $10.00 = $10.00
Total
$12.50

Difference

$12.45

GPT-4o costs $12.45 more than Llama 3.1 8B Instruct.

Benchmark Matrix

Reported model metrics from published sources.

MMLU Score

69.4%

MMLU-Pro Score

48.3%

Active Parameters

8B

Reported active parameter count at inference time.

Popular comparisons

30 total featuring Llama 3.1 8B Instruct.

Open compare hub →

Llama 3.1 8B Instruct is Meta’s small, practical Llama 3.1 chat model: an open-weight assistant-style model that is light enough to appear in local, hosted, and edge-deployment workflows while still using the Llama 3.1 family’s long 128K context window.

What this model is

This is the 8B instruction-tuned member of the Llama 3.1 family. For builders, it is useful because it sits in the sweet spot between capability, cost control, and deployment flexibility. It will not beat frontier closed models on hard reasoning, but it is often the kind of model people can actually run, fine-tune, host, or compare across multiple providers.

The important beginner lesson is that Llama 3.1 8B Instruct is an open-weight model, not a single official hosted API product with one Meta price. A builder may see many hosted prices for this model across providers, but those are provider-labelled prices and must not be presented as Meta official pricing.

Pricing notes

OpenRouter lists Llama 3.1 8B Instruct at $0.02 per 1M input tokens and $0.03 per 1M output tokens, checked on 2026-07-02.

The calculator on this page uses those OpenRouter rates. This is provider-hosted pricing, not an official Meta creator price. Meta released the model weights under the Llama 3.1 Community License, so hosted API costs can vary by provider.

Benchmarks and specs

Meta’s Llama 3.1 model card reports these values for the instruction-tuned 8B model:

  • Context window: 128,000 tokens
  • MMLU: 69.4
  • MMLU-Pro: 48.3
  • Parameters: 8B
  • Model release date: 2024-07-23
  • Knowledge cutoff: December 2023
  • Supported languages listed by Meta: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai

Best fit

  • Cheap assistant experiments where hosted-provider pricing can be compared separately
  • Local or self-hosted demos
  • Beginner education about open-weight vs hosted API pricing
  • Small-model baselines against Qwen, Mistral, DeepSeek, Gemma, and larger Llama variants

Sources