LLM Reference

Llama 3.1 8B Instruct

Released
2024-07-23
Last refreshed
2026-05-22
Status
Researched 46d ago
Open SourceRAGLong contextClassificationJSON / Tool use

Llama 3.1 8B Instruct is worth evaluating for rag, long context, and classification when its provider route and context window match the workload.

Use it for

  • Teams evaluating rag, long context, and classification
  • Workloads that can use a 128k context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
Llama 3.1
Released
2024-07-23
Context
128k
Parameters
8B
Architecture
Decoder Only
Knowledge cutoff
2023-12
Specialization
general
Training
finetuned
Created by

Large-scale open-source AI for social technologies.

Menlo Park, California, United States
Founded 2013
Website
Pricing
Output / 1M
$0.050
Input / 1M
$0.020

Cheapest of 15 routes · Novita AI

About

The Llama 3.1 8B Instruct model, released on July 23, 2024, is a multilingual large language model with 8 billion parameters, optimized for instruction-following tasks. It features an enhanced transformer architecture, supporting languages like English, German, French, and others. The model excels in dialogue applications, having been fine-tuned using supervised fine-tuning and reinforcement learning with human feedback. Trained on approximately 15 trillion tokens with a December 2023 data cutoff, it outperforms many existing open-source and closed chat models in various benchmarks. Ideal for commercial and research applications such as conversational agents and content generation, the model can be accessed on Hugging Face .

Llama 3.1 8B Instruct is an open-source model in the Llama 3.1 family. The structured metadata tracks a 128k-token context window and structured outputs. This page tracks provider routes through Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, and 12 more, with the cheapest tracked route listed at $0.02 input and $0.05 output per 1M tokens. Headline tracked benchmarks include BFCL 25.8 and MMLU PRO 44.3.

Top use-case fit

RAG

Included by capability and metadata signals in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Classification

Q/$ A

1 relevant benchmark in the decision map.

Provider price ladder

Compare all 15

Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
Novita AI$0.020$0.050
Serverless
OpenRouter$0.020$0.050
Serverless
GroqCloud$0.050$0.080
Serverless
Hyperbolic AI Inference$0.100$0.100
Serverless

Capabilities

Structured Outputs

Benchmark peer barsfor Classification

Benchmark scores(2)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
BFCL25.8v4https://gorilla.cs.berkeley.edu/leaderboard.html
MMLU PRO44.3https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro

Migration checks

No linked migration route is available for this model yet.

Rankings & picks(10)

Comparison and alternatives

Browse all comparisons →