Exceptionally low cost at $0.03/$0.04 per 1M tokens — one of the cheapest instruction models available
Fast inference speed suitable for real-time or high-throughput applications
Open-weight model enabling self-hosting and fine-tuning flexibility
Solid instruction-following for simple, well-defined tasks
Weaknesses
8K context window is severely limiting compared to competitors like Gemini 3.1 Pro (1M+) or GPT-5.4
Noticeably weaker on complex reasoning, multi-step logic, and nuanced writing versus larger models
Outclassed even by budget competitors like GPT-4o mini on harder tasks
Monthly cost estimate
See what Meta: Llama 3 8B Instruct actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.030
Output cost
$0.020
Total / month
$0.050
Based on Meta: Llama 3 8B Instruct API pricing: $0.03/1M input · $0.04/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
Meta: Llama 3 8B Instruct pricing over time
→0% since Mar 27
2 data points · tracked daily since Mar 27, 2026
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Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
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Meta: Llama 3.1 8B Instruct
Llama 3.1 8B Instruct is Meta's smallest production-ready open-weight model, optimized for fast, low-cost inference on everyday language tasks. It delivers surprisingly capable instruction-following for its size, making it a go-to for high-volume, cost-sensitive deployments.
Verdict
The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking.
Quality score
43%
Pricing
$0.02/1M in
$0.05/1M out
Speed
Very fast
Best for high-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
Context
16k tokens
Being open-weight, this model can be run locally or self-hosted via providers like Together AI, Fireworks, or Groq, often at even lower costs. The 16K context window is a meaningful limitation compared to other models in this price tier.
Open WeightBudgetFastSelf-HostableMeta
Best for
High-throughput applications where cost and speed matter more than frontier-level quality, such as chatbots, content classification, and text summarization.
Meta's Llama 3 70B Instruct is a 70-billion parameter open-weight language model fine-tuned for instruction following, representing Meta's most capable publicly available model at the time of release. It excels at general reasoning, coding assistance, and structured text tasks with strong multilingual support.
Verdict
A capable but now-outdated open-weight model undercut by its tiny context window and newer successors.
Quality score
53%
Pricing
$0.51/1M in
$0.74/1M out
Speed
Balanced
Best for developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.
Context
8k tokens
This is the original Llama 3 70B, not the 3.1 or 3.3 variants. Llama 3.1 70B offers a 128K context window at comparable pricing and is strongly preferred. Consider this model only if you have a specific reason to pin to the original Llama 3 checkpoint.
Open-weightInstruction-tunedMid-rangeMetaLlama 3
Best for
Developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.
Meta's Llama 3.1 70B Instruct is a open-weight large language model with 70 billion parameters, fine-tuned for instruction following across coding, reasoning, and general-purpose tasks. It offers a strong balance of capability and cost at $0.40/1M tokens for both input and output.
Verdict
The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
Quality score
65%
Pricing
$0.40/1M in
$0.40/1M out
Speed
Fast
Best for teams needing capable open-weight llm performance at budget pricing for coding assistance, summarization, or rag pipelines.
Context
131k tokens
Pricing shown is via third-party API providers (e.g., OpenRouter, Together AI) — costs may vary. Meta releases Llama 3.1 weights publicly, enabling self-hosting at even lower cost. Not available directly from Meta as a hosted API.
Meta: Llama 3 8B Instruct is best for high-volume, cost-sensitive applications where speed and price matter more than peak accuracy.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and very fast speed.
When should I avoid Meta: Llama 3 8B Instruct?
You need long-document processing, complex multi-step reasoning, or production-quality writing — the 8K context and model scale will be bottlenecks.
What is a cheaper alternative to Meta: Llama 3 8B Instruct?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to Meta: Llama 3 8B Instruct?
Meta: Llama 3 70B Instruct is the better pick when response time matters more than maximum depth or premium quality.
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