A dirt-cheap, fast open model for simple tasks — just don't expect frontier-level quality.
55
Coding
50
Writing
40
Research
0
Images
92
Value
15
Long Context
Use this when
High-volume, cost-sensitive applications where speed and price matter more than peak accuracy.
Skip this if
You need long-document processing, complex multi-step reasoning, or production-quality writing — the 8K context and model scale will be bottlenecks.
Pricing
$0.14/1M in
$0.14/1M out
→0%since Aug 2026
Context
8k tokens
Speed
Very fast
As an open-weight model, Llama 3 8B can be self-hosted via platforms like Ollama, Replicate, or Together AI. The 8,192 token context window is a significant practical limitation. Pricing listed reflects hosted API inference; self-hosted costs vary.
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
Real-world use cases
What people actually use Llama 3 8B Instruct for.
Classifying or tagging large volumes of short customer support tickets
Generating boilerplate code snippets for simple CRUD operations
Summarizing short news articles or product descriptions in bulk
How Llama 3 8B Instruct compares
The nearest models people weigh against it, and what actually separates them.
vs Llama 3.1 8B Instruct — Against Llama 3.1 8B Instruct (Meta), Llama 3 8B Instruct costs about 54% more per token and gives up 2x on context. Llama 3.1 8B Instruct is the one to check first if the price difference matters more than the ceiling.
vs Claude Haiku 5.5 — Against Claude Haiku 5.5 (Anthropic), Llama 3 8B Instruct runs about 53% cheaper per token and gives up 122.1x on context. Take Llama 3 8B Instruct unless you specifically need what Claude Haiku 5.5 does better.
vs GPT-5.6 Luna — Against GPT-5.6 Luna (OpenAI), Llama 3 8B Instruct runs about 80% cheaper per token, gives up 128.2x on context and answers faster. Take Llama 3 8B Instruct unless you specifically need what GPT-5.6 Luna does better.
Price History
Llama 3 8B Instruct pricing over time
→0% since Aug 4
90 data points · tracked daily since Aug 4, 2026
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High-volume, cost-sensitive applications where speed and price matter more than peak accuracy.. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Compare alternatives
Similar models worth checking before you commit.
MetaBudget
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
$0.05/1M in
$0.08/1M out
Speed
Very fast
5/5 speed
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.
Anthropic's October 7, 2026 low-cost model and the first Haiku with adjustable reasoning effort — thinking can be switched off for simple requests or raised to max, at $0.10/$0.50 per 1M under 100K input tokens.
Verdict
Anthropic's budget model, now with adjustable reasoning, at $0.10/$0.50.
Quality score
82%
Pricing
$0.10/1M in
$0.50/1M out
Speed
Very fast
5/5 speed
Context
1M tokens
Announced September 28, 2026 alongside Sonnet 5.5; generally available October 7, 2026. Gateway id anthropic/claude-haiku-5.5. Tiered pricing: $0.10/$0.50 per 1M up to 100K input tokens, $0.50/$2.50 above. 1,000,000 context, 128,000 max output. Launch figures as reported by Anthropic: Terminal-Bench 4.0 39.2 (max effort), 31.6 (extra), 12.8 (low); OSWorld 2.1 offline subset 72.4 (Haiku 4.5: 15.7). Verified October 10, 2026.
BudgetFastHigh volumeSub-agentsNew
Best for
High-volume tool use, sub-agents and everyday tasks on a tight budget
Llama 3 8B Instruct costs $0.14 per million input tokens and $0.14 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.68 at list price, before any batch or caching discounts.
What is Llama 3 8B Instruct best for?
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 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 Llama 3 8B Instruct?
Llama 3.1 8B Instruct (Meta) at $0.05/1M/1M input against Llama 3 8B Instruct's $0.14/1M/1M — roughly 54% less per token all in. The right tool for cheap, fast, high-volume tasks — not for anything that requires serious thinking. Compare it first if Llama 3 8B Instruct's pricing is the thing stopping you.
What is a faster alternative to Llama 3 8B Instruct?
Claude Haiku 5.5 — very fast against Llama 3 8B Instruct's very fast, with 1M tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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