Compact 8B open-weight model for fast, low-cost English dialogue and self-hostable inference at high throughput.
Modality:
Text
model ID
meta-llama/llama-3-8b-instruct
Output Speed *
0.00
tok/s
Intelligence Index *
0
/ 100
Context Window *
0
tokens
Input price
0.18
Anytoken
Output price
0.24
Anytoken
Llama 3 8B Instruct: Fast, Low-Cost Open-Weight Inference for High-Volume English Workloads
Llama 3 8B Instruct is Meta's 8-billion-parameter, instruction-tuned open-weight model, the smaller member of the original Llama 3 family released alongside the 70B variant. It is text-in, text-out, tuned for dialogue, and optimized for efficient deployment on modest infrastructure. Its distinguishing trait is throughput-per-dollar: it delivers strong quality for its size at very high token speeds and low cost. It fits high-volume chat, classification, extraction, and drafting workloads where latency and cost matter more than frontier reasoning or long-context capacity.
Start calling Llama 3 8B Instruct through the AnyAPI.ai unified API.
Performance
Where Llama 3 8B Instruct Earns Its Place: Speed and Cost per Token
Llama 3 8B Instruct is built for high-throughput, cost-sensitive English generation rather than frontier reasoning. Meta reports it scoring 68.4 on MMLU and 62.2 pass@1 on HumanEval, a substantial jump over prior open models at the same parameter scale, with coding and math quality far above Mistral 7B and Gemma 7B. For an 8B model this quality-per-parameter ratio matters: it can be served cheaply at high token rates, self-hosted on modest GPUs, and scaled horizontally. In production this makes it a natural default for chat, classification, and drafting where per-request cost dominates.
Benchmarks
Llama 3 8B Instruct Benchmarks: Strong for Its Size, Not Frontier-Class
On Meta's own evaluations the instruction-tuned 8B reaches 68.4 on MMLU, 62.2 pass@1 on HumanEval, and 79.6 on GSM-8K, decisively ahead of same-tier open models like Mistral 7B and Gemma 7B on code and math. Independent measurement of the closely related 8B lineage shows very competitive latency and throughput, with output well above the median for open models of similar size. Interpretation matters: these are strong 8B-class results, but composite intelligence indices place small models like this well below larger flagship and reasoning models, so it should be evaluated against its parameter tier, not frontier systems.
Output Speed
*
0.00
tok/s
Intelligence Index
*
0
/ 100
MMLU *
Broad world knowledge and problem-solving
0
%
GPQA *
PhD-level scientific reasoning across physics, biology, chemistry.
0
%
HLE *
Adherence to multi-step structured instructions.
0
%
LiveCodeBench *
Tool-calling reliability in long agentic loops.
0
%
Technical Specifications
What the model supports
Llama 3 8B Instruct is a text-only, instruction-tuned dense transformer with an 8,192-token context window and text output only. The original Llama 3 release did not ship native tool calling or function-calling training; that capability arrived with Llama 3.1. The two production-defining constraints are the 8K context window, which limits long-document and large-RAG workloads, and the English-first tuning, which narrows reliable multilingual use. Weights are openly available under the Meta Llama 3 Community License, enabling self-hosting and fine-tuning. Knowledge is limited to late-2023 training data.
Verified Specifications —
0
*
Input modalities
Text
Context window
0
tokens
Maximum output tokens
0
Reasoning
No
Knowledge cutoff
April 2024
Pricing (standard)
0.18
AnyTokens in
/
0.24
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "llama-3.1-8b-instruct",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())import requestsurl = "https://api.anyapi.ai/v1/chat/completions"payload = { "stream": False, "tool_choice": "auto", "logprobs": False, "model": "llama-3.1-8b-instruct", "messages": [ { "role": "user", "content": "Hello" } ]}headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json"}response = requests.post(url, json=payload, headers=headers)print(response.json())
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Copy
Code is copied
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"llama-3.1-8b-instruct","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"llama-3.1-8b-instruct","messages":[{"role":"user","content":"Hello"}]}'
}; try { const response = await fetch(url, options); const data = await response.json(); console.log(data);
} catch (error) { console.error(error);
}
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Copy
Code is copied
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "llama-3.1-8b-instruct",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "stream": false, "tool_choice": "auto", "logprobs": false, "model": "llama-3.1-8b-instruct", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Copy
Code is copied
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Limitations & Trade-offs
1
Small 8K context window. At 8,192 tokens, Llama 3 8B Instruct has a far smaller context window than its own successor (128K) and most current models. This directly limits large-document summarization, multi-document RAG, and long conversation history. For any workload that must ingest sizable context in a single request, Llama 3.1 8B or a larger long-context model is the better choice.
2
No native tool calling. The original Llama 3 8B Instruct was not trained for structured function calling; that capability was introduced in Llama 3.1. Building agents on this model requires wrapper prompting and manual output parsing, which is fragile at scale. Teams building tool-using agents should prefer Llama 3.1 8B or another model with trained function-calling support.
3
English-first, limited multilingual reliability. Meta positions the original Llama 3 8B Instruct for English, and multilingual performance is weaker than later multilingual-optimized Llama releases. Applications serving non-English users should validate quality carefully or select the 3.1 line, which was explicitly trained across additional languages.
4
Small-model intelligence ceiling. As an 8B non-reasoning model, it trails larger flagship and reasoning models on complex, multi-step tasks. It has no extended-thinking mode, and composite intelligence indices rank small models like this well below frontier systems. For hard reasoning, difficult code, or nuanced analysis, a larger model is preferable; reserve this model for high-volume, well-scoped tasks.
Best-Fit Workloads
Where this model earns its place
01
High-volume conversational assistants
The model was instruction-tuned specifically for dialogue and delivers strong quality for its size at high token throughput and low cost. This makes it well suited to customer-facing chat, in-app assistants, and support bots where each conversation turn fits within 8K tokens and per-request economics dominate. Its main limitation here is the short context, so keep session history compact or summarize older turns.
02
Text classification and extraction
For intent detection, routing, tagging, sentiment, and structured field extraction, an 8B model is often accurate enough while being dramatically cheaper and faster than larger models. Llama 3 8B Instruct's speed lets you run these tasks at scale in parallel or in batch. Because there is no native schema enforcement, validate extracted JSON in your application layer.
03
Self-hosted and edge inference
As an open-weight model, Llama 3 8B Instruct can be downloaded, quantized, and self-hosted on modest GPUs, giving teams full data control and no API dependency. This suits privacy-sensitive deployments, offline environments, and cost optimization through owned hardware. Quantization reduces memory further at some quality cost, making it practical on developer-grade machines.
04
Draft generation and synthetic data
Its speed and low cost make it effective for producing first drafts, templated content, and large volumes of synthetic training examples that a stronger model or human then refines. The open license permits using outputs to improve other models. Keep prompts within 8K tokens and expect to apply downstream review for tasks requiring precision.
Pricing in anytokens via AnyAPI
Input
0.18
₳
Output
0.24
₳
Cache write
—
₳
Cache read
—
₳
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04