llama-3.1-8b-instruct

completions

bydeepinfra

llama-3.1-8b-instruct

completions

Published by: metaProvider:

deepinfra

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to leading closed-source models in human evaluations. To read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).

Released

Jul 23, 2024

Knowledge

Dec 31, 2023

License

llama_3_1_community_license

Context

131072

Input

$0.01 / 1M tokens

Output

$0.02 / 1M tokens

Capabilities: tools

Accepts: text

Returns: text

Released Jul 23, 2024Knowledge Cutoff: Dec 31, 2023License: llama_3_1_community_license

Context: 131072 Input: $0.01 / 1M tokensOutput: $0.02 / 1M tokensCapabilities: toolsAccepts: textReturns: text

Access llama-3.1-8b-instruct through LangDB AI Gateway

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Benchmark Results for llama-3.1-8b-instruct

Category Performance Scores:

Maths: Score 4.30 (Top 92% - Rank #295)
Finance: Score 10.60 (Top 96% - Rank #308)
Science: Score 33.29 (Top 93% - Rank #298)
Writing: Score 27.80 (Top 93% - Rank #298)
Academia: Score 22.52 (Top 90% - Rank #288)
Marketing: Score 28.37 (Top 93% - Rank #298)
Programming: Score 8.50 (Top 95% - Rank #304)

Overall Performance: 19.340555555555557 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
HLE	5.10	Top 45%	General Knowledge
AIME	7.70	Top 76%	Mathematics
DROP	59.50	Top 90%	General Knowledge
GPQA	28.15	Top 97%	STEM (Physics, Chemistry, Biology)
MMLU	69.40	Top 88%	General Knowledge
SciCode	13.20	Top 89%	Scientific
MATH-500	51.90	Top 90%	Mathematics
MMLU-Pro	47.95	Top 91%	General Knowledge
HumanEval	72.60	Top 79%	Programming
LiveCodeBench	11.60	Top 93%	Programming
AA Math Index	4.30	Top 92%	Mathematics
AA Coding Index	8.50	Top 95%	Programming
AAII	16.90	Top 84%	General

MMLU Score: 69.40 - Massive Multitask Language Understanding benchmark

HumanEval Score: 72.60 - Coding capability benchmark

GPQA Score: 28.15 - Graduate-level reasoning benchmark

Model Comparison:

Provider: deepinfra

Model Type: completions

Context Size: 131072 tokens

Comparing against 320 models in the database

Performance Analytics for llama-3.1-8b-instruct

Usage Statistics (Last 2 Days):

Total Requests: 110 API calls
Average TPS: 550.97 tokens per second
Average Response Time: 779.10ms
Average Time to First Token: 779.10ms
Total Cost: $0.00
Average Request Cost: $0.0000

Daily Performance Breakdown:

Date	Requests	TPS	Response Time	TTFT	Cost
11/12/2025	108	553.71	788.30ms	788.30ms	$0.00
11/15/2025	2	142.02	285.20ms	285.20ms	$0.00

Performance Summary:

Model: llama-3.1-8b-instruct by deepinfra

Monitoring Period: 11/11/2025 to 11/18/2025

Average Daily Requests: 55

Peak Daily Requests: 108

Statistics

View Full Statistics

Request Volume

Daily API requests

110

Performance (TPS)

Tokens per second

550.97 tokens/s

Category Scores

Benchmark Tests

View Other Benchmarks

HLE

5.1

General Knowledge

AIME

7.7

Mathematics

DROP

59.5

General Knowledge

GPQA

28.1

STEM (Physics, Chemistry, Biology)

MMLU

69.4

General Knowledge

SciCode

13.2

Scientific

MATH-500

51.9

Mathematics

MMLU-Pro

48.0

General Knowledge

HumanEval

72.6

Programming

LiveCodeBench

11.6

Programming

AA Math Index

4.3

Mathematics

AA Coding Index

8.5

Programming

AAII

16.9

General

Metric	HLE	AIME	DROP	GPQA	MMLU	SciCode	MATH-500	MMLU-Pro	HumanEval	LiveCodeBench	AA Math Index	AA Coding Index	AAII
Score	5.1	7.7	59.5	28.1	69.4	13.2	51.9	48.0	72.6	11.6	4.3	8.5	16.9

Compare with Similar Models

Code Examples

Integration samples and API usage

Code Samples for llama-3.1-8b-instruct

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="llama-3.1-8b-instruct",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "llama-3.1-8b-instruct",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

console.log(response.choices[0].message.content);

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai//projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b-instruct",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: llama-3.1-8b-instruct

Provider: deepinfra

API Endpoint: $https://api.us-east-1.langdb.ai/

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llama-3.1-8b-instruct by deepinfra - AI Model Details, Pricing, and Performance Metrics

llama-3.1-8b-instruct

llama-3.1-8b-instruct

Access llama-3.1-8b-instruct through LangDB AI Gateway

Statistics

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

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