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Llama 3.1 Instruct 70B
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Llama 3.1 Instruct 70B

Llama 3.1 70B Instruct is a large language model optimized for multilingual dialogue application scenarios. In common industry benchmark tests, it performs excellently among many available open - source and closed - source chat models.
Intelligence(Relatively Weak)
Speed(Relatively Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
128,000
Maximum Output Tokens
2023-12-01
Knowledge Cutoff
Go Compare

Pricing

￥1.44 /M tokens
Input
￥1.44 /M tokens
Output
￥5.47 /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Llama 4 Scout
￥0.08
Llama 4 Maverick
￥0.17
Llama 3.2 Instruct 1B

Basic Parameters

Llama 3.1 Instruct 70BTechnical Parameters
Parameter Count
70,000.0M
Context Length
128.00k tokens
Training Data Cutoff
2023-12-01
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
1,204
Release Date
2024-07-23
Response Speed
55.471,188 tokens/s

Benchmark Scores

Below is the performance of Llama 3.1 Instruct 70B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
35.38
Large Language Model Intelligence Level
Coding Index
24.96
Indicator of AI model performance on coding tasks
Math Index
41.13
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
67.6
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
40.9
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.6
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
23.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
26.7
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
81.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
64.9
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
17.3
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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