AIbase
EN
Home
AI Models Finder
Llama 3.3 Instruct 70B
L

Llama 3.3 Instruct 70B

Llama 3.3 is a multilingual large language model optimized for conversational use cases in multiple languages. It is a pre-trained and instruction-tuned generative model with 70 billion parameters, outperforming many open-source and closed chat models on common industry benchmarks. Llama 3.3 supports a context length of 128,000 tokens and is designed for commercial and research use in multiple languages.
Intelligence(Medium)
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
￥4.32 /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 3.3 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
2,220
Release Date
2024-12-06
Response Speed
86.01,366 tokens/s

Benchmark Scores

Below is the performance of Llama 3.3 Instruct 70B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
41.11
Large Language Model Intelligence Level
Coding Index
27.41
Indicator of AI model performance on coding tasks
Math Index
53.63
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
71.3
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
49.8
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
28.8
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
26
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
86
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
77.3
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
30
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase