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Llama 3.1 Instruct 8B

MetaLlama開源權重Llama 3.1 Community License

描述

Llama 3.1 8B Instruct is a multilingual large language model optimized for dialogue use cases. It features a 128K context length, state-of-the-art tool use, and strong reasoning capabilities.

發布日期
2024-07-23
參數規模
8.0B
上下文長度
131K
支援模態
text

能力雷達圖

19
general
8
coding
14
reasoning
17
science
50
agents
0
multimodal

排行榜排名

領域#排名分數來源
程式碼能力榜506
10.0
AA
通用能力榜492
22.0
AA
科學能力528
16.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Biology

GPQANYU + Cohere + Anthropic (2023)30.4%自報

Code

HumanEvalOpenAI (2021)72.6%自報
Gorilla Benchmark API Bench8.2%自報

Finance

MMLU (CoT)73.0%自報
MMLU69.4%自報
MMLU-Pro48.3%自報

General

ARC-C83.4%自報
IFEvalGoogle Research (2023)80.4%自報
BFCL76.1%自報
MBPP EvalPlus (base)72.8%自報
Multipl-E MBPP52.4%自報
Multipl-E HumanEval50.8%自報
Nexus38.5%自報

Math

GSM-8K (CoT)84.5%自報
Multilingual MGSM (CoT)68.9%自報
DROP59.5%自報
MATH (CoT)51.9%自報

Reasoning

API-Bank82.6%自報

AA 評測指數

(Artificial Analysis)
Intelligence Index(Artificial Analysis)
7.4
Coding Index(Artificial Analysis)
5.4
Math Index(Artificial Analysis)
4.3
Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))
0.5
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.5
Ifbench(Google Research (2023))
0.3
Gpqa(NYU + Cohere + Anthropic (2023))
0.3
Lcr(Artificial Analysis)
0.2
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.2
Scicode(UIUC + Argonne National Lab (2024))
0.1
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.1
Aime(MAA (Mathematical Association of America))
0.1
Hle(Center for AI Safety + Scale AI (2025))
0.1
Aime 25(MAA (Mathematical Association of America))
0.0
Terminalbench V2 1
0.0
Terminalbench Hard(Stanford × Laude Institute (2026))
0.0

LLM Stats 分類評分

(LLM Stats (zeroeval))
Structured Output
80
Instruction Following
80
Legal
60
Math
60
Reasoning
60
Language
60
Finance
60
General
60
Healthcare
60
Tool Calling
50
Code
40
Physics
30
Biology
30
Chemistry
30

定價

輸入價格$0.02 / 1M tokens
輸出價格$0.05 / 1M tokens
混合價格(3:1)$0.028 / 1M tokens
快取讀取價格$0.025 / 1M tokens

速度

Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s

供應商價格排行

供應商價格排行

1 個供應商

供應商輸入輸出
1Meta主要
$0.02
$0.05

比較該模型在不同 API 供應商之間的定價。

外部連結