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
20
science
50
agents
0
multimodal
排行榜排名
基準測試分數 (LLM Stats)
(LLM Stats (zeroeval))Chat
IFEvalGoogle Research (2023)
80.4%自報
General
BFCL
76.1%自報
MMLU
69.4%自報
Multipl-E MBPP
52.4%自報
Nexus
38.5%自報
Language
MMLU (CoT)
73.0%自報
Multipl-E HumanEval
50.8%自報
MMLU-Pro
48.3%自報
Math
GSM-8K (CoT)
84.5%自報
Multilingual MGSM (CoT)
68.9%自報
MATH (CoT)
51.9%自報
Reasoning
ARC-C
83.4%自報
API-Bank
82.6%自報
MBPP EvalPlus (base)
72.8%自報
HumanEvalOpenAI (2021)
72.6%自報
DROP
59.5%自報
GPQANYU + Cohere + Anthropic (2023)
30.4%自報
Gorilla Benchmark API Bench
8.2%自報
AA 評測指數
(Artificial Analysis)Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))51.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))47.6
Ifbench(Google Research (2023))28.6
Gpqa(NYU + Cohere + Anthropic (2023))25.9
Lcr(Artificial Analysis)18.0
Tau2(Sierra + U Toronto + Vector Institute (2025))16.4
Livecodebench(UC Berkeley + MIT + Cornell (2024))11.6
Aime(MAA (Mathematical Association of America))7.7
Intelligence Index(Artificial Analysis)6.9
Coding Index(Artificial Analysis)5.4
Hle(Center for AI Safety + Scale AI (2025))5.3
Aime 25(MAA (Mathematical Association of America))4.3
Math Index(Artificial Analysis)4.3
Terminalbench V2 11.5
Terminalbench Hard(Stanford × Laude Institute (2026))0.8
LLM Stats 分類評分
(LLM Stats (zeroeval))Chat80
Instruction Following80
Structured Output80
Language60
Legal60
Math60
Reasoning60
Finance60
General60
Healthcare60
Tool Calling50
Code40
Physics30
Biology30
Chemistry30
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
2 個供應商
最便宜: DeepInfra最貴: Meta
供應商輸入輸出
1DeepInfra最便宜
$0
$0
2Meta主要
$0.02
$0.05
比較該模型在不同 API 供應商之間的定價。