Granite 3.3 8B (Non-reasoning)
IBM开源权重Apache 2.0 · 商用许可
描述
Granite-3.3-8B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-8B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. This makes it well-suited for code completion tasks
发布日期
2025-04-16
参数规模
8.2B
上下文长度
—
支持模态
text
能力雷达图
15
general
12
coding
18
reasoning
20
science
16
agents
0
multimodal
排行榜排名
基准测试分数 (LLM Stats)
(LLM Stats (zeroeval))Code
HumanEvalOpenAI (2021)
89.7%自报
Creativity
AlpacaEval 2.0
62.7%自报
Arena Hard
57.6%自报
Finance
TruthfulQA
66.9%自报
MMLU
65.5%自报
General
TriviaQA
78.2%自报
IFEvalGoogle Research (2023)
74.8%自报
ARC-C
50.8%自报
AGIEval
49.3%自报
NQ
36.5%自报
PopQA
26.2%自报
Language
Winogrande
74.4%自报
BIG-Bench Hard
69.1%自报
Math
AIME 2024
81.2%自报
GSM8k
80.9%自报
MATH-500
69.0%自报
DROP
59.4%自报
Reasoning
HumanEval+
86.1%自报
HellaSwagAI2 (2019)
80.1%自报
Safety
AttaQ
88.5%自报
AA 评测指数
(Artificial Analysis)Math Index(Artificial Analysis)6.7
Intelligence Index(Artificial Analysis)1.3
Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))0.7
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))0.5
Gpqa(NYU + Cohere + Anthropic (2023))0.3
Ifbench(Google Research (2023))0.2
Livecodebench(UC Berkeley + MIT + Cornell (2024))0.1
Tau2(Sierra + U Toronto + Vector Institute (2025))0.1
Scicode(UIUC + Argonne National Lab (2024))0.1
Aime 25(MAA (Mathematical Association of America))0.1
Aime(MAA (Mathematical Association of America))0.0
Hle(Center for AI Safety + Scale AI (2025))0.0
Lcr(Artificial Analysis)0.0
Terminalbench Hard(Stanford × Laude Institute (2026))0.0
LLM Stats 分类评分
(LLM Stats (zeroeval))Safety90
Code90
Structured Output70
Instruction Following70
Language70
General70
Legal60
Math60
Reasoning60
Finance60
Healthcare60
Creativity60
Writing60
定价
输入价格$0.03 / 1M tokens
输出价格$0.25 / 1M tokens
混合价格(3:1)$0.085 / 1M tokens
速度
Tokens/秒0.0
首Token延迟0.00s
首回答延迟0.00s
供应商价格排行
供应商价格排行
1 个供应商
供应商输入输出
1IBM主要
$0.03
$0.25
比较该模型在不同 API 供应商之间的定价。