Granite 3.3 8B (Non-reasoning)
IBM開源權重Apache 2.0 · 商用許可
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
發布日期
2025-04-16
參數規模
8.2B
上下文長度
—
支援模態
text
能力雷達圖
15
general
12
coding
18
reasoning
20
science
16
agents
0
multimodal
排行榜排名
基準測試分數 (LLM Stats)
(LLM Stats (zeroeval))Code
HumanEvalOpenAI (2021)
89.7%自報
Creativity
AlpacaEval 2.0
62.7%自報
Arena Hard
57.6%自報
Finance
TruthfulQA
66.9%自報
MMLU
65.5%自報
General
TriviaQA
78.2%自報
IFEvalGoogle Research (2023)
74.8%自報
ARC-C
50.8%自報
AGIEval
49.3%自報
NQ
36.5%自報
PopQA
26.2%自報
Language
Winogrande
74.4%自報
BIG-Bench Hard
69.1%自報
Math
AIME 2024
81.2%自報
GSM8k
80.9%自報
MATH-500
69.0%自報
DROP
59.4%自報
Reasoning
HumanEval+
86.1%自報
HellaSwagAI2 (2019)
80.1%自報
Safety
AttaQ
88.5%自報
AA 評測指數
(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 分類評分
(LLM Stats (zeroeval))Safety90
Code90
Structured Output70
Instruction Following70
Language70
General70
Legal60
Math60
Reasoning60
Finance60
Healthcare60
Creativity60
Writing60
定價
輸入價格$0.03 / 1M tokens
輸出價格$0.25 / 1M tokens
混合價格(3:1)$0.085 / 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
1IBM主要
$0.03
$0.25
比較該模型在不同 API 供應商之間的定價。