Granite 3.3 8B (Non-reasoning)
IBMOpen WeightApache 2.0 · Uso Comercial
Descripción
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
Fecha de lanzamiento
2025-04-16
Parámetros
8.2B
Longitud del contexto
—
Modalidades
text
Radar de capacidades
15
general
12
coding
18
reasoning
20
science
16
agents
0
multimodal
Rankings
| Dominio | #Posición | Puntuación | Fuente |
|---|---|---|---|
| Ranking de codificación | 531 | 6.0 | AA |
| Ranking general | 546 | 15.0 | AA |
| Ciencia | 523 | 17.0 | AA |
Puntuaciones de benchmarks (LLM Stats)
(LLM Stats (zeroeval))Code
HumanEvalOpenAI (2021)
89.7%Aut.
Creativity
AlpacaEval 2.0
62.7%Aut.
Arena Hard
57.6%Aut.
Finance
TruthfulQA
66.9%Aut.
MMLU
65.5%Aut.
General
TriviaQA
78.2%Aut.
IFEvalGoogle Research (2023)
74.8%Aut.
ARC-C
50.8%Aut.
AGIEval
49.3%Aut.
NQ
36.5%Aut.
PopQA
26.2%Aut.
Language
Winogrande
74.4%Aut.
BIG-Bench Hard
69.1%Aut.
Math
AIME 2024
81.2%Aut.
GSM8k
80.9%Aut.
MATH-500
69.0%Aut.
DROP
59.4%Aut.
Reasoning
HumanEval+
86.1%Aut.
HellaSwagAI2 (2019)
80.1%Aut.
Safety
AttaQ
88.5%Aut.
Índices de evaluación AA
(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
Puntuaciones por categoría LLM Stats
(LLM Stats (zeroeval))Safety90
Code90
Structured Output70
Instruction Following70
Language70
General70
Legal60
Math60
Reasoning60
Finance60
Healthcare60
Creativity60
Writing60
Precios
Precio de entrada$0.03 / 1M tokens
Precio de salida$0.25 / 1M tokens
Precio mixto (3:1)$0.085 / 1M tokens
Velocidad
Tokens/seg0.0
Retraso del primer token0.00s
Tiempo hasta la respuesta0.00s
Ranking de Precios por Proveedor
Ranking de Precios por Proveedor
1 proveedores
ProveedorEntradaSalida
1IBMPRINCIPAL
$0.03
$0.25
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