Ministral 3 3B
MistralMistralOpen WeightApache 2.0 · Uso Comercial
Descripción
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities. This model is the base pre-trained version, not fine-tuned for instruction or reasoning tasks, making it ideal for custom post-training processes. The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.
Fecha de lanzamiento
2025-12-02
Parámetros
3.0B
Longitud del contexto
—
Modalidades
image, text
Radar de capacidades
20
general
13
coding
24
reasoning
22
science
21
agents
10
multimodal
Rankings
| Dominio | #Posición | Puntuación | Fuente |
|---|---|---|---|
| Ranking de codificación | 479 | 12.0 | AA |
| Ranking general | 466 | 24.0 | AA |
| Ciencia | 489 | 21.0 | AA |
Puntuaciones de benchmarks (LLM Stats)
(LLM Stats (zeroeval))Biology
GPQANYU + Cohere + Anthropic (2023)
53.4%Aut.
Code
LiveCodeBench
54.8%Aut.
Communication
Wild Bench
56.8%Aut.
MM-MT-Bench
0.08 / 100Aut.
Creativity
Arena Hard
30.5%Aut.
Finance
MMLU
70.7%Aut.
General
MMLU-Redux
73.5%Aut.
Multilingual MMLU
65.2%Aut.
TriviaQA
59.2%Aut.
AGIEval
51.1%Aut.
Math
MATH
83.0%Aut.
AIME 2024
77.5%Aut.
AIME 2025
72.1%Aut.
MATH (CoT)
60.1%Aut.
Índices de evaluación AA
(Artificial Analysis)Math Index(Artificial Analysis)22.0
Intelligence Index(Artificial Analysis)7.1
Coding Index(Artificial Analysis)4.8
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))0.5
Gpqa(NYU + Cohere + Anthropic (2023))0.4
Ifbench(Google Research (2023))0.3
Tau2(Sierra + U Toronto + Vector Institute (2025))0.2
Livecodebench(UC Berkeley + MIT + Cornell (2024))0.2
Aime 25(MAA (Mathematical Association of America))0.2
Lcr(Artificial Analysis)0.2
Scicode(UIUC + Argonne National Lab (2024))0.1
Hle(Center for AI Safety + Scale AI (2025))0.1
Tau Banking0.0
Terminalbench Hard(Stanford × Laude Institute (2026))0.0
Terminalbench V2 10.0
Puntuaciones por categoría LLM Stats
(LLM Stats (zeroeval))Language70
Finance70
Healthcare70
Legal60
Math60
Reasoning60
General60
Precios
Precio de entrada$0.1 / 1M tokens
Precio de salida$0.1 / 1M tokens
Precio mixto (3:1)$0.1 / 1M tokens
Velocidad
Tokens/seg200.6
Retraso del primer token0.44s
Tiempo hasta la respuesta0.44s
Ranking de Precios por Proveedor
Ranking de Precios por Proveedor
4 proveedores
Más barato: MistralMás caro: Kilo Gateway
ProveedorEntradaSalida
1MistralPRINCIPAL
$0.1
$0.1
2NanoGPT
$0.1
$0.1
3OpenRouter
$0.1
$0.1
4Kilo Gateway
$0.1
$0.1
Comparar precios entre diferentes proveedores de API para este modelo.