MiMo-V2-Flash (Feb 2026) logo
Xiaomi
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Open weights model

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Released December 2025

Análisis de inteligencia, rendimiento y precio de MiMo-V2-Flash (Feb 2026)

Probar Benchmarks de proveedores de API

Resumen del modelo

Inteligencia

34
Índice de Inteligencia de Artificial Analysis
3 de 4 unidades para Inteligencia.

Velocidad

N/D
Tokens de salida por segundo
Valor desconocido de 4 unidades para Velocidad.

Costo

Entrada 0,00 US$Salida 0,00 US$
N/D
Costo por tarea del Índice de Inteligencia
Valor desconocido de 4 unidades para Costo.

Verbosidad

N/D
Tokens de salida del Índice de Inteligencia
Valor desconocido de 4 unidades para Verbosidad.

MiMo-V2-Flash (Feb 2026) está por encima del promedio en inteligencia y tiene un precio muy competitivo en comparación con otros modelos de pesos abiertos y tamaño similar. El modelo admite entrada de texto, genera texto y tiene una ventana de contexto de 256k tokens.

MiMo-V2-Flash (Feb 2026) obtiene 34 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa por encima del promedio entre modelos comparables (con una mediana de 27).

El precio de MiMo-V2-Flash (Feb 2026) es de $0.00 por 1 M de tokens de entrada (muy competitivo; mediana: $0.40) y $0.00 por 1 M de tokens de salida (muy competitivo; mediana: $1.20).

RazonamientoSí

Esta página muestra la versión con razonamiento de este modelo.

También puede existir una variante sin razonamiento.

Modalidad de entrada

Admite: texto

Modalidad de salida

Admite: texto

Ventana de contexto256k
~384 páginas A4 con fuente Arial de 12 puntos
Parámetros totales309B
Parámetros activos15B
Número de parámetros activos por token durante la inferencia
LicenciaMIT
Pesos del modeloHugging Face

Las métricas se comparan con modelos de la misma clase:

  • Modelos sin razonamiento → se comparan solo con otros modelos sin razonamiento
  • Modelos de razonamiento → se comparan tanto con modelos de razonamiento como sin razonamiento
  • Modelos de pesos abiertos → se comparan solo con otros modelos de pesos abiertos de la misma categoría de tamaño:
    • Muy pequeño: ≤4B parámetros
    • Pequeño: 4B–40B parámetros
    • Mediano: 40B–150B parámetros
    • Grande: >150B parámetros
  • Modelos propietarios → se comparan con modelos propietarios y de pesos abiertos del mismo rango de precio mediante una proporción combinada de precios de entrada/salida de 3:1:
    • <$0.15 por 1 M de tokens
    • $0.15–$1 por 1 M de tokens
    • >$1 por 1 M de tokens

Aspectos destacados

Inteligencia

Artificial Analysis Intelligence Index · Higher is better

Velocidad

Output tokens per second · Higher is better

Costo por tarea

Weighted average cost (USD) per Intelligence Index task · Lower is better

Inteligencia

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
See more
GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

𝜏³-BankingUpdated

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last ExamUpdated

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience AccuracyUpdated

Knowledge

AA-Omniscience Non-Hallucination RateUpdated

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgentNew

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
Reasoning models are indicated by a lightbulb icon

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Índice de apertura

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon

Comparaciones del Índice de Inteligencia

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Costo

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)
Reasoning models are indicated by a lightbulb icon

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Ventana de contexto

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Preguntas frecuentes

Preguntas comunes sobre MiMo-V2-Flash (Feb 2026)

MiMo-V2-Flash (Feb 2026) se lanzó el 16 de diciembre de 2025.

MiMo-V2-Flash (Feb 2026) fue creado por Xiaomi.

MiMo-V2-Flash (Feb 2026) obtiene 34 puntos (estimados) en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa por encima del promedio entre otros modelos de pesos abiertos y tamaño similar (mediana: 27).

Sí, MiMo-V2-Flash (Feb 2026) es un modelo de razonamiento. Usa pensamiento extendido o razonamiento en cadena para resolver problemas complejos antes de dar una respuesta.

MiMo-V2-Flash (Feb 2026) admite entrada de texto.

MiMo-V2-Flash (Feb 2026) admite salida de texto.

No, MiMo-V2-Flash (Feb 2026) no admite imágenes como entrada. Solo puede procesar texto.

No, MiMo-V2-Flash (Feb 2026) no es multimodal. Solo admite entradas de texto.

MiMo-V2-Flash (Feb 2026) tiene una ventana de contexto de 260k tokens. Esto determina cuánto texto e historial de conversación puede procesar el modelo en una sola solicitud.

Sí, MiMo-V2-Flash (Feb 2026) es un modelo de pesos abiertos. Los pesos del modelo están disponibles públicamente y pueden descargarse para alojarlo por cuenta propia.

MiMo-V2-Flash (Feb 2026) tiene 309 mil millones de parámetros (15 mil millones activos).

MiMo-V2-Flash (Feb 2026) es un modelo de mezcla de expertos (MoE) con 309 mil millones de parámetros totales, pero durante la inferencia solo usa 15 mil millones de parámetros activos.

MiMo-V2-Flash (Feb 2026) se publica bajo la licencia MIT, que permite el uso comercial. Ver licencia

MiMo-V2-Flash (Feb 2026) obtiene 34 puntos en el Índice de Inteligencia de Artificial Analysis. Este benchmark compuesto evalúa los modelos en razonamiento, conocimiento, matemáticas y programación.

MiMo-V2-Flash (Feb 2026) es un modelo de pesos abiertos que puede alojarse por cuenta propia. Ver proveedores

MiMo-V2-Flash (Feb 2026) es un modelo de pesos abiertos que puede descargarse y alojarse por cuenta propia. Comparar proveedores