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MiMo V2.6 Flash Review: $0.14 per Million, Four Input Types, 1M Tokens

Pick MiMo V2.6 Flash for high-volume multimodal jobs on a small budget, and set reasoning_effort none on simple ones; so far we have run only launch checks.

Spicy Index

76,2/ 100

Preliminar

Freedom Score

Aún sin probar

Precio
0,175 US$ por 1 M de tokens mixtos (3 de entrada : 1 de salida)
Velocidad de salida
120 tok/s
Tasa de éxito
Recopilando datos

MiMo V2.6 Flash is the lighter model in Xiaomi's V2.6 family, released as open weights in September 2026. Xiaomi positions it for high-volume multimodal office work. On our API it is one OpenAI-compatible chat endpoint that reads text, images, video and audio, holds 1,048,576 tokens of input and allows replies up to 131,072 tokens, reasoning included, at $0.14 per million input tokens.

The verdict

Reasoning is on by default: reasoningeffort accepts none, minimal, low, medium or high, and defaults to high, so set none explicitly for classification, extraction and short rewrites. Sampling has a catch. temperature (0 to 1.5) and topp apply only at none; while the model reasons it uses 1.0 and 0.95 and ignores your values.

This is a launch-day review. On 28 October 2026 our platform ran a real request through the endpoint as part of launch: one neutral message, answered without streaming at low reasoning effort. It returned a complete answer. We have not run a quality blind test or a content-level test, and this page does not score the model.

Try MiMo V2.6 Flash →

Key facts

  • Tested: one real launch request on 28 Oct 2026 (neutral prompt, low reasoning effort, no streaming), plus integration checks of media input. No scored runs yet.
  • Price: $0.14 per million input tokens, $0.0028 cached and $0.28 output. The live price is on the model page.
  • Limits: 1,048,576-token context, replies up to 131,072 tokens (default 4,096, reasoning included), text, image, video and audio input, images up to 16,384 tokens each.
  • Reasoning: reasoning_effort takes none, minimal, low, medium or high (default high).
  • Sampling: temperature 0 to 1.5 and topp 0.01 to 1, applied only at reasoningeffort none.
  • Sources: scoring rules are on our review methodology page, full parameters on the MiMo V2.6 Flash model page.

Lo que nos gustó y lo que lo frenó

Lo que nos gustó

  • A very low per-token price. $0.14 input and $0.28 output per million tokens, with cached input at $0.0028, makes per-item pipelines over thousands of documents affordable.
  • Four input types in one request. text, imageurl, videourl, audiourl and inputaudio (wav or mp3) parts can be mixed, so a slide, a screen recording and a voice memo can go in one turn.
  • A none setting. It skips the reasoning pass, so replies start sooner and use fewer output tokens, and it frees temperature and top_p.
  • A larger image budget than Pro. Images can use up to 16,384 tokens each here, against 8,192 on MiMo V2.6 Pro.
  • Tool calling is on the endpoint. Up to 128 function definitions with toolchoice and paralleltool_calls; we have not graded tool-use quality yet.

Lo que lo frenó

  • The default is high reasoning. Every request reasons unless you set none, so a pipeline that forgets to set it pays for thinking it may not need.
  • Sampling is conditional. temperature and top_p are ignored while the model reasons.
  • Reasoning is paid for out of max_tokens. A small cap at high effort can be used up before the visible answer is complete; check finish_reason.
  • The lighter model. Xiaomi positions Pro for complex projects, so hard multi-step tasks may call for it.
  • No quality numbers of our own yet. We have not compared Flash with Pro or with other low-cost models.

What we tested

On 28 October 2026 our platform sent MiMo V2.6 Flash one real request as part of launch: a single neutral user message, answered without streaming at low reasoning effort with a 1,024-token cap. It returned a complete answer, and the request was priced at the published rate. The endpoint also passed our pre-launch checks of its price table and request limits. During integration, media requests were accepted and answered.

That is a working-endpoint check, not a benchmark. We have not compared its answers with Pro or another model, tested video and audio on real clips, measured speed, or tried a prompt near the 1,048,576-token limit.

Prompts to start from

These starting prompts come from the model page's published examples. We have not graded the outputs, so they are a place to begin rather than prompts proven on this model.

1. Turn notes into action items. A good fit for reasoning_effort none.

text
Extract action items from meeting notes and separate decisions from open questions. Work from the material I provide, cite the relevant lines, and say what information is missing.

Run this prompt on MiMo V2.6 Flash →

2. A tool step. Confirms your function-calling loop end to end with a trivial function.

text
Call report_status with status ready.

Settings that matter: set reasoningeffort none for extraction, classification and short rewrites and move up only when a question needs working out; leave maxtokens headroom when reasoning is on; keep inline video and audio under 14 MB combined.

How far it writes

We have not run our content-level test on MiMo V2.6 Flash, so we have no result to report here. The model page states the position plainly: content decisions come from the model, behaviour can vary, and no guarantee of unrestricted output is made.

Not tested yet: the content-level ladder, multi-turn behaviour over a long session, languages other than English, audio and video understanding on real material, and quality of none against the reasoning levels.

Every character in a request should be an adult, keep real, identifiable people out of it, and you are responsible for the law where you publish.

What the community says

When the V2.6 family launched, one reader reported a hands-on result on Flash: "Just tried 2.6 flash on a really niche topic I specialise in and it has done a really good job." Another summed up the pitch: "This looks great in terms of cost and capabilities, truly pushing the frontier forward in terms of open weight light weight models." These are individual impressions of the family, not our results.

Alternatives

  • The larger sibling? MiMo V2.6 Pro has the same window and input types, costs more per token and caps images at 8,192 tokens; Xiaomi positions it for complex projects.
  • Another low-cost multimodal model? MiniMax M3.1 Flash reads text, images and video, but has no none setting and no audio input.
  • A cheap model with a content-level review? DeepSeek V4.1 Flash reads 1,048,576 tokens; see the DeepSeek V4.1 Flash review.

Cómo gana MiMo V2.6 Flash su Spicy Index

Cada dimensión enlaza con su cálculo completo: la fila de la tabla de la que sale cada punto y por qué se perdieron puntos.

Clasificaciones públicas 40 % de la calidad
Aún sin probar

Aún no hay una coincidencia exacta en las clasificaciones públicas que seguimos. Cómo funciona

Rol 15 % de la calidad
Aún sin probar

Nuestra batería de rol aún no se ha ejecutado con este modelo. Cómo funciona

Escritura creativa 15 % de la calidad
61,7
Seguimiento de instrucciones 15 % de la calidad
95,0
Memoria con contexto largo 15 % de la calidad
Aún sin probar

Nuestra batería de contexto largo aún no se ha ejecutado con este modelo. Cómo funciona

Capacidad Peso del 10,5 %
64,0
Freedom Score Peso del 30 %
Aún sin probar

La libertad no se ha probado o las pruebas son insuficientes. El Index conserva la puntuación original de calidad y capacidades, se marca como provisional y sigue recibiendo posición. La falta de pruebas no equivale a cero. Cómo funciona

La libertad no se ha probado o las pruebas son insuficientes. El Index conserva la puntuación original de calidad y capacidades, se marca como provisional y sigue recibiendo posición. La falta de pruebas no equivale a cero.

Abrir el informe de prueba completo →

Capacidad, punto por punto

Puntos de la tabla pública de puntuación. Los niveles reescalados y los límites que fijamos nosotros no cuentan.

Ventana de contexto (publicada por el creador)Sin declarar
0,0 / 30
Modalidades de entrada en SpicyAPITexto, más imágenes, audio
19,0 / 25
Uso de herramientasHerramientas y tool_choice
15,0 / 15
Salida estructuradaSe acepta, cumplimiento del esquema verificado
15,0 / 15
Control del razonamiento5 niveles: none · minimal · low · medium · high
15,0 / 15

Lo que MiMo V2.6 Flash puede hacer en SpicyAPI

Tomado directamente de la especificación en vivo del modelo en SpicyAPI. Algunos creadores ofrecen más en sus propias apps; esto es lo que puedes llamar hoy desde nuestra API.

Capacidades por endpoint
FunciónConversación
Ventana de contexto1.048.576 tokens
Salida máxima por solicitud131.072 tokens
Niveles de razonamientonone · minimal · low · medium · high
Llamada a herramientasSí
Salida estructuradaSí
Ver todos los parámetros y ejemplos de código →

Cuánto tarda de verdad

Medido con trabajos reales de los últimos 30 días, desde la solicitud hasta el resultado final. Los tiempos incluyen nuestras propias pruebas.

Velocidad de salida
120 tok/sIncluye el tiempo hasta el primer token
Tasa de éxito
Recopilando datos

Un tiempo aparece cuando hay al menos 10 trabajos completados. Las tasas de éxito solo cuentan trabajos de clientes y siguen ocultas para todos los modelos hasta completar 30 días de tráfico.

Lo que dice la gente de MiMo V2.6 Flash

2 positivos · 0 mixtos · 0 negativos

  • Just tried 2.6 flash on a really niche topic I specialise in and it has done a really good job.

    hacker-news· 21 sept 2026· PositivoFuente

  • This looks great in terms of cost and capabilities, truly pushing the frontier forward in terms of open weight light weight models.

    hacker-news· 21 sept 2026· PositivoFuente

Las citas son textuales de publicaciones públicas y enlazan al original. No son calificaciones ni alimentan ninguna puntuación.

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Preguntas frecuentes sobre MiMo V2.6 Flash

Prueba MiMo V2.6 Flash por tu cuenta

Ajusta cada parámetro en el Playground y mira el precio exacto antes de lanzarlo.

0,175 US$ por 1 M de tokens mixtos (3 de entrada : 1 de salida)

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