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MiniMax M3.1 Flash Review: 1M Context, Video Input, $0.15 per Million

Pick MiniMax M3.1 Flash for cheap long-context agent loops with screenshots and clips; so far we have run only launch checks, no scored tests.

Spicy Index

73,6/ 100

Preliminar

Freedom Score

Aún sin probar

Precio
0,263 US$ por 1 M de tokens mixtos (3 de entrada : 1 de salida)
Velocidad de salida
125 tok/s
Tasa de éxito
Recopilando datos

MiniMax M3.1 Flash is MiniMax's Flash-tier multimodal model, released on 19 October 2026. MiniMax pitches it as a long-context, multimodal model with tunable thinking depth, aimed at coding agents and computer workflows. On our API it is one OpenAI-compatible chat endpoint that reads text, images and video, holds 1,000,000 tokens of input and allows replies up to 524,288 tokens, with thinking counted inside that number.

The verdict

Two behaviours shape how you use it. Reasoning is always on: reasoningeffort accepts low, medium, high, xhigh or max (default max) and there is no none. And sampling is fixed by MiniMax, so temperature and topp are not accepted. A default request therefore reasons at full depth, and a small max_tokens can be spent on thinking before any visible text appears.

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 MiniMax M3.1 Flash →

Key facts

  • Tested: one real launch request on 28 Oct 2026 (neutral prompt, low reasoning effort, no streaming), plus an integration check of media input. No scored runs yet.
  • Price: $0.15 per million input tokens, $0.015 cached and $0.60 output. The live price is on the model page.
  • Limits: 1,000,000-token context, replies up to 524,288 tokens (default 4,096, thinking included), text, image and video input, 256 messages and 128 tool definitions per request.
  • Reasoning: reasoning_effort takes low, medium, high, xhigh or max (default max); reasoning cannot be disabled.
  • Sampling: fixed by the model; temperature and top_p are not accepted.
  • Sources: scoring rules are on our review methodology page, full parameters on the MiniMax M3.1 Flash model page.

Lo que nos gustó y lo que lo frenó

Lo que nos gustó

  • A low per-token price for a 1M window. At $0.15 input and $0.60 output per million tokens, a long agent trace or a stack of documents is affordable to send in full.
  • Five effort levels. Quick tool-loop steps and hard planning use the same model and the same code, with one field changed.
  • Video and images in the prompt. A screenshot of an error, a product photo or a short screen recording goes straight into the message.
  • A large reply ceiling. Up to 524,288 tokens in one reply, so long outputs do not need chunking.
  • 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

  • Reasoning cannot be disabled. There is no none, so even a simple rewrite pays for a reasoning pass; low is the lightest setting.
  • max is the default effort. Every request reasons at full depth unless you set a lower level.
  • No temperature or top_p. Use the prompt, reasoning_effort and stop sequences to shape output.
  • Reasoning is paid for out of max_tokens. The default cap is 4,096; a small cap at max effort can run out before the answer starts.
  • Mixed early reports. Some developers questioned how it performs on real agent work (see the community section), and we have no scored runs of our own yet.

What we tested

On 28 October 2026 our platform sent MiniMax M3.1 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 a media request was accepted and answered.

That is a working-endpoint check, not a benchmark. We have not compared its answers with another model, tested video on real clips, measured speed, or tried a prompt near the 1,000,000-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. Plan a reproduction. Hand it the issue report and a standard for evidence.

text
Inspect an application issue report and propose the next tool calls needed to reproduce it. Work from the material I provide, cite the relevant evidence, distinguish facts from assumptions, and identify missing information before proposing changes.

Run this prompt on MiniMax M3.1 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: use low or medium for routine agent steps and keep xhigh or max for planning that justifies the tokens; leave several thousand tokens of max_tokens headroom; send video inline and keep it under 14 MB combined.

How far it writes

We have not run our content-level test on MiniMax M3.1 Flash, so we have no result to report here. The model page states the position plainly: the platform adds no content filter of its own, content decisions come from the model, and behaviour can change between versions.

Not tested yet: the content-level ladder, multi-turn behaviour over a long session, languages other than English, video understanding on real clips, and tool-use quality over many steps.

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

The launch thread was mostly about price and doubt. One reader wrote "Another day where the Pareto frontier is moved up and to the left!" while asking for wall-clock times that releases rarely show. Another questioned the benchmarks against what testers saw before the release: "The deepswe and terminalbench4.0 scores don't seem to jive with what many opencode users' experience has been of this model while it was being tested as "space bunny"."

Alternatives

Cómo gana MiniMax M3.1 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
59,9
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 %
52,0
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
12,0 / 25
Uso de herramientasHerramientas y tool_choice
15,0 / 15
Salida estructuradaSe acepta, cumplimiento del esquema verificado
15,0 / 15
Control del razonamiento5 niveles: low · medium · high · xhigh · max
10,0 / 15

Lo que MiniMax M3.1 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 contexto1.000.000 tokens
Salida máxima por solicitud524.288 tokens
Niveles de razonamientolow · medium · high · xhigh · max
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
125 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 MiniMax M3.1 Flash

0 positivos · 1 mixtos · 1 negativos

  • Another day where the Pareto frontier is moved up and to the left!

    hacker-news· 19 oct 2026· MixtoFuente

  • The deepswe and terminalbench4.0 scores don't seem to jive with what many opencode users' experience has been of this model while it was being tested as "space bunny".

    hacker-news· 19 oct 2026· NegativoFuente

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 MiniMax M3.1 Flash

Prueba MiniMax M3.1 Flash por tu cuenta

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

0,263 US$ por 1 M de tokens mixtos (3 de entrada : 1 de salida)

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