MiniMax-M3.1-Flash
MiniMax-M3.1-Flash is a native multimodal mixture-of-experts model from MiniMax, generally available from October 19, 2026, aimed at agentic tasks, coding, computer use and cybersecurity. It takes text, image and video input, returns text, and has a context window of up to 1M tokens.
Released
October 19, 2026
Type
multimodal
License
open-weight
MiniMax Community License
Context
1,000,000 tokens
Pricing
Input
$0.15 / 1M tokens
Output
$0.60 / 1M tokens
List price on the developer's own pages, checked . Cached input $0.015. Flat rates, with no long-context tier.
Capabilities
Architecture
Parameters: ~428B total, ~23B activated (MoE)
Sources
Every figure on this page was checked against these pages on . A figure we could not source is left out.
- MiniMax: MiniMax M3.1 Flash (opens in a new tab)
- MiniMax docs: Pricing overview (opens in a new tab)
- MiniMax docs: Text generation (context windows) (opens in a new tab)
- Hugging Face: MiniMaxAI/MiniMax-M3.1-Flash model card (opens in a new tab)
- Hugging Face: MiniMax-M3.1-Flash LICENSE (opens in a new tab)
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Links
In the news: MiniMax-M3.1-Flash and MiniMax
MarkTechPost
MiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input
MiniMax has released its M3.1 Flash model on its API, priced at $0.15 per 1 million input tokens. The company stated that Space Bunny was an earlier build of M3.1 Flash, which processed over 200 trillion tokens. MiniMax M3.1 Flash is a multimodal mixture-of-experts model designed for coding, agentic office work, and visual reasoning. It features 428 billion total parameters with approximately 23 billion active parameters, sparse attention, and a native visual encoder. The model accepts text, image, and video input and outputs text, with a context window of 1,000,000 tokens. Model weights are scheduled for release in late October 2026. Reasoning is a permanent feature of M3.1 Flash, with developers able to select from five effort levels: Low, Medium, High, Xhigh, and Max. MiniMax recommends the Max setting as the default. All performance benchmarks are reported by MiniMax, with no independent replication yet available. On the CoWork set, M3.1 Flash achieved a score of 62.2 on Automation-Bench v1.0.6, which MiniMax reports as the top score on its table.
MarkTechPost
MiniMax Releases MiniMax-Music3: An Open-Weights Music Model Generating Complete Five-Minute Songs From Lyrics and a Structured Caption
MiniMax released MiniMax-Music3 on an unspecified date, an open-weights text-to-music model designed to generate complete songs from lyrical and descriptive inputs. This model accepts two distinct inputs: lyrics that include section tags and a detailed music description. It then generates a full song, up to five minutes in length, in a single pass. The output format is a 32 kHz, 16-bit stereo WAV file. The underlying architecture of MiniMax-Music3 pairs a Hybrid-LM, which comprises an 8 billion parameter Global LLM and a 0.6 billion parameter Local LLM, with a continuous synthesis stack. This synthesis stack is built upon flow matching and a Flow-Variational Autoencoder (Flow-VAE). MiniMax simultaneously released the model's weights, inference code, and three documented serving paths, indicating immediate deployability rather than a research preview. The MiniMax-Music3 Community License permits commercial use, with the stipulation that 'MiniMax-Music3' must be prominently displayed in the product's user interface. Organizations generating over $20 million in aggregate yearly revenue from products utilizing the model must obtain separate written authorization from MiniMax. Furthermore, any entity hosting third-party generation services must implement and maintain safeguards to prevent infringing outputs. The model is positioned for use across various industries, including game development, advertising and brand agencies, short-form video platforms, creator tools, e-learning, podcasting, fitness and wellness applications, retail in-store audio systems, and music-technology Software as a Service (SaaS) providers. Potential applications include generating background scores for user-generated content videos, creating adaptive music for games, producing localized advertising jingles and sonic branding, composing scratch and demo tracks for musicians, enabling mood-conditioned playlist generation, and facilitating offline batch generation for scenarios where per-song API costs are a limiting factor. Architecturally, MiniMax-Music3 integrates a hierarchical autoregressive stack with a continuous synthesis pathway. The training tokenizer employs eight layers of residual vector quantization (RVQ). The topmost layer, a semantic codebook, contains 16,384 entries and is responsible for capturing core musical semantics and structure. The subsequent seven acoustic codebooks each contain 1,024 entries, contributing to the detailed acoustic generation of the music.