Sulat.com
AI models
$10 off the fastest DeepSeek V4.1 Flash, Kimi K3 and GLM 5.3 from Synthetic
ZenMux logo

Model details

MiniMax M2

Within MiniMax's publicly listed text large language model lineup, the supplied official sources enumerate M3, M2.7, and M2.5 as the available model entries on minimax.io. There is no supplied page that specifically names or describes a model called MiniMax M2, so any architecture, training recipe, benchmark, or capability detail tied to that exact name would not be grounded in the available evidence. The model belongs to the broader MiniMax text model family that the company presents as a coherent progression of general-purpose LLMs intended for natural language tasks.

In practical terms, MiniMax's text lineup is positioned as a set of general-purpose language models, with successive entries reflecting the company's ongoing iteration on quality and capability. Because the supplied evidence does not include a dedicated MiniMax M2 model card, release note, benchmark table, or parameter count, the overview cannot responsibly claim specific strengths such as coding ability, reasoning depth, or long-context behavior for this variant. Readers should treat MiniMax M2 as part of the same family whose sibling versions (M2.5, M2.7, M3) are documented on the vendor site, while version-specific technical guarantees await direct sourcing.

ZenMuxminimax/minimax-m2

Quick Info

Powered by
Provider
ZenMux
Model key
minimax/minimax-m2
Release date
Oct 27, 2025
Last updated
Oct 27, 2025
Knowledge cutoff
2025-01-01
AI SDK package
@ai-sdk/anthropic
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.30
Output token cost
$1.20

Limits

Output tokens
64,000 tokens
Context window
204,000 tokens

Latest news about MiniMax M2

Merge Gateway

Coverage

The "MiniMax-M2 Series" paper, hosted on Hugging Face and submitted to arXiv on May 27, 2026, formally documents the M2 family of Mixture-of-Experts language models. The architecture is detailed as 229.9B total parameters with only 9.8B activated per token, built explicitly for agentic deployment rather than general ch Three engineering pillars define the series: agent-driven data pipelines producing large-scale, verifiable trajectories grounded in executable workspaces with artifact-aligned rewards; Forge, a scalable agent-native RL system using windowed-FIFO scheduling, prefix-tree merging, and inference optimization that supports

Videos about MiniMax M2