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Model details

MiniMax-M2

MiniMax-M2 sits within MiniMax's M2 family of language models, a line the supplier positions around programming and agentic workloads rather than general chat. The M2-series framing on third-party distribution channels emphasizes coding assistance, tool-driven workflows, and professional productivity, suggesting a model tuned for multi-step technical tasks where structured reasoning and external tool use matter more than open-ended conversation. Within the broader M-series roadmap, the M2 line is shown as a stepping stone between earlier legacy variants and newer frontier releases, indicating an evolution toward stronger real-world engineering performance.

In practice, M2-family models are deployed as cloud-hosted endpoints that pair a large 200K-token context window with a 229B-parameter footprint, giving them room to hold sizable codebases, long documents, or extended agent traces in a single pass. The supplier highlights characteristics such as polyglot code mastery, precision code refactoring, and low-latency inference through a highspeed sibling variant, pointing to strengths in multilingual programming support and iterative code editing. This combination of broad context, substantial capacity, and coding-oriented tuning makes the M2 family a practical fit for developer tooling, IDE assistants, and enterprise automation scenarios that demand sustained reasoning over large inputs.

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Quick Info

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Provider
MiniMax (minimaxi.com)
Model key
MiniMax-M2
Release date
Oct 27, 2025
Last updated
Oct 27, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.30
Output token cost
$1.20

Limits

Output tokens
131,072 tokens
Context window
204,800 tokens

Latest news about MiniMax-M2

MiniMax (minimaxi.com)

Coverage

Artificial Analysis published a detailed benchmark breakdown of MiniMax-M2.7 on March 25, 2026, describing it as a reasoning-only upgrade to the M2 line. The model scores 50 on the Artificial Analysis Intelligence Index, an 8-point jump over M2.5, and is positioned as equivalent to GLM-5 (Reasoning, 50) while using 20% Key deltas versus M2.5 include a GDPval-AA Elo of 1494 (up from 1203), a hallucination rate of 34% that lifts the AA-Omniscience score from −40 to +1, and broad gains on HLE (+9 pp), TerminalBench Hard (+5 pp), SciCode (+4 pp), IFBench (+4 pp), GPQA (+3 pp), and LCR (+3 pp), alongside a regression on τ²-Bench (−11 pp).

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