MiniMax M2-her
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in tone and personality, it supports rich message...
Updated 5 h ago · first seen 11 Sept 2026
model_01M294WW5Z7P59J9D3MJF29PQN
Overview
Identity
- Canonical model
- Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
- Official checkpoints
- None recordedofficial_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
- Artifacts
- None recordedSeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
- Provider deployments
- 2
- API aliases
- minimax/minimax-m2-herIdentifiers under which providers and evaluators refer to this model.
- Folded evaluation variants
- 0Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.
Openness
Openness not classified yet — no sourced evidence to place this model in the ontology.
Key facts
- Release date
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Capabilities
Modalities
- Modalities
- text
- Input
- text
- Output
- text
Capabilities
Tool calling
No
OpenRouter public model & pricing listing · T2
Structured output
No
OpenRouter public model & pricing listing · T2
Reasoning
Unavailable
Vision
Unavailable
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Providers & Pricing2
All offers in the price terminal →USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →
Price history
Output price · USD / 1M tokens 2 providers
- MiniMax API
- OpenRouter
- OpenRouterfirst observed $1.212 Sept 2026
- MiniMax APIfirst observed $1.211 Sept 2026
Input price · USD / 1M tokens 2 providers
- MiniMax API
- OpenRouter
- OpenRouterfirst observed $0.3012 Sept 2026
- MiniMax APIfirst observed $0.3011 Sept 2026
Versions & Artifacts0
Version history
Context windowfirst observation only
11 Sept 2026current
Max outputfirst observation only
11 Sept 2026current
Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.
Artifacts 0
No artifact (checkpoint, quantisation, conversion or packaging) points to this model yet.
Timeline1
Full timeline →OpenRouter lists MiniMax M2-her at $0.3 in / $1.2 out per 1M tokens
openrouter
Change history12
Viewing AI Atlas as of 1 Jul 2026 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →MiniMax M2-her was not yet in AI Atlas on 1 Jul 2026
Release daterelease_date1
Context windowcontext_length1
Max outputmax_output_tokens1
Modalitiesmodalities1
Input modalitiesmodalities_input1
Output modalitiesmodalities_output1
Descriptiondescription1
Openrouter idopenrouter_id1
Openrouter listed atopenrouter_listed_at1
Structured outputstructured_output1
Supported parameterssupported_parameters1
Tool callingtool_calling1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
13
Source tiers
T213
Freshest observation
5 h ago
Conflicts
None
Source documents 1
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (50/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →