MiniMax M1 40k

Official publisher releaseOpen weights

MiniMaxAI·#189 of 504 in the all-versions archive

MiniMax M1 40k is a language model from MiniMaxAI. It ranks #189 of 504 in ModelCap's all-versions archive, with an Index score of 52.8, modeled from the publisher's launch results against measured models. Its score range of 40.0–65.7 spans positions #110–#256. Its weights are openly downloadable.

Figures as of 6 Nov 2026, 20:49 UTC · download the public dataset · methodology

ModelCap Index · All-versions archive

52.8#189

Modeled from the publisher's launch results against measured models · range 40.0–65.7

Market Gravity

12.9

Secondary market signal

Superseded by MiniMax M1 80k. It no longer holds a current-board position; its all-versions archive rank is #189.

OpenRouter endpoint status could not be refreshed. This model is not treated as currently served until a successful observation arrives.

Input

Self-hosted

weights only · no listed API price

Output

Self-hosted

weights only · no listed API price

Context

—

tokens

Providers

endpoints

Providers

Uptime measured over the last 30 minutes

Current provider status is unavailable.

Reported evaluation leads (1)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / task	Metric	Value	Revision	Provenance
TIGER-Lab/MMLU-Pro mmlu_pro	—	81.1	Unpinned `not published`	Reported Used in launch estimate

Other versions (1)

MiniMax M1 80kLanguage board #107last year

ModelCap Index · All-versions archive

Methodology

52.8

Modeled from the publisher's launch results against measured models

All-versions archive

#189 of 504

Score range: 40.0–65.7
Rank range: #110–#256

Technical details

Public rank basis: Modeled · peer benchmarks (architecture)
Evidence label: Modeled · peer benchmarks · global-corpus-prior over 210 held-out anchors (18%); launch card against 5 resolved peers on 9 rows (82%); exceeds every named peer on 1 of 9 rows; optimism haircut 0 from cross-lab-probe; shrunk 0.1 up toward the measured corpus
Index support: 36.0% · limited
Identity binding: Exact catalogue product · minimaxai/MiniMaxAI/MiniMax-M1-40k · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata MiniMaxAI/MiniMax-M1-40k@2d1d1c2f00c97fc1245bfce7648649b76e0a8e6e (not the evaluation revision)
Index observations used: 0
Qualified Index evidence point: 52.8

How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate: 52.8 ± 10.1
Independent share: 0%
Shrink toward corpus: +0.1
Peer ceiling share: 11%
Optimism haircut: None required · cross lab probe, 7 probes
Method: precision-weighted-v1

Channels fused into this model’s launch placement
Channel	Estimate	Share
Publisher corpus prior · not independent global-corpus-prior over 210 held-out anchors	53.5 ± 23.8	18%
Launch card against resolved peers · not independent launch card against 5 resolved peers on 9 rows	52.7 ± 11.1	82%

· Launch card row aime-2024: inside the resolved peer range
· Launch card row aime-2025: inside the resolved peer range
· Launch card row gpqa-diamond: claims below every resolved peer
· Launch card row hle-without-tools: claims below every resolved peer
· Launch card row livecodebench: inside the resolved peer range
· Launch card row math-500: claims below every resolved peer
· Launch card row mmlu-pro: claims below every resolved peer
· Launch card row swe-bench-verified: inside the resolved peer range
· Launch card row tau-bench: claims above every resolved peer

Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability: —
Evidence status: No admitted measured evidence
General preference: —
Coding: —
Agents & tools: —
Reasoning: —
Evidence breadth: 0%
Evidence coverage: 0%

Market and catalogue signals · secondary, never the language Index rank

Usage (OpenRouter popularity): —
Liquidity (providers × uptime): —
Open reach (HF downloads): 81.5
Surface (context / tools / modalities): 0.0
Freshness: 13.8

· HF 30d downloads 28054
· Catalogue surface
· First seen 2025-06-05

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy

35.0

19.2–50.8 uncertainty interval

Excluded from rank

Confidence: 73%
Training support: 221 models · 129 lineages
Out-of-domain check: in domain
Feature coverage: 71%
Lineage-held-out error: 8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index

47.9

partial · 52% metadata coverage

Not capability

Artifact reproducibility: 85.0
Access & legal clarity: 100.0
Deployability: 8.0
Evaluation provenance: 0.0

Not yet published: the base model it derives from, a current serving provider, measured provider uptime, published provider pricing, declared context and output limits, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage

Market Gravity: 12.9
OpenRouter weekly popularity: Not listed

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%: 0.0
Liquidity25%: 0.0
Open reach15%: 81.5
Freshness5%: 13.8

Specification

Model ID: MiniMaxAI/MiniMax-M1-40k
Declared output cap: Unknown
OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata
Inputs: Text
Outputs: Text
Cached input tokens: Not offered
First seen on OpenRouter: 5 Jun 2025

Capabilities

Not supported: Reasoning
Not supported: Tool use
Not supported: Structured output
Not supported: Response format
Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access: Open weights
Licence: apache-2.0
Pinned revision: 2d1d1c2f00c9
Parameters: 456.1B
Architecture: MiniMaxM1ForCausalLM
Model type: minimax_m1
Hugging Face downloads (30d): 28K
Downloads (all time): 382K
Likes: 185
Repository: MiniMaxAI/MiniMax-M1-40k
Repository updated: 7 Jul 2025, 07:50 UTC

MiniMax M1 40k: common questions

How does MiniMax M1 40k rank among AI models?

MiniMax M1 40k is a superseded version and is not on the current board as of 6 November 2026. Its archive position, where one exists, is shown on the page; the newer version carries the current rank.

How much does MiniMax M1 40k cost per 1M tokens?

No live API price is listed for MiniMax M1 40k in the current snapshot as of 6 November 2026 because the latest endpoint refresh was unavailable. ModelCap shows a price only when a provider currently lists one.

Is MiniMax M1 40k open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is MiniMaxAI/MiniMax-M1-40k on Hugging Face.

When was MiniMax M1 40k released?

MiniMax M1 40k first appeared in the catalogue on 5 Jun 2025. It has since been superseded by MiniMax M1 80k. Rank and price movements since then are recorded on the site's changes feed.