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
54.1 ± 10.0
Independent share
0%
Shrink toward corpus
+0.2
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 203 held-out anchors
54.9 ± 22.7
19%
Launch card against resolved peers · not independent
launch card against 5 resolved peers on 9 rows
53.9 ± 11.1
81%
· 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: inside the resolved peer range
· Launch card row livecodebench: inside the resolved peer range
· Launch card row math-500: inside the resolved peer range
· 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)
65.7
Surface (context / tools / modalities)
60.0
Freshness
17.0
· HF 30d downloads 1622
· Catalogue: context 1000000, tools
· First seen 2025-06-13
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.
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
OpenRouter weekly popularity across the full model catalogue.
Liquidity25%
0.0
Independent providers versus the model's open or closed cohort, weighted by uptime.
Open reach15%
65.7
Hugging Face 30-day downloads for open models; neutral for closed models.
Freshness5%
17.0
Time since first public availability, on a six-month half-life.
Specification
Context window
1M tokens
Max output
—
Inputs
Text
Outputs
Text
Tokenizer
—
Cached input tokens
Not offered
First seen on OpenRouter
13 Jun 2025
Capabilities
Not supported: Reasoning
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.
MiniMax M1 80k holds ModelCap Index position #85 of 248 ranked language models as of 22 September 2026, with an Index score of 54.1 (interval 41.3–66.8); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.
How much does MiniMax M1 80k cost per 1M tokens?
No live API price is listed for MiniMax M1 80k in the current snapshot as of 22 September 2026 because the latest endpoint refresh was unavailable. ModelCap shows a price only when a provider currently lists one.
What is the context window of MiniMax M1 80k?
MiniMax M1 80k has a published context window of 1M tokens (1,000,000). Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.
Is MiniMax M1 80k 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-M1-80k on Hugging Face.
Which benchmarks has MiniMax M1 80k been evaluated on?
MiniMax M1 80k has published results on 2 public boards tracked by ModelCap as of 22 September 2026: AA τ²-Bench Telecom 34.2% (#254 of 436) and AA Humanity's Last Exam 8.9% (#310 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.
How does MiniMax M1 80k compare with MiniCPM5 2B?
MiniMax M1 80k ranks #85 (Index 54.1) and MiniCPM5 2B ranks #84 (Index 54.2) on the ModelCap Index as of 22 September 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.
When was MiniMax M1 80k released?
MiniMax M1 80k first appeared in the catalogue on 13 Jun 2025. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.