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ModelCap

Kimi K3.1 Pro

NewNo linked weights

Moonshot AI·#21 of 350 on the current board·cheaper than #17 Qwen3.8 Max Prime

Kimi K3.1 Pro is a language model from Moonshot AI. It ranks #21 of 350 on ModelCap's current board, with an Index score of 78.6, modeled from the publisher's launch results against measured models. Its score range of 60.0–97.2 spans positions #1–#74. It is listed at $2.00 per million input tokens and $10.00 per million output tokens from one provider. It reads up to 1M tokens of context, with no downloadable weights linked in the current source data.

Figures as of 24 Oct 2026, 19:14 UTC · download the public dataset · methodology

ModelCap Index · Current board
78.6#21
Modeled from the publisher's launch results against measured models · range 60.0–97.2
Market Gravity
38.6Increased by 14.3
Secondary market signal
Input
$2.00
per 1M tokens
Output
$10.00
per 1M tokens
Context
1M
tokens
Providers
1
2 endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Moonshot AImxfp4Cheapest$2.00$10.001Mmxfp4100.0%
Moonshot AIhighspeed$4.00$20.001Mmxfp4100.0%

ModelCap Index · Current board

Methodology
78.6
Modeled from the publisher's launch results against measured models
Current board
#21 of 350
Score range
60.0–97.2
Rank range
#1–#74
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · 1 reported rows against measured corpus ladders (61%); publisher-corpus-prior over 211 held-out anchors (39%); shrunk 4 toward the measured corpus
Index support
10.9% · weak
Identity binding
Exact catalogue product · moonshotai/moonshotai/kimi-k3.1-pro · aggregates disclosed benchmark configurations · endpoint configuration not claimed
Index observations used
0
Qualified Index evidence point
78.6
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
78.6 ± 14.5
Independent share
0%
Shrink toward corpus
-4.0
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Corpus ladder · not independent
1 reported rows against measured corpus ladders
82.6 ± 18.661%
Publisher corpus prior · not independent
publisher-corpus-prior over 211 held-out anchors
72.3 ± 23.339%
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)
32.0
Liquidity (providers × uptime)
34.0
Open reach (HF downloads)
—
Surface (context / tools / modalities)
100.0
Freshness
99.6
  • · OpenRouter popularity #53
  • · 1 live provider
  • · Catalogue: context 1048576, reasoning, tools, modalities Text/Image/Video
  • · First seen 2026-10-23

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
Estimator abstained

insufficient-artifact-metadata

Feature coverage
86%
Training support
221 models · 129 lineages

insufficient-artifact-metadata. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
30.1
limited · 38% metadata coverage
Not capability
Artifact reproducibility
0.0
Access & legal clarity
30.3
Deployability
80.0
Evaluation provenance
0.0

Not yet published: a pinned repository revision, the architecture and model type, a parameter count, safetensors weight metadata, the base model it derives from, a declared licence, a link to the licence terms, 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
38.6
OpenRouter weekly popularity
#53 of 342

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%
32.0

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
34.0

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
50.0

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
99.6

Time since first public availability, on a six-month half-life.

Specification

Model ID
moonshotai/kimi-k3.1-pro
Context window
1M tokens
Declared output cap
944K tokens

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, Image, Video
Outputs
Text
Tokenizer
Other
Cached input tokens
$0.20 / 1M
First seen on OpenRouter
23 Oct 2026

Capabilities

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

Compare Kimi K3.1 Pro with

Pick any model

Benchmark leaderboards featuring Kimi K3.1 Pro

All leaderboards
Public benchmark boards on which Kimi K3.1 Pro has a published result
LeaderboardScoreSource rankConfigurationPublished
Artificial Analysis Intelligence IndexArtificial Analysis55.1#5 of 132Reasoning23 Oct 2026
Humanity's Last Exam (Artificial Analysis run)Artificial Analysis52.5%#18 of 265Reasoning23 Oct 2026

Alternatives to Kimi K3.1 Pro, by the numbers

Ranked neighbours

Models within 3 places of #21 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Moonshot AI models

The same publisher's other current models with a public Index position.

Kimi K3.1 Pro: common questions

How does Kimi K3.1 Pro rank among AI models?

Kimi K3.1 Pro holds ModelCap Index position #21 of 350 ranked language models as of 24 October 2026, with an Index score of 78.6 (interval 60.0–97.2); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Kimi K3.1 Pro cost per 1M tokens?

Kimi K3.1 Pro is listed at $2.00 per 1M input tokens and $10.00 per 1M output tokens as of 24 October 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $9.00 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Kimi K3.1 Pro?

Kimi K3.1 Pro has a published context window of 1M tokens (1,048,576). The snapshot lists a 943,718 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Kimi K3.1 Pro open-weight?

No linked weights. No downloadable model weights are linked in the current source data. This does not identify every API or chat product that offers the model.

Which API providers serve Kimi K3.1 Pro?

One provider serves Kimi K3.1 Pro through OpenRouter as of 24 October 2026: Moonshot AI. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Kimi K3.1 Pro been evaluated on?

Kimi K3.1 Pro has published results on 2 public boards tracked by ModelCap as of 24 October 2026: AA Intelligence Index 55.1 (#5 of 132) and AA Humanity's Last Exam 52.5% (#18 of 265). Each board page ranks every tracked model on that benchmark. The ModelCap Index scores the results its methodology admits, with published uncertainty, and shows reference boards without scoring them.

How does Kimi K3.1 Pro compare with MiMo-V2.6-Pro?

Kimi K3.1 Pro ranks #21 (Index 78.6) and MiMo-V2.6-Pro ranks #20 (Index 79.0) on the ModelCap Index as of 24 October 2026. The head-to-head page lines up their benchmarks, listed prices, context windows and provider counts side by side.

When was Kimi K3.1 Pro released?

Kimi K3.1 Pro first appeared in the catalogue on 23 Oct 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.