Kimi K3.1 scores 55 on Artificial Analysis, placed on the Pareto line

Artificial AnalysisK

Artificial AnalysisModels

Coding Agents

Image, Speech, Video

Inference

Leaderboards

About

AI Trends

K

Kimi

•Proprietary model

CompareTry it out API Provider Benchmarks

Model summary

Intelligence

#4 / 215

55

Artificial Analysis Intelligence Index

4 out of 4 units for Intelligence.

Speed

#112 / 186

73.5

Output tokens per second

2 out of 4 units for Speed.

Cost

#85 / 113

In $2.00Out $10.00Cache Discount 90%

$2.11

Cost per Intelligence Index task

3 out of 4 units for Cost.

Verbosity

#92 / 116

150M

Output tokens from Intelligence Index

4 out of 4 units for Verbosity.

Comparison Summary

Kimi K3.1 Pro is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. It's also slower than average and very verbose. The model supports text and image input, outputs text, and has a 1M tokens context window.

Kimi K3.1 Pro scores 55 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 27). When evaluating the Intelligence Index, it generated 150M tokens, which is very verbose in comparison to the median of 87M.

Pricing for Kimi K3.1 Pro is $2.00 per 1M input tokens (moderately priced, median: $2.00) and $10.00 per 1M output tokens (moderately priced, median: $10.00). On average, it costs $2.11 per task to evaluate Kimi K3.1 Pro on the Intelligence Index.

At 73 tokens per second, Kimi K3.1 Pro is slower than average (81).

ReasoningYesThis page shows the reasoning version of this model.

Context window1M~1500 A4 pages of size 12 Arial font

217 models in this class

Metrics are compared against models of the same class:

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

27 of 709 models

Add model from specific provider

No publicly available API

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

27 of 709 models

Add model from specific provider

No publicly available API

ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Open Weights

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

27 of 709 models

Most attractive quadrant

Pareto line

StepFunZ AIXiaomiOpenAIAnthropicMistralMetaSpaceXAIKimiReflectionGoogleAlibabaMiniMaxNVIDIADeepSeek

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Measures the performance of models on specific capabilities and industries

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

27 of 227 models

Add model from specific provider

No publicly available API

Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexPersonalityLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

24 of 37 evaluations27 of 709 models

Add model from specific provider

No publicly available API

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1

Agentic real-world work tasks, Elo

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-3DNew

Agentic interactive 3D experiences, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

Intelligence Evaluation Relevance

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

27 of 237 models

Add model from specific provider

No publicly available API

AA-Briefcase Elo

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

27 of 582 models

Add model from specific provider

No publicly available API

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

27 of 709 models

Most attractive quadrant

Pareto line

StepFunZ AIXiaomiOpenAIAnthropicMistralMetaSpaceXAIKimiReflectionGoogleAlibabaMiniMaxNVIDIADeepSeek

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

27 of 709 models

No publicly available API

AnswerReasoning

Output Tokens per Intelligence Index Task

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

27 of 709 models

AnswerReasoningCache WriteCache HitInput

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

27 of 709 models

Add model from specific provider

OutputReasoningCache WriteCache ReadNon-Cache Input

Cost to Run Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

27 of 709 models

Add model from specific provider

Cache HitInputOutput

Cache Hit

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

4 more notes

Context Window

Context WindowIntelligence Index vs. Context Window

Context Window

Context window: tokens limit · Higher is better

27 of 709 models

Add model from specific provider

No publicly available API

Context Window for RAG

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Context Window

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Measured by Output Speed (tokens per second)

27 of 709 models

Add model from specific provider

Output Speed

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Model Performance Representation

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

27 of 709 models

Time per Intelligence Index Task

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Measured by Time (seconds) to First Token

27 of 709 models

Add model from specific provider

Thinking (reasoning models, when applicable)Input processing

Time to First Answer Token

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

27 of 709 models

Add model from specific provider

Outputting time'Thinking' time (reasoning models)Input processing time

End-to-End Response Time

Seconds to receive a 500 token response. Key components:

Model Performance Representation

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Kimi K3.1 Pro was released on October 23, 2026.

Kimi K3.1 Pro scores 55 on the Artificial Analysis Intelligence Index, placing it well above