Kimi K3.1 Pro Launches with Intelligence Score of 55 and High Cost-Efficiency
Moonshot AI has released the Kimi K3.1 Pro model, scoring 55 on the Artificial Analysis Intelligence Index and placing it on the Pareto frontier. The model offers leading intelligence at a highly competitive price point but is slower than average and exhibits high verbosity. It is currently available through a single API provider.
Full Report
Contents39 sections
Proprietary model
•
Released October 2026
Kimi K3.1 Pro Intelligence, Performance & Price Analysis
Try it outAPI Provider Benchmarks
Model summary
Intelligence
55Artificial Analysis Intelligence Index4 out of 4 units for Intelligence.
Speed
73.5Output tokens per second2 out of 4 units for Speed.
Cost
In 10.00Cache Discount 90%$2.11Cost per Intelligence Index task3 out of 4 units for Cost.
Verbosity
150MOutput tokens from Intelligence Index4 out of 4 units for Verbosity.
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).
| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |
|---|---|
| Input modality | Supports: text and image |
| Output modality | Supports: text |
| Context window | 1M~1500 A4 pages of size 12 Arial font |
Metrics are compared against models of the same class:
- Non-reasoning models → compared only with other non-reasoning models
- Reasoning models → compared across both reasoning and non-reasoning
- Open weights models → compared only with other open weights models of the same size class:
- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3 input/output price ratio:
Intelligence
Artificial Analysis Intelligence Index · Higher is better
Speed
Output tokens per second · Higher is better
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better
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 v1.1No publicly available API
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.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
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 v1.1No publicly available API
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.
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.
Intelligence Index vs. Cost per Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index taskMost attractive quadrantPareto line
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 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.
Capability Indexes
Measures the performance of models on specific capabilities and industries
Artificial Analysis Finance & Accounting Index
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 betterNo publicly available API
Benchmarks
Evaluations
Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100See moreNo publicly available APIAA-Briefcase v1.1
Agentic knowledge work, Elo
Knowledge
AA-Omniscience Non-Hallucination Rate
1 - hallucination rate
Agentic SaaS workflows
Agentic real-world work tasks, Elo
Reasoning & knowledge
Professional document reasoning, All-pass
Agentic coding & terminal use
Long context reasoning
SciCodeUnder review
Coding
Physics reasoning
Agentic scientific research workflows in a terminal
Agentic software engineering in open-source repositories
Quantitative analysis on spreadsheets & documents
Harvey LAB-AA v1.1Updated
Legal agentic work, Hallucination-Gated All-Pass Rate
Agentic business operations
AA-3DNew
Agentic interactive 3D experiences, Elo
Kubernetes incident root-cause analysis
MLCR-AAUpdated
Medical long context reasoning
Visual 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
While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
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.
AA-Briefcase v1.1
AA-Briefcase Elo
AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is betterNo publicly available API
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.
AA-Omniscience
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.No publicly available API
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.
Intelligence Index Comparisons
Intelligence Index vs. Cost per Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index taskMost attractive quadrantPareto line
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 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.
Token Use
Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence IndexNo publicly available API
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).
Cost
Cost per Intelligence Index Task
Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better
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.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the 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).
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens)
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.
Context Window
Context Window
Context window: tokens limit · Higher is betterNo publicly available API
Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.
Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).
Speed
Measured by Output Speed (tokens per second)
Output Speed
Output tokens per second · Higher is better
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).
Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.
Time per Intelligence Index Task
Weighted average decode time (minutes) per Intelligence Index task · Excludes TTFT and overhead time · Lower is better
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.
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time
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.
End-to-End Response Time
Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
Seconds to receive a 500 token response. Key components:
- Input time: Time to receive the first response token
- Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
- Answer time: Time to generate 500 output tokens, based on output speed
Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.
Frequently Asked Questions
Common questions about Kimi K3.1 Pro
Kimi K3.1 Pro was released on October 23, 2026.
Kimi K3.1 Pro was created by Kimi.
Kimi K3.1 Pro scores 55 on the Artificial Analysis Intelligence Index, placing it well above average among other models in a similar price tier (median: 27).
Kimi K3.1 Pro generates output at 73.5 tokens per second (based on Kimi's API), which is below average compared to other models in a similar price tier (median: 81.1 t/s).
Kimi K3.1 Pro has a time to first token (TTFT) of 3.20s (based on Kimi's API), which is better than average compared to other models in a similar price tier (median: 3.62s).
Kimi K3.1 Pro costs $2.00 per 1M input tokens (better than average, median: $2.00) and $10.00 per 1M output tokens (better than average, median: $10.00), based on Kimi's API.
Kimi K3.1 Pro costs $2.00 per 1M input tokens and $10.00 per 1M output tokens (based on Kimi's API). For a blended rate (7:2 cache hit/input/output ratio), this is $1.54 per 1M tokens. Pricing may vary by provider. Compare provider pricing
When evaluated on the Intelligence Index, Kimi K3.1 Pro generated 150M output tokens, which is at the higher end compared to other models in a similar price tier (median: 87M).
Yes, Kimi K3.1 Pro is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
Kimi K3.1 Pro supports text and image input.
Kimi K3.1 Pro supports text output.
Yes, Kimi K3.1 Pro supports image input and can analyze, describe, and answer questions about images.
Yes, Kimi K3.1 Pro is multimodal. It can process text and image input and generate text output.
Kimi K3.1 Pro has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.
No, Kimi K3.1 Pro is proprietary. The model weights are not publicly available.
Kimi K3.1 Pro has 2.8 trillion parameters.
Kimi K3.1 Pro achieves a score of 55 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, Kimi K3.1 Pro is available via API through 1 provider. Compare API providers
Kimi K3.1 Pro is available through 1 API provider. Compare providers