Kimi K3
Moonshot AIKey kimi-k3Also reported as moonshotai/kimi-k3, alibaba/kimi-k3, combo/kimi-k3, kimi/kimi-k3, cline-pass/kimi-k3, merius/moonshotai/kimi-k3, hf:moonshotai/kimi-k3, modal/kimi-k3, inferhub/kimi-k3, aihubmix/kimi-k3, fact/kimi-k3, sk/kimi-k3, cline-free/kimi-k3, moonshot/kimi-k3, cb/kimi-k3, newapi/kimi-k3, ali/kimi-k3, synthetic/hf:moonshotai/kimi-k3, openrouter/kimi-k3, cline/kimi-k3
- Rank
- #51
- by active users
- Active users
- 7
- Down 10 from 17 active users in the previous 7 days. vs previous 7 days
- Adoption
- 1.2%
- of 598 accounts
- Tokens
- 36.3M
- cache included
- Est. cost
- $27
- at public list prices
- First seen
- Jun 13, 2026
- in any submission
Nov 7–13, 2026 (UTC)598 accounts100% of tokens from an identified modelData through Nov 13, 2026, 01:19 UTCHow we count
Daily active users
Distinct accounts that used this model on each UTC day.
1 active users on Nov 13, 2026
Show daily values
| Date (UTC) | Active users | Tokens |
|---|---|---|
| Nov 7, 2026 | 2 | 978K |
| Nov 8, 2026 | 2 | 9.7M |
| Nov 9, 2026 | 3 | 18.1M |
| Nov 10, 2026 | 2 | 4.6M |
| Nov 11, 2026 | 1 | 238K |
| Nov 12, 2026 | 2 | 2.3M |
| Nov 13, 2026 | 1 | 426K |
Token mix
How the 36.3M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.
- Input9.7%
- Output1.4%
- Cache read88%
- Cache write0.7%
- Reasoning<0.1%
Agents that carry Kimi K3
Each agent's share of this model's tokens, and how many accounts used the model through it.
Claude Code63% of this model's tokens3active users
Senpi (OmO Native)27% of this model's tokens1active users
Amp5.8% of this model's tokens1active users
Oh My Pi4.9% of this model's tokens1active users- cc-mirror/curl<0.1% of this model's tokens1active users
- cc-mirror/python-urllib<0.1% of this model's tokens1active users
- cc-mirror/mozilla<0.1% of this model's tokens1active users
Benchmarks
The best result for Kimi K3 in each benchmark Tokscale tracks. Each benchmark has its own unit, run conditions and source, so compare a score only with other models on the same benchmark. Rank is among the models Tokscale matched in that benchmark.
- Epoch Capabilities IndexOverall · Epoch AI158.6
Epoch Capabilities Index: 158.6
- Model
- Kimi K3
- Listed as
- Kimi K3
- Conditions
- Not reported
- Interval
- 156.6–160.8
- Unit
- ECI
- Model released
- Jul 16, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #24 of 158 matched modelsNo conditions reportedModel released Jul 16, 2026Source: epoch.ai (opens in a new tab) - DeepSWEAgentic coding · Datacurve, collected by Epoch AI68.5%
DeepSWE: 68.5%
- Model
- Kimi K3
- Listed as
- kimi-k3 (max)
- Conditions
- Max · mini-swe-agent
- Interval
- 64.0–73.1%
- Unit
- pass@1
- Model released
- Jul 16, 2026
- Retrieved
- Nov 12, 2026
Source · DeepSWE results by Datacurve, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #8 of 26 matched modelsMax · mini-swe-agentModel released Jul 16, 2026Source: deepswe.datacurve.ai (opens in a new tab) - FrontierSWEAgentic coding · FrontierSWE, collected by Epoch AI25.9%
FrontierSWE: 25.9%
- Model
- Kimi K3
- Listed as
- Kimi K3
- Conditions
- Max · proximus
- Unit
- mean@5
- Model released
- Jul 16, 2026
- Retrieved
- Nov 12, 2026
Source · FrontierSWE results, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #14 of 23 matched modelsMax · proximusModel released Jul 16, 2026Source: frontierswe.com (opens in a new tab) - WebDev ArenaWeb development · LMArena1,655
WebDev Arena: 1,655
- Model
- Kimi K3
- Listed as
- Kimi K3 (Max)
- Conditions
- Max
- Interval
- 1649–1661
- Unit
- Arena score
- Published
- Oct 19, 2026
- Retrieved
- Oct 21, 2026
Source · LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset on Hugging Face), CC BY 4.0
- Agent ArenaAgents · LMArena+0.02
Agent Arena: +0.02
- Model
- Kimi K3
- Listed as
- Kimi K3 (Max)
- Conditions
- Max · Arena Agent Mode
- Interval
- 0.01 to 0.03
- Unit
- net improvement
- Published
- Oct 19, 2026
- Retrieved
- Oct 21, 2026
Source · LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset on Hugging Face), CC BY 4.0
Rank #23 of 48 matched modelsMax · Arena Agent ModePublished Oct 19, 2026Source: huggingface.co (opens in a new tab) - GPQA DiamondReasoning · Epoch AI93.1%
GPQA Diamond: 93.1%
- Model
- Kimi K3
- Listed as
- kimi-k3_max
- Conditions
- Max
- Unit
- % correct
- Run date
- Jul 16, 2026
- Retrieved
- Nov 12, 2026
Best of 3 reported results for this model.
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Near its ceiling: the top score is 96.5%, and Epoch AI estimates the reachable maximum at 97%. Differences between top models are small.
Show 2 more results with other run conditionsHide the other results
- Listed as kimi-k3_high91.9%
GPQA Diamond: 91.9%
- Model
- Kimi K3
- Listed as
- kimi-k3_high
- Conditions
- High
- Unit
- % correct
- Run date
- Aug 7, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as kimi-k3_low84.8%
GPQA Diamond: 84.8%
- Model
- Kimi K3
- Listed as
- kimi-k3_low
- Conditions
- Low
- Unit
- % correct
- Run date
- Aug 7, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI72.2%
FrontierMath Tiers 1–3 (v2): 72.2%
- Model
- Kimi K3
- Listed as
- kimi-k3_max
- Conditions
- Max
- Unit
- % solved
- Run date
- Jul 17, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Near its ceiling: the top score is 95.1%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.
- SimpleQA VerifiedKnowledge · Epoch AI50.6%
SimpleQA Verified: 50.6%
- Model
- Kimi K3
- Listed as
- kimi-k3_max
- Conditions
- Max
- Unit
- % correct
- Run date
- Aug 27, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Pricing
List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 2026. Moonshot AI is the lab's own API and the reference price for the estimates below.
Price at real agent usageEstimate at list prices
What 1M tokens cost at the reference list price when they split into input, output and cache the way Tokscale users' agents used them. It is not what anyone paid.
- At this model's mix
- $0.78
- Est. per 1M tokens
88% cache reads - At the community mix
- $0.45
- Est. per 1M tokens
96% cache reads - 3:1 input/output blend
- $6
- Per 1M tokens at list prices
No cache
Cache reads were 88% of Kimi K3 tokens in the last 7 days and 96% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.
Assumptions
- Moonshot AI list prices per 1M tokens: input $3, output $15, cache read $0.30, cache write $3.
- This model's mix is its own input, output, cache and reasoning tokens in the last 7 days.
- The community mix is the tokens of every identified model in the last 7 days.
- This is an estimate at list prices, not what anyone paid.
- Reasoning tokens are priced as output.
- Long-context tiers, batch pricing and discounts are not applied.
- Token mixes come from public Tokscale submissions. Hidden accounts are excluded.
List prices by provider
- Moonshot AIReference
kimi-k3$3 input per 1M tokens$15 output per 1M tokens$0.30 cache read per 1M tokens$3 cache write per 1M tokens1.05M tokens of context - Amazon Bedrock
us.moonshotai.kimi-k3$3.30 input per 1M tokens$16.50 output per 1M tokens$0.33 cache read per 1M tokens$4.13 cache write per 1M tokens1.05M tokens of context - OpenRouter
moonshotai/kimi-k3$0.19 input per 1M tokens$16 output per 1M tokens$0.18 cache read per 1M tokensCache write not listed1.05M tokens of context - Vercel AI Gateway
moonshotai/kimi-k3$3 input per 1M tokens$15 output per 1M tokens$0.30 cache read per 1M tokensCache write not listed1M tokens of context
Show 4 more providersShow fewer providers
- Together AI
moonshotai/Kimi-K3$3 input per 1M tokens$15 output per 1M tokens$0.30 cache read per 1M tokensCache write not listed1.05M tokens of context - Fireworks AI
accounts/fireworks/models/kimi-k3$3 input per 1M tokens$15 output per 1M tokens$0.30 cache read per 1M tokensCache write not listed1.05M tokens of context - Deep Infra
moonshotai/Kimi-K3$2.85 input per 1M tokens$14.25 output per 1M tokens$0.28 cache read per 1M tokens$3.56 cache write per 1M tokens1.05M tokens of context - Baseten
moonshotai/Kimi-K3$3 input per 1M tokens$15 output per 1M tokens$0.30 cache read per 1M tokensCache write not listed1.05M tokens of context
Specs
From models.dev (MIT), retrieved Nov 12, 2026, as the lab or the first listed provider reports them. A dash means models.dev does not list the value.
- Context window
- 1.05M tokens
- Max output
- 1.05M tokens
- Input
- Text, Image, Video
- Output
- Text
- Knowledge cutoff
- — not listed
- Release date
- Jul 16, 2026
- Open weights
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
Top public profiles
Public profiles that used Kimi K3 most in this period, by tokens. Hidden accounts are never listed.
- 1
Christopher Butler@techsaint17.3Mtokens$11est. cost3active days$11 est. cost · 3 active days
- 2
DevNewbie1826@DevNewbie18269.7Mtokens$7est. cost1active days$7 est. cost · 1 active days
- 3
Park Hee Chan@channprj5.3Mtokens$5est. cost1active days$5 est. cost · 1 active days
- 4
Rakai Seto@RakaiSeto2.1Mtokens—est. cost2active daysno price · 2 active days
- 5
崔鹏飞 Cui Pengfei@cuipengfei1.8Mtokens$3est. cost3active days$3 est. cost · 3 active days
- 6
曹亚文@bobcyw79.9Ktokens—est. cost1active daysno price · 1 active days
- 7
如是@MiFaZhan3.4Ktokens<$1est. cost2active days<$1 est. cost · 2 active days
More from Moonshot AI
Other Moonshot AI models in the same period, by active users.
- Kimi K3.1 Pro2.8% adoption17active users
- Kimi For Coding0.8% adoption5active users
- Kimi For Coding Highspeed0.3% adoption2active users
- Kimi K2.60.3% adoption2active users
- Kimi K3 Max0.3% adoption2active users
- Kimi K2.7 Code0.3% adoption2active users
Sources and attribution
Each score keeps its own source, unit and run conditions. Tokscale does not combine benchmarks into one score, and a missing score means the model was not evaluated, not that it scored zero.