Kimi K2.5

Moonshot AIKey kimi-k2.5Also reported as moonshotai/kimi-k2.5, hf:moonshotai/kimi-k2.5, pro/moonshotai/kimi-k2.5, hf:moonshotai/Kimi-K2.5, azure/kimi-k2.5, kimi/kimi-k2.5, cr/kimi-k2.5, alicode-intl/kimi-k2.5, baseten/kimi-k2.5, workers-ai/@cf/moonshotai/kimi-k2.5, opencode-go/kimi-k2.5, kmc/kimi-k2.5, @cf/moonshotai/kimi-k2.5, cf/@cf/moonshotai/kimi-k2.5

No usage of Kimi K2.5 in the last 7 days. Showing all time instead.

Rank
#59
by active users
Active users
295
All time
Adoption
12%
of 2,457 accounts
Tokens
51.5B
cache included
Est. cost
$16.7K
at public list prices
First seen
Jan 27, 2026
in any submission

Oct 10, 2024 – Nov 12, 2026 (UTC)2,457 accounts100% of tokens from an identified modelData through Nov 12, 2026, 07:57 UTCHow we count

Daily active users

Distinct accounts that used this model on each UTC day.

0 active users on Nov 12, 2026

Daily active users for Kimi K2.5, Oct 10, 2024 to Nov 12, 2026. Peak 32. Use the left and right arrow keys to read each day.40200
Show daily values
Daily active users and tokens for Kimi K2.5
Date (UTC)Active usersTokens
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Jan 1, 202500
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Jan 1, 202600
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Jan 27, 202642.2M
Jan 28, 20261298.8M
Jan 29, 2026914M
Jan 30, 20261869.7M
Jan 31, 2026780.2M
Feb 1, 202619297M
Feb 2, 202621161M
Feb 3, 202619457M
Feb 4, 202626877.1M
Feb 5, 2026191.5B
Feb 6, 202625678.6M
Feb 7, 202621901.2M
Feb 8, 202616796.3M
Feb 9, 202620584.5M
Feb 10, 202620787.4M
Feb 11, 202618573M
Feb 12, 202618649.2M
Feb 13, 202620457.4M
Feb 14, 202616423.3M
Feb 15, 202615653.1M
Feb 16, 202615542.7M
Feb 17, 202615466.3M
Feb 18, 202615421.3M
Feb 19, 202619570.2M
Feb 20, 202613367.1M
Feb 21, 202614130.8M
Feb 22, 202618193.5M
Feb 23, 202619163.4M
Feb 24, 202619150.5M
Feb 25, 202624230.4M
Feb 26, 202620136.3M
Feb 27, 202620159.4M
Feb 28, 202620384.7M
Mar 1, 202613213.1M
Mar 2, 202623363.8M
Mar 3, 202622609.9M
Mar 4, 202624584.9M
Mar 5, 2026241.1B
Mar 6, 202625411.5M
Mar 7, 202619418.4M
Mar 8, 202616367.8M
Mar 9, 202623539.1M
Mar 10, 202619442.1M
Mar 11, 202624562.1M
Mar 12, 202623356.3M
Mar 13, 202624520M
Mar 14, 202619369.8M
Mar 15, 202626291.1M
Mar 16, 202625320.9M
Mar 17, 202621377.1M
Mar 18, 202630495.9M
Mar 19, 202630223.4M
Mar 20, 202627622M
Mar 21, 202621235.9M
Mar 22, 202618150.4M
Mar 23, 202628465.6M
Mar 24, 202623569.7M
Mar 25, 202627366.6M
Mar 26, 202626444.1M
Mar 27, 202629349.9M
Mar 28, 202623236M
Mar 29, 202620421.2M
Mar 30, 202623447.6M
Mar 31, 202628330.1M
Apr 1, 202625202M
Apr 2, 202623291.2M
Apr 3, 202623230.6M
Apr 4, 202618317.3M
Apr 5, 202615655.1M
Apr 6, 202622498.3M
Apr 7, 202632739M
Apr 8, 202629690.8M
Apr 9, 202623584.1M
Apr 10, 202623948.6M
Apr 11, 2026212.3B
Apr 12, 2026273B
Apr 13, 202628382.6M
Apr 14, 202624663M
Apr 15, 202629435.7M
Apr 16, 202625407.2M
Apr 17, 202621515.3M
Apr 18, 202618256.4M
Apr 19, 202616169.5M
Apr 20, 202627257.8M
Apr 21, 202625359.8M
Apr 22, 202618296.7M
Apr 23, 202616184.6M
Apr 24, 20261099.5M
Apr 25, 20261398.9M
Apr 26, 20266145.7M
Apr 27, 2026951.2M
Apr 28, 202615128.2M
Apr 29, 202612126.6M
Apr 30, 2026989.2M
May 1, 2026818.6M
May 2, 2026828.4M
May 3, 2026826.9M
May 4, 202611176.1M
May 5, 20268186.8M
May 6, 202613133.3M
May 7, 20266208.2M
May 8, 202610141.9M
May 9, 20267145M
May 10, 20267122.4M
May 11, 202610164.5M
May 12, 202613491.6M
May 13, 2026947.8M
May 14, 202610330.6M
May 15, 20267211.6M
May 16, 2026531.7M
May 17, 2026597.6M
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May 19, 20266613.9M
May 20, 2026674M
May 21, 202676.4M
May 22, 202633.8M
May 23, 20265133.1M
May 24, 2026376.1M
May 25, 20264113.8M
May 26, 20266314.9M
May 27, 20264388.1M
May 28, 2026365.1M
May 29, 202667.7M
May 30, 202631.4M
May 31, 20261177K
Jun 1, 20261591K
Jun 2, 2026321M
Jun 3, 2026463.4M
Jun 4, 2026311M
Jun 5, 2026341.9M
Jun 6, 20263149.5M
Jun 7, 2026630.5M
Jun 8, 2026113.3M
Jun 9, 2026759.2M
Jun 10, 20266155.1M
Jun 11, 2026514.6M
Jun 12, 20262439K
Jun 13, 202600
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Jun 16, 20261954K
Jun 17, 202600
Jun 18, 202612.2M
Jun 19, 20262314K
Jun 20, 20262688K
Jun 21, 20261233K
Jun 22, 202622.5M
Jun 23, 20261924K
Jun 24, 2026215.9M
Jun 25, 202621.4M
Jun 26, 202621.5M
Jun 27, 20261354K
Jun 28, 20261177K
Jun 29, 20264189.7M
Jun 30, 20262107.1M
Jul 1, 20262120.3M
Jul 2, 20262151M
Jul 3, 20263111.4M
Jul 4, 202621.5M
Jul 5, 2026156K
Jul 6, 20263177.8M
Jul 7, 20264217.5M
Jul 8, 20264233.7M
Jul 9, 20263105.8M
Jul 10, 20264187.9M
Jul 11, 2026432.7M
Jul 12, 2026354.8M
Jul 13, 20265153.8M
Jul 14, 20264156.2M
Jul 15, 20262173.9M
Jul 16, 20264172.9M
Jul 17, 20266204.2M
Jul 18, 202628.4M
Jul 19, 2026255.1M
Jul 20, 20263168.2M
Jul 21, 20266177.9M
Jul 22, 20263240.1M
Jul 23, 20261122.2M
Jul 24, 2026249.9M
Jul 25, 202600
Jul 26, 202600
Jul 27, 20262234M
Jul 28, 20263143.9M
Jul 29, 20262113.5M
Jul 30, 202600
Jul 31, 202612M
Aug 1, 2026147.6M
Aug 2, 202614M
Aug 3, 202600
Aug 4, 202600
Aug 5, 202615.1M
Aug 6, 202616.3M
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Aug 11, 202616.2M
Aug 12, 202615.1M
Aug 13, 20261949K
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Aug 18, 20261710K
Aug 19, 20262459K
Aug 20, 202632.5M
Aug 21, 20262382K
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Aug 25, 20261638K
Aug 26, 202621.6M
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Oct 13, 202611.9M
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Token mix

How the 51.5B tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input52%
  • Output0.5%
  • Cache read47%
  • Cache write0.5%
  • Reasoning<0.1%

Agents that carry Kimi K2.5

Each agent's share of this model's tokens, and how many accounts used the model through it.

  1. OpenCode59% of this model's tokens200active users
  2. Claude Code16% of this model's tokens53active users
  3. OpenClaw15% of this model's tokens32active users
  4. Pi3.0% of this model's tokens22active users
  5. Hermes Agent3.0% of this model's tokens19active users
  6. Synthetic2.2% of this model's tokens2active users
  7. Cursor0.9% of this model's tokens10active users
  8. Qwen0.4% of this model's tokens5active users
  9. Jcode0.3% of this model's tokens2active users
  10. Cline<0.1% of this model's tokens1active users
  11. CodeBuddy<0.1% of this model's tokens5active users
  12. Gajae Code<0.1% of this model's tokens3active users
  13. Oh My Pi<0.1% of this model's tokens5active users
  14. Kilo<0.1% of this model's tokens7active users
  15. MiMo Code<0.1% of this model's tokens3active users
  16. WorkBuddy<0.1% of this model's tokens1active users
  17. Codex CLI<0.1% of this model's tokens3active users
  18. Command Code<0.1% of this model's tokens2active users
  19. Kimchi<0.1% of this model's tokens1active users
  20. Gemini CLI<0.1% of this model's tokens1active users
  21. 9Router<0.1% of this model's tokens1active users

Benchmarks

The best result for Kimi K2.5 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.

All benchmarks
  • Epoch Capabilities IndexOverall · Epoch AI
    148.8
    Rank #72 of 158 matched modelsNo conditions reportedModel released Jan 27, 2026Source: epoch.ai (opens in a new tab)
  • WebDev ArenaWeb development · LMArena
    1,437
    Rank #67 of 110 matched modelsNo conditions reportedPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
    Show 1 more result with other run conditionsHide the other results
    • Listed as Kimi K2.5 (Instant)
      1,405
      No conditions reportedPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • Humanity's Last ExamReasoning · Center for AI Safety and Scale AI, collected by Epoch AI
    24.4%
    Rank #16 of 40 matched modelsNo conditions reportedModel released Jan 27, 2026Source: lastexam.ai (opens in a new tab)
  • SimpleQA VerifiedKnowledge · Epoch AI
    34.3%
    Rank #59 of 86 matched modelsNo conditions reportedRun date Aug 27, 2026Source: epoch.ai (opens in a new tab)
  • SWE-bench VerifiedHistory · Epoch AI
    73.8%
    Rank #17 of 32 matched modelsNo conditions reportedRun date Feb 17, 2026Source: epoch.ai (opens in a new tab)

    History only. Newer agentic coding benchmarks are on the main board.

Not evaluated: GPQA Diamond, FrontierMath Tiers 1–3 (v2), Terminal-Bench 4.1, CursorBench, DeepSWE, FrontierSWE, Agent Arena. A missing result is not a score of zero.

Pricing

List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 2026. Amazon Bedrock is the reference price for the estimates below. It is the first provider listed; models.dev lists no price from the lab's own API.

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
$0.61
Est. per 1M tokens
47% cache reads
At the community mix
$0.62
Est. per 1M tokens
93% cache reads
3:1 input/output blend
$1.20
Per 1M tokens at list prices
No cache

Cache reads were 47% of Kimi K2.5 tokens across all time and 93% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.

Assumptions

  • Amazon Bedrock list prices per 1M tokens: input $0.60, output $3.
  • This model's mix is its own input, output, cache and reasoning tokens across all time.
  • The community mix is the tokens of every identified model across all time.
  • 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.
  • No cache-read price is listed, so cache reads are priced as input.
  • No cache-write price is listed, so cache writes are priced as input.

List prices by provider

  • Amazon BedrockReferencemoonshotai.kimi-k2.5
    $0.60 input per 1M tokens$3 output per 1M tokensCache read not listedCache write not listed262K tokens of context
  • Azurekimi-k2.5
    $0.60 input per 1M tokens$3 output per 1M tokensCache read not listedCache write not listed262K tokens of context
  • OpenRoutermoonshotai/kimi-k2.5
    $0.53 input per 1M tokens$3.33 output per 1M tokens$0.21 cache read per 1M tokensCache write not listed262K tokens of context
  • Vercel AI Gatewaymoonshotai/kimi-k2.5
    $0.60 input per 1M tokens$3 output per 1M tokensCache read not listedCache write not listed256K tokens of context
  • Deep Inframoonshotai/Kimi-K2.5
    $0.45 input per 1M tokens$2.25 output per 1M tokens$0.07 cache read per 1M tokensCache write not listed262K 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
262K tokens
Max output
262K tokens
Input
Text, Image
Output
Text
Knowledge cutoff
Jan 2025
Release date
Jan 27, 2026
Open weights
Yes
Reasoning
Yes
Tool calling
Yes

Top public profiles

Public profiles that used Kimi K2.5 most in this period, by tokens. Hidden accounts are never listed.

  1. 1Jonathannkayy@Jonathannkayy5.4Btokens$2.8Kest. cost7active days$2.8K est. cost · 7 active days
  2. 2alexmhtop@alexmhtop4.8Btokens$1.5Kest. cost101active days$1.5K est. cost · 101 active days
  3. 3Alexander B@rygel3.8Btokens$2Kest. cost25active days$2K est. cost · 25 active days
  4. 4If you read this you're gay@zhaochenyuan00273.2Btokens$425est. cost23active days$425 est. cost · 23 active days
  5. 5Leon Alvarez Del Canto@LeonAlvarez3.1Btokens$1.3Kest. cost50active days$1.3K est. cost · 50 active days
  6. 6Owen@kang-heewon2.8Btokens$1.6Kest. cost40active days$1.6K est. cost · 40 active days
  7. 7Emmanuel Olowe@isEmmanuelOlowe2.3Btokens$1.2Kest. cost49active days$1.2K est. cost · 49 active days
  8. 8Bellman@Yeachan-Heo1.8Btokens$264est. cost25active days$264 est. cost · 25 active days
  9. 9Haining Yu@Golden0Voyager1.7Btokens$245est. cost25active days$245 est. cost · 25 active days
  10. 10Prakersh Maheshwari@prakersh1.3Btokens$582est. cost15active days$582 est. cost · 15 active days

More from Moonshot AI

Other Moonshot AI models in the same period, by active users.

  1. Kimi K315% adoption369active users
  2. Kimi K2.614% adoption335active users
  3. Kimi For Coding10% adoption247active users
  4. Kimi K2.5 Free9.6% adoption235active users
  5. Kimi K2.7 Code9.0% adoption222active users
  6. Kimi K2 Thinking3.2% adoption78active users
All models

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.

  • Epoch AIEpoch Capabilities Index, Epoch's own runs and the external results it collects.CC BY 4.0 · Retrieved Nov 12, 2026 · Data through Nov 9, 2026Epoch AI, ‘Capabilities & benchmarking’. Published online at epoch.ai. Retrieved from https://epoch.ai/benchmarks.
  • LMArenaWebDev Arena and Agent Arena leaderboards.CC BY 4.0 · Retrieved Oct 21, 2026 · Data through Oct 19, 2026LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), Hugging Face.
  • models.devSpecs and list prices per provider.MIT · Retrieved Nov 12, 2026models.dev, an open catalog of AI models (github.com/anomalyco/models.dev).
  • TokscaleActive users, tokens and token mix from public Tokscale submissions. Hidden accounts are excluded.How we count