Models › Kimi-K2 family

Kimi-K2-Instruct

moonshotai Kimi (Modified MIT) (modified-mit) out 16 months ago

1.03T parameters (8 of 384 experts per token), text in, text out, 128K tokens of context. 2 providers serve it from $0.57 per million input tokens.

Verified 2026-10-25 Rank #53 in LLMs · score 107.2 method v1 Sources 4 Downloads 30d 343.2k · #204 of 619 tracked

Can I run it?616GB at INT4Estimateneeds a multi-GPU nodemethod
What does it cost?$0.57/1M incheapest of 1Novitafp82026-10-25
Can I use it?Kimi (Modified MIT) (modified-mit)MIT plus a display-the-model-name attribution requirement for products above 100M MAU or US$20M monthly revenue; the exact wording is in each repo's LICENSElicence file2026-10-24
Does it work?59.06on BFCL#2 of 28 measuredleaderboard2026-10-25
Do people use it?343.2kdownloads, 30 days#204 of 619 we track · top 33%+4 likes, downloads −2% this week3,007 likesHugging Face2026-10-24

Worth reading

Picked by rule from 20 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 936 HN pts

Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

Kimi K2 Thinking is an open-source thinking agent model that executes up to 300 sequential tool calls to solve complex problems.

  • The model marks latest efforts in test-time scaling by scaling thinking tokens and tool calling steps.
  • K2 Thinking is live on kimi.com under the chat mode with full agentic mode available soon.
moonshotai.github.io 2025-11-06 936 HN pts · 427 comments 34k words about the Kimi K2 family family
Latest · 4 months ago

Kimi K2.7 Code is generally available in GitHub Copilot

Kimi K2.7 Code is generally available in GitHub Copilot as the first open-weight model offered as a selectable option in the Copilot model picker.

github.blog 2026-07-02 417 HN pts · 185 comments 317 words about the Kimi K2 family family

20 best of 20 tracked

The 2 picks above, plus these 18. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleKimi K2.6: Advancing open-source codingkimi.com · 2026-04-20 · 710 HN pts · official
  2. ArticleKimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Modelkimi.com · 2026-01-27 · 502 HN pts · official
  3. ArticleKimi K2: Open Agentic Intelligencekimi.com · undated · official
  4. DiscussionKimi K2 is a state-of-the-art mixture-of-experts (MoE) language modeltwitter.com · 2025-07-11 · 348 HN pts
  5. DiscussionKimi K2 1T model runs on 2 512GB M3 Ultrastwitter.com · 2025-12-14
  6. CodeKimi K2 is a state-of-the-art mixture-of-experts (MoE) language modelgithub.com · 2025-07-12 · 352 HN pts
  7. CodeKimi-K2 Tech Report [pdf]github.com · 2025-07-21 · 84 HN pts
  8. PaperKimi K2: Open Agentic Intelligencearxiv.org · undated
  9. ArticleNo BS: Kimi K2 Reviewhuggingface.co · undated
  10. ArticleComparing GPT-5.4, Opus 4.6, GLM-5.1, Kimi K2.5, MiMo V2 Pro and MiniMax M2.7codejam.info · 2026-04-18
  11. CodeMoonshotAI/Kimi-K2github.com · undated
  12. ArticleMoonshotai/Kimi-K2-Instructsimonwillison.net · 2025-07-11
  13. ArticleMoonshot AI's Kimi K2 outperforms GPT-4 in key benchmarks – and it's freeventurebeat.com · 2025-07-13
  14. ArticleKimi K2: Architecture, Capabilities & Benchmarksfireworks.ai · 2025-08-01
  15. DiscussionKimi-K2-Thinking 1T at 28 tok/SEC with MLX tensor paralleltwitter.com · 2025-12-19
  16. Articlehttps://intuitionlabs.ai/articles/kimi-k2-technical-deep-diveintuitionlabs.ai · undated
  17. ArticleAnalysis of the Kimi K2 Open-Weight Language Modelintuitionlabs.ai · undated
  18. ArticleKimi K2 | Open Laboratoryopenlaboratory.com · undated

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

#Cheapest 2In /1MOut /1MContextQuantSeen
1 Novita $0.57 $2.30 128K fp8 Oct 25
2 Featherless — — — — Oct 25

All 2 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Served by 2 API providers; 18 quantized builds for local use.

  • As a service2Featherless, Novita
  • On own hardware18quantized builds
  • Built on2217 fine-tunes · 5 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: hadadxyz/ai ♥ 819 · huggingface-projects/ai-video-composer ♥ 664 · pliny-the-prompter/obliteratus ♥ 621 · umint/ai ♥ 258

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 616 GB at INT4, 1.23 TB at FP8, 2.46 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 616 GB of weights (estimate; KV cache and activations extra)INT4616 GBFP8: about 1.23 TB of weights (estimate; KV cache and activations extra)FP81.23 TBBF16: about 2.46 TB of weights (estimate; KV cache and activations extra)BF162.46 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 616 GB of weights (estimate; KV cache and activations extra)INT4616 GBFP8: about 1.23 TB of weights (estimate; KV cache and activations extra)FP81.23 TBBF16: about 2.46 TB of weights (estimate; KV cache and activations extra)BF162.46 TB

Estimate params × bytes per param × 1.2 · how we estimate

How it scores

Third-party measurements only; author-reported numbers stay in Reference, marked as such.

#2 of 28 on BFCL so far

59.06. Each tick is a model on this site with a measured BFCL row; author-reported numbers stay in Reference. Percent (overall accuracy). FC

zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010059.06 · #2 of 28
zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010059.06 · #2 of 28

Source · leaderboard · 2026-10-25

The story so far

Weights, every dated article, and repo commits on one rail.

7 dated articles and 30 repo commits so far

Teal is the weights (2025-07-11), amber triangles are 7 dated articles (each links to it), grey ticks are repo commits.

AugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-04-23: 3 repo commits2026-01-30: 1 repo commit2025-11-07: 1 repo commit2025-11-04: 1 repo commit2025-10-22: 2 repo commits2025-10-10: 1 repo commit2025-09-05: 1 repo commit2025-08-11: 3 repo commits2025-07-28: 1 repo commit2025-07-22: 1 repo commit2025-07-21: 1 repo commit2025-07-18: 2 repo commits2025-07-15: 5 repo commits2025-07-14: 1 repo commit2025-07-13: 1 repo commit2025-07-12: 1 repo commit2025-07-11: 4 repo commitsKimi K2.7 Code is generally available in GitHub Copilot · 2026-07-02Kimi K2.6: Advancing open-source coding · 2026-04-20Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model · 2026-01-27Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model · 2025-11-06Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model · 2025-07-11Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model · 2025-07-12Kimi-K2 Tech Report [pdf] · 2025-07-21Weights 2025-07-11 · 2025-07-11Weights 2025-07-11now · 2 providers
AugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-04-23: 3 repo commits2026-01-30: 1 repo commit2025-11-07: 1 repo commit2025-11-04: 1 repo commit2025-10-22: 2 repo commits2025-10-10: 1 repo commit2025-09-05: 1 repo commit2025-08-11: 3 repo commits2025-07-28: 1 repo commit2025-07-22: 1 repo commit2025-07-21: 1 repo commit2025-07-18: 2 repo commits2025-07-15: 5 repo commits2025-07-14: 1 repo commit2025-07-13: 1 repo commit2025-07-12: 1 repo commit2025-07-11: 4 repo commitsKimi K2.7 Code is generally available in GitHub Copilot · 2026-07-02Kimi K2.6: Advancing open-source coding · 2026-04-20Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model · 2026-01-27Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model · 2025-11-06Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model · 2025-07-11Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model · 2025-07-12Kimi-K2 Tech Report [pdf] · 2025-07-21Weights 2025-07-11 · 2025-07-11Weights 2025-07-11now · 2 providers

What people built on it

40 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

40 derivatives on the Hub, led by 18 quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

18 quantized (Hugging Face model tree)17 fine-tunes (Hugging Face model tree)5 adapters (Hugging Face model tree)18 quantized17 fine-tunes5 adapters
18 quantized (Hugging Face model tree)17 fine-tunes (Hugging Face model tree)5 adapters (Hugging Face model tree)18 quantized17 fine-tunes5 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

7 rows

Benchmarks 7 rows

measured third-party leaderboard row (4)author-reported number from the model card or eval files (3)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
measured Aider Polyglot · Write code with agents59.1 percent (pass_rate_2)edit_format=diffleaderboard2025-07-17
measured BFCL · Call tools and APIs59.06 percent (overall accuracy)FC
5 sub-scores
  • Non-Live AST Acc: 81.6
  • Live Acc: 78.68
  • Multi Turn Acc: 50.63
  • Web Search Acc: 66.5
  • Memory Acc: 29.03
leaderboard2026-10-25
measured SWE-bench Verified · Write code with agents53.4 percent resolvedagent: CodeSweep - SWE-agentleaderboard2025-08-04
measured SWE-bench Verified · Write code with agents43.8 percent resolvedagent: mini-SWE-agentleaderboard2025-08-07
author-reported apex-swe (mercor/APEX-SWE)11.2harness: claude_code; reasoning_effort: thinkingAPEX-SWE Leaderboard2026-04-22
author-reported SWE_Bench_Pro (ScaleAI/SWE-bench_Pro)27.67—SWE-Bench Pro official evaluation results2026-10-24
author-reported terminalbench_2 (harborframework/terminal-bench-2.0)27.8agent: Terminus 2Terminal-Bench Leaderboard2025-11-01

Use-case evidence

5 use cases

What it is good at 5 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

21 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDeepseekV3ForCausalLM
Model typekimi_k2
Dtypebfloat16
Params by dtypeBF16: 2,514,970,968 · F32: 23,040 · F8_E4M3: 1,023,893,241,856
Size bandXXL
MoEyes
Experts384
Experts per token8
Context length131,072 (config.json max_position_embeddings)
LicenseKimi (Modified MIT) (modified-mit) (other)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DeepseekV3ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · DeepseekV3ForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Kimi-K2-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)2.46 TB · Cluster (≥640 GB)
FP8 (1 byte/param)1.23 TB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)616 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original release (shipped quantized by the lab) Family: moonshotai/Kimi-K2 40 derivatives on the Hub

quantized: 18finetune: 17adapter: 5

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
unsloth/Kimi-K2-Instruct-GGUF quantized GGUF — — other unsloth 229 705.2k
mlx-community/Kimi-K2-Instruct-4bit quantized -4bit 1.03T XXL other mlx-community 13 124k
RedHatAI/Kimi-K2-Instruct-quantized.w4a16 quantized -quant 1.03T XXL other RedHatAI 12 19.7k
ubergarm/Kimi-K2-Instruct-GGUF quantized GGUF — — other ubergarm 22 11.7k
DevQuasar/moonshotai.Kimi-K2-Instruct-GGUF quantized GGUF — — — DevQuasar 2 9k
unsloth/Kimi-K2-Instruct quantized — 1.03T XXL other unsloth 22 6k
KVCache-ai/Kimi-K2-Instruct-GGUF quantized GGUF — — other KVCache-ai 31 5.1k
UnicornChan/Kimi-K2-GPTQ-4b-128g-experts quantized GPTQ 1.03T XXL mit UnicornChan 1 6.2k
gabriellarson/Kimi-K2-Instruct-GGUF quantized GGUF — — other gabriellarson 5 4.7k
jobs-git/Kimi-K2-Instruct-GGUF quantized GGUF — — other jobs-git 0 4.9k
mlx-community/moonshotai_Kimi-K2-Instruct-mlx-3bit quantized mlx 1.03T XXL other mlx-community 2 4.4k
unsloth/Kimi-K2-Instruct-BF16 finetune -BF16 1.03T XXL other unsloth 4 3.7k
FinnTheAI/Kimi-K2.6-SmartQuant-GGUF quantized GGUF — — other FinnTheAI 3 3.6k
Deviad/Kimi-K2.6-JANGTQ_3L quantized — 83.4B XL other Deviad 0 3k
yujiepan/kimi-k2-tiny-random finetune — 10.9M S — yujiepan 2 2.3k
anikifoss/Kimi-K2-Instruct-DQ4_K quantized GGUF — — other anikifoss 4 1.4k
terribleplan/moonshotai_Kimi-K2-Instruct_GGUF quantized GGUF — — — terribleplan 0 1.6k
DevQuasar/moonshotai.Kimi-K2-Instruct-BF16 finetune -BF16 1.03T XXL — DevQuasar 3 1.1k
luisra/Kimi-K2-Instruct-4bit quantized -4bit — — other luisra 0 933
tiny-random/kimi-k2 finetune — 10.9M S — tiny-random 4 521
iSolver-AI/test123-quantized.w4a16 quantized -quant — — apache-2.0 iSolver-AI 0 190
mradermacher/Kimi-K2-Instruct-GGUF finetune GGUF — — other mradermacher 3 0
mradermacher/Kimi-K2-Instruct-i1-GGUF finetune GGUF — — other mradermacher 3 0
OPEA/Kimi-K2-Instruct-int4-mixed-AutoRound quantized int4 1.34B S — OPEA 0 128
Neha555Altaf/Food-nutrient-analyzer-II adapter — — — mit Neha555Altaf 1 0
Divinci-AI/kimi-k2-vindex finetune — — — cc-by-nc-4.0 Divinci-AI 1 0
Divinci-AI/kimi-k2-instruct-vindex finetune — — — other Divinci-AI 1 0
joebruce1313/oblivion adapter — — — mit joebruce1313 0 0
Rcgtt/RC-CHPA adapter — — — mit Rcgtt 0 0
dayealim/modelname finetune — — — apache-2.0 dayealim 0 0
VirKapur/College-Assistant finetune — — — mit VirKapur 0 0
muskan3109/sanskrit-hi finetune — — — apache-2.0 muskan3109 0 0
Drexrrr/yes adapter — — — llama4 Drexrrr 0 0
stupidss/Wanderereng adapter — — — other stupidss 0 0
EYDY/ZEKO finetune — — — artistic-2.0 EYDY 0 0
alipress/AliSHaker finetune — — — creativeml-openrail-m alipress 0 0
flowbaat/BaatFlow finetune — — — mit flowbaat 0 0
aarsh137/sadad finetune — — — apache-2.0 aarsh137 0 0
Kunal650/kiwi2 finetune — — — mit Kunal650 0 0
KurniaKadir/hrmy finetune — — — apache-2.0 KurniaKadir 0 0

Repository changelog

30 commits

Repository changelog 30 commits

DateCommitSHA
2026-04-23 Add Terminal-Bench evaluation result (27.8%) (#64) fd1984e
2026-04-23 Add SWE-Bench Pro evaluation results (#65) 4bbe370
2026-04-23 Create .eval_results/apex-swe.yaml (#67) e6e15d4
2026-01-30 Transformers v5 support (#62) 1cbe779
2025-11-07 Adjust number of reserved tokens to match the model (#15) c2fee60
2025-11-04 Update tokenization_kimi.py (#56) 49e03b0
2025-10-22 remove-added-functions.-in-tool_call_id (#60) 0102674
2025-10-22 fix-apply-chat-template (#59) cc61331
2025-10-10 update chat-template and tokenizer (#58) 874f2bb
2025-09-05 Add `new_version` tag with the updated model (#57) 2a19363
2025-08-11 Update chat_template.jinja (#49) c52f808
2025-08-11 Update README.md c499175
2025-08-11 standalone chat template (#47) b215bf7
2025-07-28 fix typo (#33) 0826e83
2025-07-22 Updated Readme with released Technical Report link (#35) 6129c6d
2025-07-21 fix typos d2513b8
2025-07-18 Update README.md 4f23950
2025-07-18 update chat template 6a1f5e6
2025-07-15 Update README.md d1e2b19
2025-07-15 update tokenizer_config 37a95d8
2025-07-15 Update tokenizer_config.json (#13) 6be65c0
2025-07-15 Update tokenizer_config.json (#13) 1221eb6
2025-07-15 fix: allow tokenize special tokens 02de71c
2025-07-14 comming soon -> coming soon (#12) 841a184
2025-07-13 add github link in readme 23bdc5f
2025-07-12 update third party notices 2f7e011
2025-07-11 update banner c9a3d34
2025-07-11 Upload folder using huggingface_hub b543046
2025-07-11 update readme 7f98307
2025-07-11 Update docs/deploy_guidance.md 2bfbc7b

All providers

2 providers · 2 endpoints

Where to run 2 providers · 2 endpoints

Hosted endpoints observed for Kimi-K2-Instruct, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.57/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Novita
hf, openrouter
moonshotai/kimi-k2-instruct 128K fp8 $0.57 $2.30 83.5 1198 ms yes live 2026-10-25
Featherless
hf
moonshotai/Kimi-K2-Instruct — — — — — — — live 2026-10-25

Articles and coverage

20 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official3

kimi.com2026-04-20officialauthorityHN front pageabout the Kimi K2 familyHN 710 pts · 372 comments2,299 words
Kimi K2.6 is an open-source model released for advanced coding, long-horizon execution, and agent swarm capabilities. It is available via Kimi.com, the Kimi App, the API, and Kimi Code, showing a 12% increase in code generation accuracy and a 96.60% tool invocation success rate. The model scales agent swarms to 300 sub-agents executing across 4,000 coordinated steps simultaneously.written, reviewed, cited
  • Kimi K2.6 optimizes model inference in Zig, a highly niche programming language, to demonstrate exceptional out-of-distribution generalization.
  • The model reconfigured the exchange-core thread topology from 4ME+2RE to 2ME+1RE to extract a 185% medium throughput leap.
kimi.com2026-01-27officialauthorityHN front pageabout the Kimi K2 familyHN 502 pts · 239 comments1,856 words
Kimi K2.5 self-directs an agent swarm with up to 100 sub-agents executing parallel workflows across up to 1,500 tool calls. This swarm architecture reduces execution time by up to 4.5x compared to a single-agent setup without predefined workflows.generated, cited
kimi.comofficialauthorityabout the Kimi K2 family1,329 words
Kimi K2 is a Mixture-of-Experts model with 32 billion activated parameters and 1 trillion total parameters that is now open-sourcing Kimi-K2-Base and Kimi-K2-Instruct. The model was pre-trained on 15.5T tokens using the MuonClip optimizer, which stabilizes training via a qk-clip technique to prevent exploding attention logits. Users can deploy it on engines like vLLM or SGLang, and examples show it generating insights through 16 IPython calls or orchestrating 17 seamless tool calls for travel planning.written, reviewed, cited
  • The model is a reflex-grade Instruct variant that operates without long thinking capabilities.
  • Vision features are currently not supported for the Kimi K2 model.
  • One-shot prompting yields performance degradation compared to using the model under an agentic framework.

Analysis6

github.blog2026-07-02authorityHN front pageabout the Kimi K2 familyHN 417 pts · 185 comments317 words
Kimi K2.7 Code is generally available in GitHub Copilot as the first open-weight model offered as a selectable option in the Copilot model picker. The model is beginning to roll out to Copilot Pro, Pro+, and Max plans within Visual Studio Code version 1.127.0 or later, while it remains off by default for Copilot Business and Copilot Enterprise requiring plan administrators to enable the policy. Rollout will be gradual as GitHub continues to monitor the model's quality and performance.written, reviewed, cited
moonshotai.github.io2025-11-06HN front pageabout the Kimi K2 familyHN 936 pts · 427 comments34,041 words
Kimi K2 Thinking is an open-source thinking agent model that executes up to 300 sequential tool calls to solve complex problems. The model achieves 44.9% on HLE with tools, 60.2% on BrowseComp, and 71.3% on SWE-Bench Verified. It demonstrated deep reasoning by solving a PhD-level mathematics problem through 23 interleaved reasoning and tool calls.written, reviewed, cited
  • The model marks latest efforts in test-time scaling by scaling thinking tokens and tool calling steps.
  • K2 Thinking is live on kimi.com under the chat mode with full agentic mode available soon.
github.com2025-07-12authorityHN front pageabout the Kimi K2 familyHN 352 pts · 2 comments2,355 words
github.com2025-07-21authorityabout the Kimi K2 familyHN 84 pts · 6 comments
arxiv.orgauthorityabout the Kimi K2 family
huggingface.coauthorityabout the Kimi K2 family

Community1

twitter.com2025-07-11HN front pageabout the Kimi K2 familyHN 348 pts · 179 comments
All coverage tracked for this model (20) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Kimi K2: Open Agentic Intelligencekimi.com official—2026-10-07exa
Kimi K2: Open Agentic Intelligencearxiv.org—2026-10-07exa
No BS: Kimi K2 Reviewhuggingface.co—2026-10-07exa
MoonshotAI/Kimi-K2github.com—2026-10-07exa
https://intuitionlabs.ai/articles/kimi-k2-technical-deep-diveintuitionlabs.ai—2026-10-07exa
Analysis of the Kimi K2 Open-Weight Language Modelintuitionlabs.ai—2026-10-07exa
Kimi K2 | Open Laboratoryopenlaboratory.com—2026-10-07exa
Kimi K2.7 Code is generally available in GitHub Copilotgithub.blog2026-07-022026-10-07hn
Kimi K2.6: Advancing open-source codingkimi.com official2026-04-202026-10-07hn
Comparing GPT-5.4, Opus 4.6, GLM-5.1, Kimi K2.5, MiMo V2 Pro and MiniMax M2.7codejam.info2026-04-182026-10-07hn
Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Modelkimi.com official2026-01-272026-10-07hn
Kimi-K2-Thinking 1T at 28 tok/SEC with MLX tensor paralleltwitter.com2025-12-192026-10-07hn
Kimi K2 1T model runs on 2 512GB M3 Ultrastwitter.com2025-12-142026-10-07hn
Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning modelmoonshotai.github.io2025-11-062026-10-07hn
Kimi K2: Architecture, Capabilities & Benchmarksfireworks.ai2025-08-012026-10-07exa
Kimi-K2 Tech Report [pdf]github.com2025-07-212026-10-07hn
Moonshot AI's Kimi K2 outperforms GPT-4 in key benchmarks – and it's freeventurebeat.com2025-07-132026-10-07hn
Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language modelgithub.com2025-07-122026-10-07hn
Moonshotai/Kimi-K2-Instructsimonwillison.net2025-07-112026-10-07hn
Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language modeltwitter.com2025-07-112026-10-07hn

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:33 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:00 UTC · Last verified 2026-10-25

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