Models › Kimi-K2 family

Kimi-K2-Thinking

moonshotai Kimi (Modified MIT) (modified-mit) out 12 months ago

1.03T parameters (8 of 384 experts per token), text in, text out, 256K tokens of context. 3 providers serve it from $0.60 per million input tokens.

Verified 2026-10-25 Rank #129 in LLMs · score 94.7 method v1 Sources 4 Downloads 30d 47.7k · #386 of 619 tracked

What does it cost?$0.60/1M incheapest of 1Novitabf162026-10-24
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?63.4on SWE-bench Verified#8 of 22 measuredleaderboard2025-12-10
Do people use it?47.7kdownloads, 30 days#386 of 619 we track · below the median+4 likes, downloads −12% this week1,730 likesHugging Face2026-10-24

Worth reading

Picked by rule from 23 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
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

23 best of 23 tracked

The 3 picks above, plus these 20. 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. DiscussionKimi K2 is a state-of-the-art mixture-of-experts (MoE) language modeltwitter.com · 2025-07-11 · 348 HN pts
  4. DiscussionKimi K2 1T model runs on 2 512GB M3 Ultrastwitter.com · 2025-12-14
  5. CodeKimi K2 is a state-of-the-art mixture-of-experts (MoE) language modelgithub.com · 2025-07-12 · 352 HN pts
  6. CodeKimi-K2 Tech Report [pdf]github.com · 2025-07-21 · 84 HN pts
  7. Paper[PDF] Kimi K2: Open Agentic Intelligence - arXivarxiv.org · undated
  8. ArticleComparing GPT-5.4, Opus 4.6, GLM-5.1, Kimi K2.5, MiMo V2 Pro and MiniMax M2.7codejam.info · 2026-04-18 · 6 HN pts
  9. ArticleIntroducing Kimi K2 Thinkingkimi.ai · undated · official
  10. ArticleKimi K2 Thinking: How to Run Locallydocs.unsloth.ai · 2025-11-08
  11. ArticleChina's AI Upstart Moonshot Stuns Valley Again with a $4.6M Wonderentropytown.com · 2025-11-09
  12. Article5 Thoughts on Kimi K2 Thinkinginterconnects.ai · 2025-11-09
  13. ArticleMoonshot AI's Kimi K2 outperforms GPT-4 in key benchmarks – and it's freeventurebeat.com · 2025-07-13
  14. DiscussionKimi-K2-Thinking 1T at 28 tok/SEC with MLX tensor paralleltwitter.com · 2025-12-19
  15. ArticleKimi K2 Thinkingmoonshotai.github.io · undated
  16. Articlemoonshotai / kimi-k2-thinkingdocs.api.nvidia.com · undated
  17. ArticleKimi K2 Thinking Explained: Architecture, Benchmarks & API ...qubrid.com · undated
  18. ArticleKimi K2 Thinking: what 200+ tool calls mean for productionlambda.ai · 2026-02-04
  19. ArticleKimi K2 Thinking: How Moonshot's $4.6M Open-Source Model Challenges GPT-5 With Trillion-Parameter MoE - PulseMarkpulsemark.ai · 2025-11-17
  20. ArticleKimi K2 Thinking: Open-Source LLM Guide, Benchmarks, and Tools | DataCampdatacamp.com · 2025-11-10

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 $0.60 $2.50 256K bf16 Oct 24
2 Featherless — — — — Oct 25

All 3 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 3 API providers; 18 quantized builds for local use.

  • As a service3Featherless, Google, Novita
  • On own hardware18quantized builds
  • Built on2312 fine-tunes · 10 adapters · 1 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: akhaliq/anycoder ♥ 3,321 · hadadxyz/ai ♥ 819 · umint/ai ♥ 258 · llamameta/Free-kimi-k2-thinking ♥ 16

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.

#8 of 22 on SWE-bench Verified so far

63.4. Each tick is a model on this site with a measured SWE-bench Verified row; author-reported numbers stay in Reference. Percent resolved. agent: mini-SWE-agent

MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2.5: 70.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3.2: 70 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010063.4 · #8 of 22
MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2.5: 70.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3.2: 70 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010063.4 · #8 of 22

Source · leaderboard · 2025-12-10

The story so far

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

Most coverage came in the first two weeks

Teal is the weights (2025-11-04), amber triangles are 8 dated articles (each links to it), grey ticks are repo commits.

What people built on it

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

41 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)12 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)10 adapters (Hugging Face model tree)18 quantized12 fine-tunes1 merge10 adapters
18 quantized (Hugging Face model tree)12 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)10 adapters (Hugging Face model tree)18 quantized12 fine-tunes1 merge10 adapters

All variants →

Reference

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

Benchmarks

28 rows

Benchmarks 28 rows

measured third-party leaderboard row (1)author-reported number from the model card or eval files (27)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 SWE-bench Verified · Write code with agents63.4 percent resolvedagent: mini-SWE-agentleaderboard2025-12-10
author-reported AIME 2025 · Reason through hard problems94.5no toolsmodel author2026-10-07
author-reported AIME 2025 · Reason through hard problems99.1w/ pythonmodel author2026-10-07
author-reported AIME 2025 · Reason through hard problems100heavymodel author2026-10-07
author-reported BrowseComp60.2w/ toolsmodel author2026-10-07
author-reported BrowseComp-ZH62.3w/ toolsmodel author2026-10-07
author-reported FinSearchComp-T347.4w/ toolsmodel author2026-10-07
author-reported Frames87w/ toolsmodel author2026-10-07
author-reported GPQA Diamond · Reason through hard problems84.5no toolsmodel author2026-10-07
author-reported HMMT · Reason through hard problems89.4no toolsmodel author2026-10-07
author-reported HMMT · Reason through hard problems95.1w/ pythonmodel author2026-10-07
author-reported HMMT · Reason through hard problems97.5heavymodel author2026-10-07
author-reported HealthBench58no toolsmodel author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems23.9no toolsmodel author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems44.9w/ toolsmodel author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems51heavymodel author2026-10-07
author-reported IMO-AnswerBench · Reason through hard problems78.6no toolsmodel author2026-10-07
author-reported LiveCodeBench · Write code with agents83.1no toolsmodel author2026-10-07
author-reported Longform Writing73.8no toolsmodel author2026-10-07
author-reported MMLU · Reason through hard problems94.4no toolsmodel author2026-10-07
author-reported MMLU-Pro · Reason through hard problems84.6no toolsmodel author2026-10-07
author-reported OJ-Bench (cpp)48.7no toolsmodel author2026-10-07
author-reported SWE-bench · Write code with agents41.9w/ toolsmodel author2026-10-07
author-reported SWE-bench Multilingual · Write code with agents61.1w/ toolsmodel author2026-10-07
author-reported SWE-bench Verified · Write code with agents71.3w/ toolsmodel author2026-10-07
Show all 28 rows (3 more)
BenchmarkValueSetting / notesReported byDate
author-reported SciCode44.8no toolsmodel author2026-10-07
author-reported Seal-056.3w/ toolsmodel author2026-10-07
author-reported Terminal-Bench · Write code with agents47.1w/ simulated tools (JSON)model author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

The architecture utilizes 8 experts per token and supports a 256k context window.[1][2]

  1. experts per token
  2. “256k context window”

Generated 2026-10-07 15:02 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

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: 11,722,193,536 · F32: 23,040 · I32: 1,014,686,023,680
Size bandXXL
MoEyes
Experts384
Experts per token8
Context length262,144 (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-Thinking: 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 41 derivatives on the Hub

quantized: 18finetune: 12adapter: 10merge: 1

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
nvidia/Kimi-K2-Thinking-NVFP4 quantized NVFP4 519B XXL other nvidia 40 652k
mlx-community/Kimi-K2-Thinking quantized — 1.03T XXL other mlx-community 17 122.2k
unsloth/Kimi-K2-Thinking-GGUF quantized GGUF — — other unsloth 115 88.9k
ubergarm/Kimi-K2-Thinking-GGUF quantized GGUF — — other ubergarm 22 19.1k
bartowski/moonshotai_Kimi-K2-Thinking-GGUF quantized GGUF — — other bartowski 5 14.7k
mlx-community/Kimi-K2-Thinking-4bit quantized -4bit 1.03T XXL other mlx-community 9 11.1k
DevQuasar/moonshotai.Kimi-K2-Thinking-GGUF quantized GGUF — — — DevQuasar 5 7.9k
amd/Kimi-K2-Thinking-MXFP4 quantized MXFP4 550B XXL other amd 0 7.8k
moxin-org/Kimi-K2-Thinking-Moxin-GGUF quantized GGUF — — other moxin-org 9 7.3k
amd/Kimi-K2-Thinking-MXFP4-AttnFP8 quantized MXFP4 550B XXL other amd 0 3.3k
mlx-community/Kimi-K2-Thinking-3bit quantized — 1.03T XXL other mlx-community 1 2.4k
unsloth/Kimi-K2-Thinking-BF16 finetune -BF16 — — other unsloth 6 1.8k
squ11z1/Hypnos-Colossus-1T quantized GGUF — — other squ11z1 2 1.9k
amd/Kimi-K2-Thinking-W4A8 quantized — — — other amd 0 1.9k
unsloth/Kimi-K2-Thinking quantized — 1.06T XXL other unsloth 4 1.5k
picklass/Kimi-K2-Thinking-MLX-4.25bit quantized MLX — — — picklass 0 1.5k
nvidia/Kimi-K2-Thinking-Eagle3 finetune — 1.84B S other nvidia 2 995
DevQuasar-2/moonshotai.Kimi-K2-Thinking-BF16 finetune -BF16 1.03T XXL — DevQuasar-2 0 851
ling1000T/Kimi-K2-Thinking-gguf quantized gguf — — other ling1000T 1 500
sdan/jokegen2-1t-rl adapter — — — apache-2.0 sdan 9 0
mradermacher/Kimi-K2-Thinking-i1-GGUF quantized GGUF — — other mradermacher 3 295
luisra/Kimi-K2-Thinking-4bit quantized -4bit — — other luisra 0 202
axs2k/anubis finetune — — — gpl-3.0 axs2k 3 0
sdan/jokegen2-1t-sft adapter — — — apache-2.0 sdan 3 0
UNYIL17/MODEL-ONE-1 merge — — — apache-2.0 UNYIL17 1 0
obalcells/kimi-k2-thinking-mo-midtrained-lora adapter — — — — obalcells 0 50
uwuwuwuwuwuwu/kimi-k2.5-reward-hacking-step-0 adapter — — — — uwuwuwuwuwuwu 0 49
d0ks/perry-anderson-lora adapter — — — apache-2.0 d0ks 0 39
mradermacher/Kimi-K2-Thinking-GGUF finetune GGUF — — other mradermacher 0 0
Mashbox41/faith2 finetune — — — apache-2.0 Mashbox41 0 0
guitar73/guitar adapter — — — openrail guitar73 0 0
firdavsCreative2025/FirdavsDijital-text-classifier finetune — — — mit firdavsCreative2025 0 0
samgiz/docc_e finetune — — — apache-2.0 samgiz 0 0
ADOBET88/pildun2026 finetune — — — apache-2.0 ADOBET88 0 0
wajdi-26/hoom finetune — — — apache-2.0 wajdi-26 0 0
BACCHUS45/Potex adapter — — — apache-2.0 BACCHUS45 0 0
Maxmaxjawa/Scripshit finetune — — — llama3.3 Maxmaxjawa 0 0
arvindcr4/tinker-rl-bench-arch_gsm8k_kimi-k2 finetune — — — mit arvindcr4 0 0
arvindcr4/tinker-rl-arch_gsm8k_kimi-k2-kimi-k2 adapter — — — apache-2.0 arvindcr4 0 0
aidenkbm/jokegen2-1t-rl adapter — — — apache-2.0 aidenkbm 0 0

Repository changelog

18 commits

Repository changelog 18 commits

DateCommitSHA
2026-01-30 Transformers v5 support (#53) a51ccc0
2025-11-08 fix-default-system-prompt (#12) 6126819
2025-11-07 Fix args in SGLang launch command (#9) f1ed9ca
2025-11-07 Update README.md 1b9dbb7
2025-11-07 fix vocab size (#4) 6e3cdad
2025-11-06 Update LICENSE 357b94a
2025-11-06 Update README.md 603577f
2025-11-06 Update README.md 7d2962b
2025-11-06 Update README.md f5ed4a8
2025-11-06 Update README.md d02605f
2025-11-06 Update README.md 5b64d2a
2025-11-06 update c9caddd
2025-11-06 update f1cdb45
2025-11-06 update deploy guidance 6dead1a
2025-11-06 fix tokenizer a060a3e
2025-11-06 Update README.md 22bbb72
2025-11-06 Update README.md 7b23274
2025-11-04 initial commit 652f5f9

All providers

3 providers · 3 endpoints

Where to run 3 providers · 3 endpoints

Hosted endpoints observed for Kimi-K2-Thinking, 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: $0.60/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Google
openrouter
moonshotai/kimi-k2-thinking 256K unknown $0.60 $2.50 — — yes status -5 2026-10-24
Novita
openrouter
moonshotai/kimi-k2-thinking 256K bf16 $0.60 $2.50 — — yes live 2026-10-24
Featherless
hf
moonshotai/Kimi-K2-Thinking — — — — — — — live 2026-10-25

Articles and coverage

23 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.

Official2

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

Analysis7

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 pageHN 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
artificialanalysis.aiauthority967 words
arxiv.orgauthorityabout the Kimi K2 family
codejam.info2026-04-18about the Kimi K2 familyHN 6 pts · 2 comments1,457 words
GPT-5.4 achieved an average cleanliness score of 9 while GLM-5.1 scored 8 in the technical approach evaluation. Kimi K2.5 and MiniMax M2.7 were consistently ranked at the bottom with average scores of 5.6 and 5.2 respectively.generated, cited

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twitter.com2025-07-11HN front pageabout the Kimi K2 familyHN 348 pts · 179 comments
All coverage tracked for this model (23) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Kimi K2 Thinking - Intelligence, Performance & Price Analysisartificialanalysis.ai—2026-10-07exa
[PDF] Kimi K2: Open Agentic Intelligence - arXivarxiv.org—2026-10-07exa
Introducing Kimi K2 Thinkingkimi.ai official—2026-10-07exa
Kimi K2 Thinkingmoonshotai.github.io—2026-10-07exa
moonshotai / kimi-k2-thinkingdocs.api.nvidia.com—2026-10-07exa
Kimi K2 Thinking Explained: Architecture, Benchmarks & API ...qubrid.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 K2 Thinking: what 200+ tool calls mean for productionlambda.ai2026-02-042026-10-07exa
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: How Moonshot's $4.6M Open-Source Model Challenges GPT-5 With Trillion-Parameter MoE - PulseMarkpulsemark.ai2025-11-172026-10-07exa
Kimi K2 Thinking: Open-Source LLM Guide, Benchmarks, and Tools | DataCampdatacamp.com2025-11-102026-10-07exa
5 Thoughts on Kimi K2 Thinkinginterconnects.ai2025-11-092026-10-07hn
China's AI Upstart Moonshot Stuns Valley Again with a $4.6M Wonderentropytown.com2025-11-092026-10-07hn
Kimi K2 Thinking: How to Run Locallydocs.unsloth.ai2025-11-082026-10-07hn
Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning modelmoonshotai.github.io2025-11-062026-10-07hn
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
Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language modeltwitter.com2025-07-112026-10-07hn

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Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:35 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:02 UTC · Last verified 2026-10-25

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