LOADING DATASHEET
LOADING DATASHEET
by Moonshot
RANKED #23 OF 270 ASSISTANTS · OVERALL #37 OF 6,566 · VIBE SCORE 6.9 · 2,079 VOICES
A fast text and coding assistant built for everyday technical help.
512 mentions
155 mentions
26 weeks · 3,354 voices
QUIETLY DEGRADING
Recent vibe and chatter are both sliding.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download Kimi K3 once and it's yours. No subscription, no rate limits, works offline.
2.8T parameters (104B active), native 4-bit (MXFP4) is 1.56 TB, 1-bit GGUF is ~594 GB
Apple MacBook Pro M1 Max (64 GB Unified Memory, 2 TB SSD)
64 GB UNIFIED MEMORY
~$1,300 USEDToo big for consumer RAM; runs at ~0.6 tok/s via WASTE SSD-streaming (3-bit quant)
Used Dual-Socket AMD EPYC Server (EPYC 7002/7003) with 1TB D
1 TB DDR4 RAM
~$7,300 USEDLoads 1-bit GGUF (~594 GB) entirely in RAM via llama.cpp, ~1-2 tok/s
NVIDIA DGX H100 / B200 Cluster (8x H100 80GB GPUs)
640 GB VRAM + 2TB SYSTEM RAM
~$300,000+ NEWFull native MXFP4 precision, 1M context, high throughput (~20+ tok/s)
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Kimi K3 takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMApple MacBook Pro M1 Max (64 GB Unified Memory, 2 TB SSD) | SWEET SPOTUsed Dual-Socket AMD EPYC Server (EPYC 7002/7003) with 1TB D | FULL POWERNVIDIA DGX H100 / B200 Cluster (8x H100 80GB GPUs) |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~14 MIN★★★★★ | ~5.6 MIN★★★★★ | —★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~42 MIN★★★★★ | ~17 MIN★★★★★ | —★★★★★ |
| Build a websitea small landing page, markup and styles together | ~3.7 HR★★★★★ | ~89 MIN★★★★★ | —★★★★★ |
| Build a backendan API with routes, storage and tests | ~12 HR★★★★★ | ~4.6 HR★★★★★ | —★★★★★ |
| Build a gamea playable browser game in one file | ~6.9 HR★★★★★ | ~2.8 HR★★★★★ | —★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. A DASH MEANS NO SPEED WAS RESEARCHED FOR THAT RIG. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
Users love Kimi K3 for its dependable coding help and fast answers, even tackling security fixes other models refuse. However, some complain about recent price hikes, preachy refusals, and occasional hallucinations.
1528 POSTS · 551 COMMENTS · BLUESKY · REDDIT · HACKER NEWS · X · DEV.TO · GITHUB · LEMMY · BLOG · DEV FORUMS
44 thumbs up · 7 thumbs down
28 thumbs up · 21 thumbs down
8 thumbs up · 5 thumbs down
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3 thumbs up · 6 thumbs down
7 thumbs up · 1 thumbs down
1 thumbs up · 7 thumbs down
3 thumbs up · 0 thumbs down
“Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of “cyber guardrails”.”
“Kimi K3 is now more expensive.”
Checked picks first: tags say why you would switch, stars say how fully each one stands in for Kimi K3.
Kimi K3 weights now released.
Kimi K3: Open Frontier Intelligence
Kimi-K3 arrived: The era of the Chinese labs being far behind is over
Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of “cyber guardrails”. Hugging Face: We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing
Kimi K3 full model running on 16x GB10 cluster at 20+tps
David Sacks says U.S. AI guardrails are making American models less competitive after China’s Kimi K3 fixed 15 security bugs that Codex and Fable refused
Kimi-K3 on HuggingFace
2 things are happening at once: - AI coding models are converging. For 95% of tasks, most people can’t tell whether GPT-
GLM-5.3 achieves 60 on the Artificial Analysis Intelligence Index, on par with Kimi K3 and up 7 points from GLM-5.2. Onc
Kimi K3 tops Frontend Code Arena
Kimi K3 is top of nextjs eval
Setting up of a 16xGB10 (DGX Spark) cluster