Kimi K3: Open Frontier Intelligence
Kimi K3 is a 2.8T-parameter open model featuring native vision and a 1-million-token context window.
Up to date
moonshotai Kimi K3 (Modified MIT) (kimi-k3) out 4 months ago
2.78T parameters, images and text in, text out, 1M tokens of context. 22 providers serve it from $0.24 per million input tokens.
Verified 2026-10-25 Rank #7 in LLMs · score 131.7 method v1 Sources 5 Downloads 30d 909.8k · #119 of 619 tracked
Changed this week: Cheapest input price fell 57.8%: $0.536 → $0.226 per 1M (OpenRouter).2026-10-24 · Cheapest input price fell 55.6%: $0.536 → $0.238 per 1M (OpenRouter).2026-10-24 · 2 new quantized builds on the Hub (58 in all).2026-10-24 · 1 more
No drawable SVG in the reply.
Why did the open-weights model cross the road?
To get to the open source.
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~~~~~~~~~~~~~~~~~~~~~~~~The ringer: the same four playful tests for every model, replies shown as received and checked by code. A joke, not a benchmark; never ranked.3 of 4 pass · parasail-kimi-k3 on parasail · run 2026-10-09 · temperature 0 · cost $0.121 · 2026-10 thing: supermarket · all models →
Picked by rule from 35 tracked pieces: the lab's own post, the most shared, a field report, the latest.
Kimi K3 is a 2.8T-parameter open model featuring native vision and a 1-million-token context window.
Moonshot AI announced Kimi K3, a 2.8 trillion parameter model available via API with open weights promised by July 27, 2026.
A fork of Deltafin runs the full 2.8-trillion-parameter Kimi K3 model on a single M5 Max MacBook Pro with 128 GB RAM, streaming 1.45 TB of expert weights from four SSDs.
The 4 picks above, plus these 21. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.
Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.
20 providers with a live price, from $0.24 to $3.45 per million input tokens; the median is $2.42. One dot per provider, cheapest in teal.
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.
Widely served (22 API providers); heavily run locally (58 quantized builds); a big base for builders (54 fine-tunes, adapters and merges).
Top Spaces running it: embedl/hfviewer ♥ 181 · akhaliq/Kimi-K3 ♥ 19 · discord-community/HuggingMod ♥ 17 · HapppyHooochie/Kimi-K3-Abliterated-Demo ♥ 10
Weights only, an estimate: leave room for the KV cache. Method
About 1.67 TB at INT4, 3.34 TB at FP8, 6.67 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.
Estimate params × bytes per param × 1.2 · how we estimate
The probe replies as received on 2026-10-07 from parasail, graded by code. Receipts, not a score.
def is_balanced(s: str) -> bool:
pairs = {')': '(', ']': '[', '}': '{'}
stack = []
for ch in s:
if ch in '([{':
stack.append(ch)
elif ch in ')]}':
if not stack or stack.pop() != pairs[ch]:
return False
return not stack {"city": "Lisbon", "unit": "c"} {"name":"Foo-9B-Instruct","license":"Apache-2.0","parameters_b":9.2,"context_tokens":131072,"languages":["English","French","German","Japanese"]} {
"providers": [
{
"provider": "parasail",
"endpoint_model": "parasail-kimi-k3",
"source_url": "https://api.parasail.io/v1/chat/completions",
"ttft_ms_median": 13942,
"ttft_any_ms_median": 589,
"tokens_per_s_median": 88.3,
"runs": [
{
"ttft_s": 13.942,
"ttft_any_s": 0.994,
"tokens_per_s": 103.8,
"latency_s": 14.44,
"tokens_out": 1500,
"reasoning_chars": 5407,
"finish": "length"
},
{
"ttft_s": 9.107,
"ttft_any_s": 0.589,
"tokens_per_s": 88.3,
"latency_s": 10.09,
"tokens_out": 891,
"reasoning_chars": 3398,
"finish": "stop"
},
{
"ttft_s": 18.848,
"ttft_any_s": 0.557,
"tokens_per_s": 70.8,
"latency_s": 20.28,
"tokens_out": 1435,
"reasoning_chars": 4930,
"finish": "stop"
}
]
}
],
"note": "Single client, wall clock; tok/s counts every output token including hidden reasoning."
} {
"calls": [
{
"name": "get_weather",
"args": {
"city": "Lisbon",
"date": "2026-10-14"
}
},
{
"name": "check_calendar",
"args": {
"date": "2026-10-14"
}
},
{
"name": "book_table",
"args": {
"date": "2026-10-14",
"party_size": 2,
"restaurant": "Cervejaria Ramiro",
"time": "19:30"
}
}
],
"checks": {
"get_weather": true,
"check_calendar": true,
"book_table": true
},
"final": "All done! Here's a summary:\n\n- **Weather in Lisbon on 2026-10-14:** Clear, high of 22°C, only 5% chance of rain — no rain expected ☀️\n- **Free evening slot found:** 19:30–21:30\n- **Reservation:** Table for 2 at Cervejaria Ramiro, 19:30 on 2026-10-14\n\n**Confirmation code: TBL-4471**\n\nEnjoy your evening — the seafood there is fantastic!",
"fixture": "receipts.errand.v1"
} {
"lengths_k": [
8,
32,
64
],
"depths": [
0.1,
0.5,
0.9
],
"cells": [
[
1,
1,
1
],
[
1,
1,
1
],
[
1,
1,
1
]
],
"detail": [
{
"k": 8,
"depth": 0.1,
"recalled": true,
"reply": "amber-falcon-7731",
"latency_s": 1.91,
"finish": "stop"
},
{
"k": 8,
"depth": 0.5,
"recalled": true,
"reply": "cobalt-marten-7828",
"latency_s": 1.53,
"finish": "stop"
},
{
"k": 8,
"depth": 0.9,
"recalled": true,
"reply": "saffron-newt-7925",
"latency_s": 1.32,
"finish": "stop"
},
{
"k": 32,
"depth": 0.1,
"recalled": true,
"reply": "jade-lynx-8022",
"latency_s": 3.85,
"finish": "stop"
},
{
"k": 32,
"depth": 0.5,
"recalled": true,
"reply": "umber-kestrel-8119",
"latency_s": 3.7,
"finish": "stop"
},
{
"k": 32,
"depth": 0.9,
"recalled": true,
"reply": "coral-heron-8216",
"latency_s": 2.21,
"finish": "stop"
},
{
"k": 64,
"depth": 0.1,
"recalled": true,
"reply": "slate-badger-8313",
"latency_s": 5.44,
"finish": "stop"
},
{
"k": 64,
"depth": 0.5,
"recalled": true,
"reply": "ochre-otter-8410",
"latency_s": 4.15,
"finish": "stop"
},
{
"k": 64,
"depth": 0.9,
"recalled": true,
"reply": "teal-ibis-8507",
"latency_s": 3.67,
"finish": "stop"
}
]
} Weights, every dated article, and repo commits on one rail.
Teal is the weights (2026-06-13), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.
112 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.
From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.
Everything else, folded: the full tables, every source.
| Benchmark | Value | Setting / notes | Reported by | Date |
|---|---|---|---|---|
| author-reported AA-LCR · Read long documents | 74.7 | — | model author | 2026-10-07 |
| author-reported CritPt | 23.4 | — | model author | 2026-10-07 |
| author-reported DeepSWE · Write code with agents | 67.5 | — | model author | 2026-10-07 |
| author-reported GPQA Diamond · Reason through hard problems | 93.5 | — | model author | 2026-10-07 |
| author-reported HLE-Full | 43.5 / 56.0 | — | model author | 2026-10-07 |
| author-reported ProgramBench | 77.8 | — | model author | 2026-10-07 |
| author-reported apex-agents (mercor/apex-agents) | 41 | — | Model Card | 2026-10-24 |
| author-reported deep_swe (datacurve/deep-swe) | 67.5 | — | Model Card | 2026-10-24 |
| author-reported diamond (Idavidrein/gpqa) | 93.5 | — | Model Card | 2026-10-24 |
| author-reported hle (cais/hle) | 56 | — | Model Card | 2026-10-24 |
| author-reported toolathlon_verified (hkust-nlp/Toolathlon) | 76.5 | — | moonshotai/Kimi-K3 model card | 2026-08-20 |
The architecture utilizes a Mixture-of-Experts framework and supports a 1-million-token context window.[1]
Generated 2026-10-07 14:59 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.
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.
Listed in LLMs
| Pipeline tag | image-text-to-text |
|---|---|
| Library | transformers |
| Architectures | KimiK3ForConditionalGeneration |
| Model type | kimi_k3 |
| Dtype | bfloat16 |
| Params by dtype | BF16: 57,179,884,544 · F32: 11,122,432 · U8: 2,722,740,830,208 |
| Size band | XXL |
| MoE | yes |
| Experts | 896 |
| Context length | 1,048,576 (config.json max_position_embeddings) |
| License | Kimi K3 (Modified MIT) (kimi-k3) (other) |
| Hub snapshot | 2026-10-25 |
vLLM: supported · KimiK3ForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · KimiK3ForConditionalGeneration · 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.
Weights-only memory for Kimi-K3: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.
| BF16 (2 bytes/param) | 6.67 TB · Cluster (≥640 GB) |
|---|---|
| FP8 (1 byte/param) | 3.34 TB · Cluster (≥640 GB) |
| INT4 (0.5 bytes/param) | 1.67 TB · Cluster (≥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.
A fixed probe set run against a live API endpoint: the same questions for every model, answers graded by code, nothing judged by a model. Latencies are single-request wall clock from one client location, not a benchmark.
| Probe | Result | Latency | Reply (truncated) |
|---|---|---|---|
| Self-description | info | 2.6 s | I'm Kimi, developed by Moonshot AI (月之暗面). I don't know my training data cutoff. |
| Tool call | pass | 1.8 s | {"city": "Lisbon", "unit": "c"} |
| Strict JSON | pass | 2.5 s | {"name":"Foo-9B-Instruct","license":"Apache-2.0","parameters_b":9.2,"context_tokens":131072,"languages":["English","French","German","Japanese"]} |
| Code (hidden tests) | pass | 4.1 s | def is_balanced(s: str) -> bool: pairs = {')': '(', ']': '[', '}': '{'} stack = [] for ch in s: if ch in '([{': stack.append(ch) elif ch in ')]}': if not stack or stack.pop() != pairs[ch]: return False return not stack |
| Multi-step arithmetic | pass | 1.9 s | 16 |
| Needle recall at 64k tokens | pass | 6.7 s | amber-falcon-7731 |
Probe set firstlook.v1 · observed 2026-10-07 20:12 UTC · endpoint · provider docs · cost of this run $0.23
quantized: 58finetune: 52adapter: 2
| Variant | Relation | Quant | Params | Band | License | Author | Likes | Downloads |
|---|---|---|---|---|---|---|---|---|
| unsloth/Kimi-K3-GGUF | quantized | GGUF | — | — | other | unsloth | 402 | 1.3M |
| inference-optimization/Kimi-K3-0.40B | finetune | — | 396M | S | mit | inference-optimization | 83 | 88.5k |
| Inferact/Kimi-K3-DSpark | finetune | — | 3.56B | M | other | Inferact | 55 | 75.8k |
| nvidia/Kimi-K3-NVFP4 | quantized | NVFP4 | 1.42T | XXL | other | nvidia | 42 | 46.1k |
| pipenetwork/Kimi-K3-REAP80-MLX-mxfp4-q8 | quantized | MLX | 601B | XXL | other | pipenetwork | 16 | 31.5k |
| AtomicChat/Kimi-K3-GGUF | quantized | GGUF | — | — | other | AtomicChat | 17 | 19.9k |
| RedHatAI/Kimi-K3-NVFP4 | quantized | NVFP4 | 1.59T | XXL | other | RedHatAI | 13 | 20k |
| pipenetwork/Kimi-K3-REAP73-MLX-mxfp4-q8 | quantized | MLX | 792B | XXL | other | pipenetwork | 9 | 19k |
| modal-labs/Kimi-K3-DFlash | finetune | — | 2.60B | M | other | modal-labs | 4 | 19.1k |
| mgoin/Kimi-K3-pruned75 | finetune | — | 737B | XXL | other | mgoin | 1 | 17.1k |
| unsloth/Kimi-K3 | quantized | — | 2.78T | XXL | other | unsloth | 257 | 2.9k |
| Ryanchen911/Kimi-K3-Uncensored-GGUF | quantized | GGUF | — | — | other | Ryanchen911 | 55 | 11k |
| Uniboshi/Kimi-K3-Abliterated-V1 | finetune | — | 2.78T | XXL | other | Uniboshi | 146 | 5.2k |
| pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8 | quantized | MLX | 792B | XXL | other | pipenetwork | 8 | 11.3k |
| kernelpool/Kimi-K3-2bit-UVMAX | quantized | — | 2.78T | XXL | other | kernelpool | 5 | 9.8k |
| avlp12/Kimi-K3-Alis-MLX-Dynamic-2.10bpw | quantized | MLX | 449B | XXL | other | avlp12 | 4 | 8.7k |
| GrEarl/Kimi-K3-GGUF | quantized | GGUF | — | — | other | GrEarl | 30 | 5.6k |
| pipenetwork/Kimi-K3-REAPgraded-MLX-mxfp4-q8 | quantized | MLX | 1.05T | XXL | other | pipenetwork | 1 | 6.8k |
| mlx-community/Kimi-K3-mlx-reap160-2bit | finetune | mlx | 543B | XXL | other | mlx-community | 2 | 6.3k |
| Kuberwastaken/Kimi-K3-GGUF | quantized | GGUF | — | — | other | Kuberwastaken | 14 | 5.6k |
| bullerwins/Kimi-K3-GGUF | quantized | GGUF | — | — | — | bullerwins | 0 | 5.7k |
| RedHatAI/Kimi-K3-speculator.dspark | finetune | — | 4.74B | M | apache-2.0 | RedHatAI | 8 | 5k |
| GrEarl/Kimi-K3-GGUF-IQ1_S | quantized | GGUF | — | — | other | GrEarl | 22 | 3.8k |
| RedHatAI/Kimi-K3-FP8-BLOCK | quantized | -FP8 | 2.78T | XXL | other | RedHatAI | 12 | 3.8k |
| catalystsec/Kimi-K3-Audit-451E-MLX-MXFP4 | quantized | MLX | 1.43T | XXL | other | catalystsec | 1 | 3.6k |
| catalystsec/Kimi-K3-Audit-240E-MLX-MXFP4 | quantized | MLX | 786B | XXL | other | catalystsec | 0 | 3.6k |
| qtum/Kimi-K3-GGUF | quantized | GGUF | — | — | other | qtum | 0 | 3.4k |
| nota-ai/Kimi-K3-Nota-Global-Pruned-50 | finetune | — | 1.42T | XXL | other | nota-ai | 15 | 2.4k |
| 6block/Kimi-K3-GGUF | quantized | GGUF | — | — | other | 6block | 1 | 2.9k |
| vcruz305/Kimi-K3-GGUF | quantized | GGUF | — | — | other | vcruz305 | 6 | 2.4k |
| prometheusAIR/Kimi-K3-REAP55-GGUF | quantized | GGUF | — | — | other | prometheusAIR | 15 | 1.4k |
| lightseekorg/kimi-k3-dspark | finetune | — | 3.56B | M | other | lightseekorg | 2 | 1.8k |
| abacusai/Smaug-Agentic | finetune | — | 2.78T | XXL | other | abacusai | 25 | 669 |
| vessl/Kimi-K3-W4AFP8 | quantized | — | 1.42T | XXL | other | vessl | 12 | 1.3k |
| riverclouds/Kimi-K3-8L-dummy | finetune | — | — | — | other | riverclouds | 0 | 1.8k |
| Inferact/Kimi-K3-DSpark-Block5 | finetune | — | 2.85B | M | — | Inferact | 2 | 1.6k |
| lightseekorg/kimi-k3-eagle3-mla | finetune | — | 3.02B | M | other | lightseekorg | 1 | 1.6k |
| DevQuasar/moonshotai.Kimi-K3-GGUF | quantized | GGUF | — | — | — | DevQuasar | 6 | 1.4k |
| PatronusAI/kimi-k3-nvfp4 | quantized | nvfp4 | 1.42T | XXL | — | PatronusAI | 2 | 1.2k |
| SHSLab/Kimi-K3-Abliterated | finetune | — | 2.78T | XXL | other | SHSLab | 12 | 705 |
| Date | Commit | SHA |
|---|---|---|
| 2026-09-02 | Add Toolathlon-Verified evaluation result (#183) | f831ab6 |
| 2026-08-20 | Update encoding_k3.py (#173) | a590ce0 |
| 2026-07-27 | Add community evaluation results for GPQA, HLE, DEEP-SWE, APEX-AGENTS (#37) | 9f62e4e |
| 2026-07-27 | Update README.md (#33) | 301be1b |
| 2026-07-27 | [HF Staff] this tag is required to enable inference providers, please merge! (#30) | a7b6c02 |
| 2026-07-27 | Update README.md | 2496450 |
| 2026-07-27 | Update README.md | dc514c7 |
| 2026-07-27 | Initial commit | c5d1dd4 |
Hosted endpoints observed for Kimi-K3, 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: $0.23/M.
| Provider | Model ref | Context | Quant | $/M in | $/M out | tok/s | TTFT | Tools | Status | Observed |
|---|---|---|---|---|---|---|---|---|---|---|
| Relace openrouter | moonshotai/kimi-k3 | 1M | fp4 | $0.23 | $7.70 | — | — | yes | status -2 | 2026-10-24 |
| Wafer openrouter | moonshotai/kimi-k3 | 1M | mxfp4 | $0.24 | $16.00 | — | — | yes | live | 2026-10-24 |
| InferenceNet openrouter | moonshotai/kimi-k3 | 1M | fp4 | $0.24 | $10.80 | — | — | yes | live | 2026-10-24 |
| Makora openrouter | moonshotai/kimi-k3 | 1M | fp4 | $0.65 | $15.00 | — | — | yes | live | 2026-10-24 |
| Sail Research openrouter | moonshotai/kimi-k3 | 1M | fp4 | $1.20 | $5.50 | — | — | yes | live | 2026-10-24 |
| AkashML openrouter | moonshotai/kimi-k3 | 1M | fp4 | $1.20 | $14.00 | — | — | yes | live | 2026-10-24 |
| Morph openrouter | moonshotai/kimi-k3 | 1M | fp8 | $1.42 | $14.90 | — | — | yes | live | 2026-10-24 |
| Decart openrouter | moonshotai/kimi-k3 | 1M | mxfp4 | $1.65 | $8.25 | — | — | yes | live | 2026-10-24 |
| Chutes openrouter | moonshotai/kimi-k3 | 1M | mxfp4 | $1.85 | $9.50 | — | — | — | live | 2026-10-24 |
| Fireworks AI hf, openrouter | accounts/fireworks/models/kimi-k3 | 1M | unknown | $2.10 | $10.50 | 30 | 1066 ms | yes | live | 2026-10-25 |
| DeepInfra hf, deepinfra, openrouter | moonshotai/Kimi-K3 | 1M | mxfp4 | $2.28 | $11.40 | 62 | 451 ms | yes | live | 2026-10-25 |
| Phala openrouter | moonshotai/kimi-k3 | 1M | unknown | $2.55 | $12.75 | — | — | yes | live | 2026-10-24 |
| DigitalOcean openrouter | moonshotai/kimi-k3 | 1M | unknown | $2.55 | $12.95 | — | — | yes | live | 2026-10-24 |
| Together AI hf, openrouter | moonshotai/Kimi-K3 | 1M | unknown | $3.00 | $15.00 | 36.4 | 495 ms | yes | live | 2026-10-25 |
| Baseten hf, openrouter | moonshotai/Kimi-K3 | 1M | fp8 | $3.00 | $15.00 | 43 | 1824 ms | yes | live | 2026-10-25 |
| Parasail parasail, openrouter | parasail-kimi-k3 | 1M | fp4 | $3.00 | $15.00 | — | — | yes | serverless | 2026-10-24 |
| ParetoAlpha openrouter | moonshotai/kimi-k3 | 1M | unknown | $3.00 | $15.00 | — | — | yes | live | 2026-10-24 |
| Amazon Bedrock openrouter | moonshotai/kimi-k3 | 1M | unknown | $3.00 | $15.00 | — | — | yes | live | 2026-10-24 |
| Modal openrouter | moonshotai/kimi-k3 | 1M | mxfp4 | $3.00 | $15.00 | — | — | yes | live | 2026-10-24 |
| Moonshot AI openrouter | moonshotai/kimi-k3 | 1M | mxfp4 | $3.00 | $15.00 | — | — | yes | live | 2026-10-24 |
| Alibaba openrouter | moonshotai/kimi-k3 | 1M | unknown | $3.45 | $17.25 | — | — | yes | live | 2026-10-24 |
| Featherless hf | moonshotai/Kimi-K3 | — | — | — | — | — | — | — | live | 2026-10-25 |
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.
Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:31 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 14:59 UTC · Last verified 2026-10-25
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