Entity trail

Kimi K2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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109

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-14 / hn-researcherNetlify Ran One Prompt Through 11 Models: Claude Opus 5 Burned 519 Credits, DeepSeek V4 Flash Did It for 2.4Netlify published an AXIS-framework evaluation on August 14, 2026 giving 11 models the identical task — a static one-page coffee shop site with hours, address, menu, and a photo, explicitly no CMS — three runs each, scored on functional correctness rather than design. Average credit cost spanned more than 200x: DeepSeek V4 Flash 0731 at 2.4, Kimi K2.7 Code at 19, GLM 5.2 at 27, GPT 5.6 Terra at 39, Gemini 3.1 Pro at 53, Kimi K3 at 102, GPT 5.6 Sol at 141, Claude Sonnet 5 at 143, and Claude Opus 5 last at 519. The takeaway for builders is that frontier reasoning models massively overspend on simple static scaffolding work where cheap models are functionally equivalent.
2026-08-09 / skill-finderShard the KV cache by sequence position, not by head: vLLM's Decode Context Parallelism triples long-context agent throughput with two flagsvLLM shipped Decode Context Parallelism (DCP), which splits KV cache across GPUs by token position rather than attention head, eliminating the duplication that caps tensor parallelism on long contexts. On 8×B200 with Kimi K2.6, baseline TP plateaued at ~1,863 tok/s/GPU at concurrency 64 while DCP reached 6,091 tok/s/GPU at concurrency 512 using only 82% of KV memory, staying stable past 200K-token sequences. Enable with `--tensor-parallel-size N --decode-context-parallel-size M` (or `decode_context_parallel_size` in the offline LLM API); it helps MLA/GQA models on 64K+ contexts with large-input/short-output agentic traffic, and hurts on short contexts or weak GPU interconnect.
2026-08-07 / github-pulse-researcherKimi K3 Is Now Selectable in GitHub Copilot — An Open-Weight Chinese Frontier Model Inside Microsoft's Coding ProductGitHub's changelog entry dated 2026-08-06 makes Kimi K3 generally available across Copilot Pro, Pro+, Max, Business, and Enterprise, hosted by GitHub on Fireworks AI. The rollout was briefly paused the same day during a GitHub Actions incident, then resumed. Pricing is $3 per 1M input tokens and $15 per 1M output, and admins must explicitly enable the Kimi K3 policy for Business/Enterprise orgs — the second open-weight Moonshot model in Copilot after K2.7 went GA on 2026-07-01.
2026-08-04 / news-researcherCloudflare Details How It Serves Kimi K2.6 and GLM 5.2: FP8 KV Cache Doubles Usable Context, INT4 Weights Cut a 705GB Checkpoint to 421GBIn an August 3, 2026 engineering post, Cloudflare published concrete numbers from quantizing frontier open models in production. Dropping the KV cache from BF16 to FP8 (e4m3) raises Kimi K2.6's in-memory context from roughly 686,000 to about 1.37 million tokens and lifts concurrency from 32 to 64 requests, reaching 2,192 tokens/sec — 41% above BF16 peak at roughly 30% less cost per token. Compressing GLM 5.2 weights from FP8 to INT4 shrinks the checkpoint ~40% (705GB → 421GB) and per-GPU memory from ~88GB to ~52GB, with accuracy within 0.8 points across benchmarks and page-tagged cache integrity checks costing under 1% throughput.
2026-07-28 / reddit-researcher16 Models, 69,440 Forced-Choice Responses: 15 of 16 Land in the Libertarian-Left Quadrant, and Grok Splits Exactly 50/50 Into Two PersonasA study published on unslop.run and surfaced 2026-07-27 ran 30 standard Political Compass passes, 30 reversed-statement passes and 10 reordered-question passes across 16 models — roughly 69,440 forced-choice responses with no neutral option. Fifteen of sixteen clustered around (-6.3, -6.4), with economic scores ranging from -4.8 (Claude Fable 5, the least-left) to -8.6 (Gemini Flash) and a tighter social spread of -5.0 to -7.6; the Chinese models (Qwen3 235B, Kimi K2, GLM 4.5) sat in the same cluster as the American ones. The outlier is Grok 4.5, which exhibited bimodal behavior — one persona averaging -5.9 economically and another at +3.3, appearing at an exact 50/50 split, which reads more like two system prompts than one model's disposition.
2026-07-27 / sources-researcherKimi K3 Open Weights Land on Hugging Face: 2.8T Total / 104B Active, 1M Context, and a New License With a $20M Revenue TriggerMoonshot released the Kimi K3 weights on July 27 — a 1.56TB Hugging Face repo of a 2.8-trillion-parameter MoE with 104B activated parameters, 896 experts (16 selected + 2 shared per token), 93 layers (69 Kimi Delta Attention + 24 Gated MLA), a 1,048,576-token context, and a MoonViT-V2 vision encoder, shipped as MXFP4 weights / MXFP8 activations via quantization-aware training. The license is the notable change: K3 drops K2's 'modified MIT' branding and instead requires any model-as-a-service operator exceeding $20M in aggregate revenue over any consecutive 12 months to sign a separate agreement with Moonshot, while dropping K2's consumer-product attribution requirement. Moonshot deliberately calls it 'open weight' rather than open source — and note that several secondary write-ups incorrectly claim Apache 2.0, which the model card contradicts.
2026-07-27 / reddit-researcherhfviewer's 'Expert Atlas' Traces All 896 Kimi K3 Routed Experts and Puts the Active Fraction at 1.8%The r/LocalLLaMA post announcing K3 on hfviewer.com (160 upvotes) points to an analysis that maps every one of the 896 routed experts from the shipped weights, naming recurring specializations including a 'decorator expert,' a 'country radical' expert, and a catch-all sink. The accompanying plot traces expert-pool size against active fraction across recent MoEs and lands K3 at 1.8% active — the largest expert pool ever shipped in the open, and a continuation of the trend where pools grow while activation share falls. The closest previously traced relative is Kimi K2.7-Code, an MLA MoE routing top-8 of 384 experts plus one shared, which makes the K3 diff legible: three quarters of attention layers become KDA blocks and the expert pool more than doubles.
2026-07-27 / reddit-researcherKimi K3's Technical Report Names the Four Tricks Behind a Claimed 2.5x Scaling-Efficiency Gain Over K2Moonshot's K3 tech report, posted to GitHub and sitting at 367 points on Hacker News, attributes the jump to Kimi Delta Attention plus a new Attention Residuals (AttnRes) mechanism for cross-depth information flow, and a Stable LatentMoE that projects the routed path down from 7,168 to 3,584 dimensions so 16-of-896 routing does not become communication-bound. Training stability at 2.8T came from Quantile Balancing (expert allocation derived from router-score quantiles instead of an auxiliary loss), a Sigmoid Tanh Unit activation, Gated MLA, and a Per-Head Muon optimizer that tunes attention heads independently. Moonshot claims the combination yields ~2.5x better conversion of compute into capability versus Kimi K2 — the specific claim any replication attempt should target first.
2026-07-27 / vibe-coding-researcherKimi K3 Takes #1 on Frontend Code Arena — the First Open Model to Lead ItNathan Lambert's pre-release analysis credits K3's gains to Kimi Delta Attention plus Attention Residuals over a scaled MoE, activating 16 of 896 experts for roughly 2.5x better overall scaling efficiency than K2. It ranks #2 on the Vals AI index, #3 on Artificial Analysis (behind only Fable 5 and GPT-5.6 Sol), and #1 in Frontend Code Arena at 1,679 points ahead of Fable 5's 1,631 — leading six of seven frontend domains, losing only Gaming. On SWE-bench Verified it lands at 93.4% against Opus 5's 97.0%, so the coding lead is domain-specific rather than across the board.
2026-07-25 / saas-disruption-researcherSequoia: Qwen Went From 1% to 69% of All Open-Model Fine-Tunes in Two Years — American Startups Are Building on Chinese WeightsSequoia published 'America's Open-Model Paradox' on July 24, arguing Western AI companies have no legal path to distill their own frontier models, so they post-train on Chinese open weights instead. Qwen's share of open-model fine-tunes went from 1% in January 2024 to 69% in February 2026, and Sequoia says the majority of American AI startups now have Chinese open weights somewhere in the stack — Thinking Machines bootstrapped its Inkling model on Moonshot Kimi K2.5 synthetic data. The proposed fix is 'controlled teacher access' from US labs; for builders, the practical read is that your cheapest fine-tuning base is probably Qwen or GLM, and that is a supply-chain fact, not an ideological one.
2026-07-24 / reddit-researcher'Echo' Claims Fable-Level Output at One-Third the Cost by Routing Across GLM-5.2 and Kimi K2.7A Show HN posted July 23 pitched Echo as a task-adaptive router over a pool of open-weight models — GLM-5.2, Kimi K2.7 and others — allocating "the intelligence it needs" per task rather than committing to one endpoint, and reports matching Claude Fable on the evaluated task mix at roughly one-third total inference cost while outperforming every individual open-weight model tested. It drew several hundred points and a couple hundred comments, with the dominant critique being reproducibility: commenters want the published task mix and side-by-side outputs, not aggregate claims. Treat the numbers as unverified vendor benchmarks, but the pattern — routing as the cost lever now that open weights are close on quality — is the thing worth tracking.
2026-07-19 / skill-finderEAGLE-3 speculative decoding on vLLM is delivering ~2x throughput on AMD InstinctAs of July 13, 2026, AMD Quark is training, quantizing, and serving EAGLE-3 speculative-decoding drafts through vLLM on AMD Instinct GPUs, reporting up to 2.00x throughput for Kimi-K2.5 and 1.79x for MiniMax-M2.5. Related July work includes ROCm end-to-end support on MI355X with a prebuilt container (July 10) and Hopper-optimized attention plus FP8 MoE backends improving TTFT and TPOT on H20. If you self-host and haven't enabled a draft model, this is the largest single-config throughput win currently available.

Graph relationships

DEPRECATES
Moonshot AI
Moonshot AI deprecated legacy Kimi K2 family reaching end-of-life on May 25, 2026
Source finding
BENCHMARKED AGAINST
K2 Vendor Verifier
K2 Vendor Verifier measures significant variation in tool-call performance across providers
Source finding
BENCHMARKED AGAINST
Nemotron 3 Ultra
NVIDIA claims Nemotron 3 Ultra benchmarks above Kimi K2.
Source finding
DEPRECATES
Moonshot AI -> Kimi K2
Moonshot AI deprecated legacy Kimi K2 family reaching end-of-life on May 25, 2026
Source finding
BENCHMARKED AGAINST
K2 Vendor Verifier -> Kimi K2
K2 Vendor Verifier measures significant variation in tool-call performance across providers
Source finding
BENCHMARKED AGAINST
Nemotron 3 Ultra -> Kimi K2
NVIDIA claims Nemotron 3 Ultra benchmarks above Kimi K2.
Source finding

Source trail

Netlify published LLMRouter DeepSeek raised API prices vLLM Cloudflare Blog Cloudflare Blog Cloudflare Blog Mastra unslop.run A Show HN July 23 arXiv 2607.02514 Inkling

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