Entity trail

Kimi K2.6

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-08-22 / reddit-researcherMeasured Across 9 Models: Telling an LLM to Be Concise Saves ~1.5x, Shortening Your Prompt Costs Up to 96% MoreResearchers posting to r/MachineLearning tested output compression against input compression at five reduction levels on five short-answer datasets, across GPT-4o, GPT-5.4, Claude Haiku 4.5, Claude Sonnet 4.6, Qwen2.5-VL-7B, Qwen3.5-9B, DeepSeek-R1-Distill, Gemma-4-E4B and Kimi-K2.6. Instructing shorter output averaged 1.5x cheaper and up to 3x in the best case with accuracy roughly flat, and the effect held across an eleven-language run including Swahili, Bengali, Thai and Telugu. Trimming the input prompt did the reverse, costing up to 96% more on the worst benchmark because the model compensates with longer answers. The authors surfaced the paper specifically because Claude Code had shipped a concise output style the day before.
2026-08-22 / hn-researcherSemiAnalysis Measured the Open-Weights Catch-Up Time and Found It Halves Every Era, Now Down to Under Five MonthsSemiAnalysis published on August 21 that the open vs closed gap moves in cycles and closes roughly twice as fast each generation. Era 1 scaling: GPT-3.5 Turbo 75.7 vs Llama-2-70B 39.9, closed when DeepSeek V3 matched GPT-4o in December 2024. Era 2 reasoning: o1-preview led DeepSeek R1 by 12.1 points, closed by the R1-0528 checkpoint in an 8.5-month window. Era 3 agentic: Kimi K2.6 passed Opus 4.5 in 4.8 months and GLM-5.2 cleared GPT-5.2 in 6. The authors flag their own caveat that benchmarks miss productization.
2026-08-21 / hn-researcherClaude Plus a $27 PineTime Cleared a Years-Old Hardware Project in a Couple of Hours, With a 10-Minute Bluetooth Image TransferMike Kasberg's August 19 post, at 100 points and 54 comments, walks through building a Casio-style watch face for a $27 PineTime running InfiniTime, using Claude alongside Kimi K3/K2.6 and DeepSeek v4. The trick that made it work was rendering all static elements as one fullscreen background image and coding only the dynamic parts. The costs are real: the 240x240 image took about 10 minutes to transfer over Bluetooth and 1-2 seconds to refresh on swipe because the watch streams it from the filesystem rather than holding it in memory. Initial AI layouts overlapped text and needed manual fixes. His conclusion is that agents do well on embedded work when there is a simulator and good docs.
2026-08-17 / skill-finderContext bombs: plant a prompt injection in your AWS secrets and offensive AI agents refuse to continue (57% → 5% admin escalation)Tracebit found that placing short text designed to trip an attacking LLM's safety guardrails inside canary AWS Secrets Manager values halts autonomous attack agents mid-reconnaissance while alerting the defender that the canary was read. Across 152 runs against Opus 4.8, Gemini 3.1 Pro, GLM 5.2, DeepSeek 4 Pro and Kimi K2.6, admin access fell from 57% to 5% (93% → 0% for Opus 4.8), full compromise from 36% to 1%, and any-attack-path completion from 91% to 15%. The technique is free to deploy and doubles as a honeytoken, but Schneier's caveat is the real constraint: it only works against agents that have guardrails, so a locally-run unfiltered model walks straight through it.
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.
2026-07-27 / 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.
2026-07-16 / sources-researcherThinking Machines Lab Ships Inkling: 975B-A41B Multimodal, Apache 2.0 — the Leading American Open-Weights ModelTML's first full LLM is open-weights: 975B total / 41B active MoE, multimodal (text/image/audio in, text out), trained on 45 trillion tokens, with a 1M-token context in the open weights (256K via API/Tinker). A smaller Inkling-Small (276B-A12B) ships alongside. It ranks #41 on the Intelligence Index — top U.S. open-weights model but behind GLM-5.2 and Kimi K2.6 — and #9 in Agentic Web App Arena, with notably concise reasoning and strong tool calling. Architecture is unusual: non-RoPE relative positional encoding, 5:1 local-to-global sliding-window attention, short convolutions, and 2 shared expert sinks. Available on Hugging Face, Databricks, Baseten, Modal, and vLLM.
2026-07-14 / thought-leaders-researcherChinese Models Go Mainstream in US Enterprises: Goldman Names DeepSeek V4, Kimi K2.6, GLM-5, Qwen3.5 — Now 30–46% of API TrafficOn July 13, Goldman Sachs reportedly told Wall Street clients to adopt DeepSeek V4, Kimi K2.6, GLM-5, and Qwen3.5, citing 80–90% of frontier capability at roughly 70x lower cost than GPT-5.6. Corroborating data pegged Chinese models at 30–46% of US enterprise API traffic, up from about 4.5% a year ago. For builders, the cost/quality gap is now large enough that a major bank is formally recommending open Chinese models for regulated workflows.

Graph relationships

USES
Cloudflare
Cloudflare quantizes and serves Kimi K2.6 in production.
Source finding
BENCHMARKED AGAINST
Terminal-Bench 2.0
Kimi K2.6 sustained 4000+ tool calls across a 13-hour run on Terminal-Bench 2.0.
Source finding
BENCHMARKED AGAINST
GLM-5.1
Kimi K2.6 outperforms GLM-5.1 on agentic tasks despite similar SWE-bench Pro performance.
Source finding
RELEASED
Moonshot AI
Moonshot AI released Kimi K2.6 with improved reasoning and agent planning.
Source finding
BENCHMARKED AGAINST
GPT-5.4
Kimi K2.6 outperforms GPT-5.4 on SWE-Bench Pro by 0.9 points
Source finding
BENCHMARKED AGAINST
SWE-bench Pro
Kimi K2.6 scores 58.6 on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6
Source finding
RELEASED
Moonshot AI
Moonshot AI released Kimi K2.6, a 1 trillion parameter open-source model with agent swarm scaling
Source finding
BENCHMARKED AGAINST
Claude Opus 4.6
Kimi K2.6 scored 58.6 on SWE-Bench Pro vs Claude Opus 4.6's 53.4
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BENCHMARKED AGAINST
GPT-5.4
Kimi K2.6 scored 58.6 on SWE-Bench Pro vs GPT-5.4's 57.7
Source finding
RELEASED
Moonshot AI
Moonshot AI released Kimi K2.6 open-weight MoE model April 20, 2026
Source finding
BENCHMARKED AGAINST
Claude Opus 4.6
Kimi K2.6 outperforms Claude Opus 4.6 on major coding benchmarks
Source finding
BENCHMARKED AGAINST
GPT-5.4
Kimi K2.6 scores 58.6% on SWE-Bench Pro vs GPT-5.4's 57.7%
Source finding

Source trail

r/MachineLearning (49 upvotes)SemiAnalysis mikekasberg.com / Hacker News Tracebit — Context Bombs (via Schneier on Security, 2026-08-12)Netlify / Hacker News vLLM Blog — Efficient Decode Context Parallelism for Long Context Workloads GitHub Changelog Cloudflare Blog Moonshot AI (surfaced via Hacker News, 367 points / 164 comments)Interconnects Latent Space (AINews) / Thinking Machines Lab unrot (citing CNBC / Goldman Sachs)

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