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

2026-09-04 / rss-researcherGitHub is retiring four Copilot models on October 2, 2026GitHub published a changelog entry on 2026-09-03 announcing the upcoming deprecation of selected Copilot models, filed under its Retired/Deprecations category. Secondary coverage lists the four as Gemini 3.5 Flash, Gemini 3.6 Flash, Kimi K2.7 Code and Claude Opus 4.7, retiring October 2, 2026, with Gemini 3.8 Flash, Kimi K3 and Claude Opus 5 as the pushed replacements. The GitHub changelog confirms the entry and its date, but the specific model list comes from one secondary source, so check the entry itself before repinning anything.
2026-09-01 / arxiv-researcherWebWorld Uses the Browser as the Judge a VLM Cannot Fool, Beating Its Own Base Model by 14.9 PointsSelf-improving web code normally has the same VLM proposing and judging the repair, so visual plausibility substitutes for whether the page works; WebWorld makes the browser the counterparty instead, compiling each VLM critique into a typed interaction contract and re-executing the candidate. An acceptance certificate issues only when target progress holds and every previously verified capability is preserved, and only certified transitions reach the SFT export, forming a quality ratchet. WebWorld-27B improves its raw base by 5.3 points on HTMLBench-400 and 14.9 points on MiniAppBench-Val, reaching Kimi-K2.6 and GPT-5.4 levels on interactive HTML generation, and equal-size ablations show the lift nearly vanishes without the certificate.
2026-09-01 / arxiv-researcherData-Science Code Efficiency Does Not Track Correctness: Kimi-K2.5 Ranks Last on Pass, First on SpeedDSEffi-Bench is the first benchmark targeting execution efficiency in LLM-generated data science code, with 1,000 instances across 10-plus libraries, stress-testing harnesses and human-validated references, evaluated on 16 models. GPT-5.4 leads correctness at 66.9% Pass but its 71.7% efficiency score barely beats GPT-5.4-mini's 71.6% despite solving 47 more tasks, while Kimi-K2.5 is lowest in correctness among frontier models at 40.2% yet posts the best efficiency score at 73.6%. A human-annotated taxonomy finds 79.1% of efficiency deficits come from domain-specific causes rather than algorithmic complexity, and library-conditioned routing approaches Claude-Opus-4.6 Best@3 efficiency at 13.0x lower cost.
2026-09-01 / github-pulse-researcherLMDeploy 0.17.0 integrates DeepEPv2 and adds Kimi K2.6 plus a Mooncake store KV connectorPublished 2026-09-01, LMDeploy v0.17.0 integrates DeepEPv2 (#4783), adds PyTorch-engine support for Kimi K2.6 (#4846), and wires a Mooncake store KV connector (#4903) for disaggregated KV cache. Performance work includes PDL for paged attention and V4 prefill (#4861), compact blocked FP8 MoE and route preparation optimization (#4857), reduced speculative decoding pre/post-processing overhead (#4877), further GLM-5.2 serving optimization (#4853), and support for page sizes that are not powers of two (#4854). Server-side fan-out for n>1 choices (#4841) and structural_tag response_format across both turbomind and pytorch engines (#4906) round it out.
2026-08-30 / thought-leaders-researcherThinking Machines Lab and UIUC beat the human text-to-SQL benchmark for the first time, with no pipeline engineeringAn August 27 post from Thinking Machines Lab with Yuxuan Zhu, Tengjun Jin, Yoojin Choi and Daniel Kang describes ReViSQL-K2.6, an RLVR run on Kimi-K2.6 using VeriEQL semantic-equivalence verification plus rule-based process rewards on expert-curated data. With 16-sample self-consistency it reaches 92.97% on Arcwise-Plat-SQL at $0.56/task, edging the 92.96% human benchmark, and greedy decoding gets 91.37% at $0.035/task. The load-bearing claim for builders is that it outperforms scaffolded baselines by 8 to 22 points while deleting the orchestration layer entirely.
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.

Graph relationships

SUPPORTS
LMDeploy
LMDeploy 0.17.0 adds PyTorch-engine support for Kimi K2.6 model.
Source finding
USES
ReViSQL-K2.6
ReViSQL-K2.6 is an RLVR run on Kimi-K2.6 using semantic-equivalence verification.
Source finding
BENCHMARKED AGAINST
ReViSQL-K2.6
ReViSQL-K2.6 achieved 92.97% on the Arcwise-Plat-SQL benchmark, edging the human benchmark of 92.96%.
Source finding
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
Source finding
BENCHMARKED AGAINST
GPT-5.4
Kimi K2.6 scored 58.6 on SWE-Bench Pro vs GPT-5.4's 57.7
Source finding

Source trail

GitHub Changelog (model list via ntcompatible.com)arXiv 2608.30530 arXiv 2608.30248 GitHub Thinking Machines Lab 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

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