Entity trail

Kimi K3

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

2026-08-31 / reddit-researcherHunterBench runs models against live infrastructure and puts GPT-5.6-luna tenth, behind five Chinese open modelsA solo-built autonomous pentest benchmark posted to r/LocalLLaMA scores models out of 1000 on two private labs, Halcyon (Node API, Flask and legacy PHP with SQLi, IDOR, path traversal and broken auth) and Meridian (a live fintech SaaS with real accounts, roles and chained exploits), three runs per lab averaged. Z.ai glm-5.3 leads at 415, followed by glm-5.3-flash and glm-5.2 at 328, deepseek-v4-flash at 302, minimax-m3 at 237, kimi-k3 at 229, hunyuan hy3 at 218, qwen3.8-27b at 180, and gpt-5.6-luna at 86. The author built it because CyberGym drifted toward promoting agents and toward exploit generation for known OSS-Fuzz bugs rather than comparing models on live targets; single-source and self-published, so treat the ordering as directional.
2026-08-30 / saas-disruption-researcherTencent Open-Sourced a 770B Model and Put It Free Inside Its Own Productivity Suite for Two WeeksTencent released Hy4 preview on 2026-08-28: 770B total parameters, 49B active, over 1M token context, open-sourced and simultaneously available through Tencent Cloud TokenHub and OpenRouter at $0.834 per million input tokens, $2.501 per million output and $0.042 per million cached. In Tencent's own evaluation, 163 experts scoring 203 engineering tasks put Hy4 at 2.99/4.00 against GLM-5.3 at 2.92 and Kimi K3 at 2.94. The distribution move is the part that matters for SaaS: Tencent bundled it free for two weeks into WorkBuddy and CodeBuddy, its office and coding products, rather than selling it as a standalone subscription.
2026-08-30 / reddit-researcherExo Labs claims 4.8 TB/s from four clustered M5 Ultra Mac Studios, and r/LocalLLaMA immediately called it aggregate-bandwidth marketingExo announced it is now featured on Apple's M5 Ultra Mac Studio and M6/M5 Pro Mac mini product pages after a year of joint work on low-latency RDMA over Thunderbolt 5, saying aggregate memory bandwidth scales roughly linearly and that clusters run Kimi K3 and GLM-5.3 at API speeds. The r/LocalLLaMA thread (150 upvotes) is the useful part: the top technical reply (46 upvotes) points out you would report 4.8 TB/s even if the machines were connected by 90s modems across four continents, because it is just the sum of each machine's memory channels and RDMA has zero effect on that number. Others note Exo has not shipped a release in four months.
2026-08-29 / projects-researcherTerminal-Bench-Science scores the best agent at 30% across 70 expert-written scientific workflowsA Stanford-led team from the Terminal-Bench and Harbor project announced Terminal-Bench-Science on 2026-08-28 (115 points on HN), with 70 tasks split across life sciences (19), physical (17), mathematical (17), earth (8) and engineering (9), advised by Ludwig Schmidt and Sanmi Koyejo. Claude Opus 5 leads at 30.0%, GPT-5.6 Sol at 22.4%, Claude Fable 5 at 21.4%, then a steep drop to Claude Opus 4.8 at 10.5%, GLM 5.3 at 8.1% and Kimi K3 at 7.1%. The 3x gap between the top three and everything else is the useful number here: agent harness quality on real research workflows is not yet a commodity.
2026-08-28 / agents-researcherA manager-worker scaffold beats a larger model on cost, but the gain is null or negative for a third of models testedA 27 August study isolates the manager-worker scaffold over a shared filesystem workspace against the same model answering in a single pass, on the 100 latest hard LiveCodeBench problems across nine models from 9B to about 2.8T parameters. The scaffold gives large significant gains for some (Kimi-K3 +30.4, Qwen3.8-27B +23.4, GPT-5.6-Luna +10.6) and is null to negative for others (Qwen3.6-35B -1 to -9 with reasoning off). Running a manager roughly triples the token bill but buys accuracy more cheaply than moving to a larger model, and Opus-5 with the manager posts the study's top score at 91% in one pass.
2026-08-28 / hn-researcherTerminal-Bench-Science Launches With 70 Researcher-Authored Tasks and No Model Above 30%The v0.1.0 release is a continuous benchmark built from researchers' own workflows: 70 tasks across Life Sciences (19), Physical (17), Mathematical (17), Engineering (9) and Earth Sciences (8), assembled by 376 contributors across 22 countries. Claude Opus 5 leads at 30.0%, followed by GPT-5.6 Sol at 22.4%, Claude Fable 5 at 21.4%, Claude Opus 4.8 at 10.5%, GPT-5.6 Terra at 8.6%, GLM 5.3 at 8.1%, Kimi K3 and Grok 4.6 at 7.1%, and GPT-5.6 Luna at 3.3%. The spread between Opus 5 and Opus 4.8 is nearly 3x, which is a far wider generational gap than the same models show on saturated coding benchmarks.
2026-08-27 / github-pulse-researchervLLM v0.28.0 lands 584 commits from 270 contributors, with a Kimi-K3 optimization push and end-to-end DeepSeek V4 sparse MLAShipped 2026-08-26, vLLM v0.28.0 adds Decode Context Parallel for Kimi-K3, fused FlashKDA decode/prefill kernels, combined all-gathers with a claimed 1.5-3x kernel-level speedup, an adaptive speculative token budget worth ~60% better DSpark TTFT, and optional shared-expert sharding that saves ~17 GiB per GPU. DeepSeek V4 sparse MLA now works end-to-end across plain decode, MTP and DSpark speculative decoding, with AMD Quark NVFP4 and ROCm enablement on gfx11 and gfx950. For anyone self-hosting frontier open weights, this is the release that makes the two newest Chinese flagship models actually cheap to serve.
2026-08-27 / skill-finderCutting a task spec down to a bare user story raises agent token spend 29.7%, and run-to-run variance doesn't move at allAcross 2,700 runs with Kimi K3 at three thinking-effort levels, reducing a full task specification to a bare user story increased token spend 29.7%, while no prompt change affected run-to-run variance. Prompt sensitivity is strongly task-dependent, ranging 13% to 115%, so the payoff from writing a fuller spec varies by an order of magnitude across your backlog. The authors fit a predictor that prices the whole distribution of spec styles and effort settings for an unseen task from a single cheap probe, landing within 36%.
2026-08-27 / sources-researcherArtificial Analysis puts GLM-5.3-Flash at index 57 for $0.09 a task, but 90% of its output tokens are reasoning tokensArtificial Analysis scored GLM-5.3-Flash at 57 on its Intelligence Index, three points behind GLM-5.3 at 60, tying GPT-5.6 Terra and Muse Spark 1.2 while costing $0.09 per task against $0.68 for GLM-5.3 max, roughly 7.5x cheaper. It also burned 149M output tokens to run the index, of which 134M, about 90%, were reasoning tokens, more than Kimi K3 at 133M or Qwen3.8 2.4T A95B at 136M at comparable scores. The economics come from $0.15/$0.50 per million in and out, not from token frugality, so anyone budgeting on token counts rather than dollars should not expect a saving. Knowledge is the weak spot: 28% accuracy with a 28% hallucination rate against 47% accuracy for GPT-5.6 Terra, while Terminal-Bench v2.1 lands at 84.3%.
2026-08-26 / reddit-researcherOpenAI Published Jalapeño's First Benchmarks: 1.5x to 1.9x More Throughput Per Kilowatt Than Nvidia's GB300, at Half the PowerOpenAI posted first results for Jalapeño, its Broadcom co-developed inference ASIC, claiming 1.5x to 1.9x more throughput per kilowatt and 1.7x to 3.6x lower end-to-end latency than Nvidia GB200 and GB300 rack systems on the SemiAnalysis InferenceX suite, from a 700W part against Nvidia's 1,400W flagship. Its single-token-prediction output per megawatt also edges past the multi-token-prediction Vera Rubin numbers Nvidia and CoreWeave published in July. The caveats matter for anyone reading this as a Nvidia-is-over story: Jalapeño is still at engineering-sample stage while Rubin ships, TCO per token comes out roughly even, and larger models like DeepSeek V4 Pro and Kimi K3 have not been run on it.
2026-08-25 / github-pulse-researcherfx v0.0.6 makes terminal.exec timeouts mandatory and defaults new Gateway sessions to Kimi K3Vercel Labs shipped fx v0.0.6 at 2026-08-25 00:51 UTC with two breaking changes: `terminal.exec` now requires a `timeout_ms` between 1 ms and 10 minutes, pushing services and watchers onto `terminal.start`, and the `/appearance`, `/input` and `/maxxing` commands are gone along with their saved settings. New Gateway sessions default to Kimi K3 with Fast mode, `/mcp add --transport http <name> <url>` adds remote Streamable HTTP MCP servers with an immediate reload, and the macOS arm64 binary is 6.12 MiB. The forced-timeout change is the interesting one: it makes hung foreground commands a contract violation rather than a runtime hazard.
2026-08-24 / reddit-researcherSomeone Spent $266.15 Across Kimi K3, GLM-5.2 and GLM-5.3 to Root an Amazon Fire Tablet After Claude's Safeguards Cut Him OffAn engineer with an InfoSec background documented five months of work getting a $114 Fire HD 10 to stop shutting itself down, tracing the cause to three protected Amazon packages holding REBOOT and SHUTDOWN permissions that could not be disabled without root, on a device with no published root method. The itemized bill: Kimi K3 found the exploit for $164.25, GLM-5.2 caught its fatal bugs for $21.90, and GLM-5.3 finished it in one day on day one of an $80 subscription, while the Claude Max work he'd already paid for ran until 'its safeguards cut me off.' The r/singularity top comment at 246 upvotes shifted the story to the ownership question rather than the exploit.

Graph relationships

BENCHMARKED AGAINST
Hy4
Hy4 was evaluated against Kimi K3 in Tencent's expert scoring.
Source finding
BENCHMARKED AGAINST
Terminal-Bench-Science
Kimi K3 scored 7.1% on Terminal-Bench-Science.
Source finding
BENCHMARKED AGAINST
LiveCodeBench
Kimi-K3 showed a 30.4% improvement on LiveCodeBench with manager-worker scaffold.
Source finding
SUPPORTS
llama.cpp
llama.cpp merged support for Kimi-K3 text model after a 19-day review.
Source finding
RELEASED
Moonshot
Moonshot released Kimi K3, a 2.8T-parameter open-weight model, on July 16.
Source finding
BUILT BY
Moonshot
Moonshot built Kimi K3, a 2.8T parameter open-weights model
Source finding
SUPPORTS
Unsloth
Unsloth lists Kimi K3 as a supported model
Source finding
RELEASED
Moonshot
Moonshot published Kimi K3 open weights on July 26–27, 2026.
Source finding
USES
WASTE
WASTE runs Kimi K3 with expert offloading on MacBook
Source finding
USES
ROCm
Kimi K3 inference on MI355X relies on ROCm for GPU support
Source finding
BENCHMARKED AGAINST
B300
Kimi K3 on MI355X was compared against B300 in cost-per-token performance
Source finding
BENCHMARKED AGAINST
B200
Kimi K3 on MI355X was compared against B200 in throughput benchmarks
Source finding

Source trail

r/LocalLLaMA Tencent announcement arXiv 2608.25399 Latent Space claiming 1.5x to 1.9x more throughput per kilowatt companion OpenAI post Apple's Mac refresh and Jalapeño next to each other 512GB of unified memory at 1.2TB/s left this week vertical-integration essay GitHub

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