Kadoch

في بحثك عن الحقيقة ستجد العديد من الأكاذيب التي تشكل جزءًا من حياتك.

[Kadoch @aprendizforever]

@DRzzizzozz #3

[Akshay 🚀 @akshay_pachaar]

Kimi K2.6 raises the bar for open-source models.

Moonshot released it yesterday, and for the first time, an open-weight model holds its ground against Claude Opus 4.6 on the benchmarks that matter for agentic work.

It also costs a fraction of the price.

𝗧𝗵𝗲 𝗽𝗿𝗶𝗰𝗶𝗻𝗴

Kimi K2.6 runs at $0.95 per million input tokens and $4 per million output tokens.

Claude Opus 4.6 runs at $5 and $25.

With cache hits, the gap widens. K2.6 drops to $0.16 per million on cached inputs. Opus 4.6 drops to $0.50.

That's roughly 5-6x cheaper across the board, before and after caching.

𝗧𝗵𝗲 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀

K2.6 leads Opus 4.6 on four of the six head-to-head comparisons Moonshot published:

- SWE-bench Pro: 58.6 vs 53.4 (agentic coding)
- HLE with tools: 54.0 vs 53.0 (agentic reasoning)
- DeepSearchQA: 92.5 vs 91.3 (deep research)
- LiveCodeBench: 89.6 vs 88.8

Opus 4.6 still wins on SWE-bench Multilingual and BrowseComp, but the gap is under a point in both.

𝗧𝗵𝗲 𝗽𝗮𝗿𝘁 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀

Benchmarks are the easy story. The harder and more interesting story is long-horizon execution.

K2.6 ran a single autonomous task for over 12 hours, making 4,000+ tool calls, to port and optimize inference for a small LLM in Zig, a language most models barely touch.

It ended up running around 20% faster than LM Studio on the same hardware.

Separately, it refactored an 8-year-old financial matching engine across 13 hours, delivering a 133% peak throughput gain.

This is the capability gap that usually separates frontier closed models from open ones. K2.6 closes a meaningful chunk of it.

You get weights you can actually deploy, a Modified MIT license, 5-6x lower inference cost, and performance that no longer forces you to compromise on agentic workloads.

The moat around Frontier Labs is shrinking fast.

Read more: https://t.co/ye6bkXBYTD

[Kadoch @aprendizforever]

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