Meet Kimi K2.6: Advancing Open-Source Coding
🔹Open-source SOTA on HLE w/ tools (54.0), SWE-Bench Pro (58.6), SWE-bench Multilingual (76.7), BrowseComp (83.2), Toolathlon (50.0), Charxiv w/ python(86.7), Math Vision w/ python (93.2)
What's new:
🔹Long-horizon coding - 4,000+ tool calls, over 12 hours of continuous execution, with generalization across languages (Rust, Go, Python) and tasks (frontend, devops, perf optimization).
🔹Motion-rich frontend - Videos in hero sections, WebGL shaders, GSAP + Framer Motion, Three.js 3D.
🔹Agent Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from K2.5's 100 / 1,500). One prompt, 100+ files.
🔹Proactive Agents - K2.6 model powers OpenClaw, Hermes Agent, etc for 24/7 autonomous ops.
🔹Claw Groups (research preview) - bring your own agents, command your friends', bots & humans in the loop.
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K2.6 is now live on https://t.co/YutVbwktG0 in chat mode and agent mode.
For production-grade coding, pair K2.6 with Kimi Code: https://t.co/uvoSJKyGCY
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🔗 API: https://t.co/EOZkbOwCN4
🔗 Tech blog: https://t.co/9wWvgIQSS3
🔗 Weights & code: https://t.co/Be0hjs2RTP
this part of the KIMI K2.6 launch blog is insane:
> it deployed Qwen3.5-0.8B model locally on a Mac.
> coded and optimized its inference in Zig
> (never knew you could do that)
> improved throughput from ~15 to ~193 tokens/sec
> made it 20% faster than LM Studio
> did 4,000+ tool calls, >12 hours of execution, 14 iterations
I just implemented Google’s TurboQuant for vLLM.
My USB-charger-sized HP ZGX now fits 4,083,072 KV-cache tokens on GB10.
This may be the biggest open inference breakthrough of 2026 so far.
Training is the flex. Inference is the forever bill.
After Huggingface, I truly believe Unsloth is most responsible for the democratization of deep learning.
Qwen3.5 series of models are GREAT. Even the 2B and 4B ones. 0.8B is immensely finetunable too.
Just having access to a readymade RL notebook is so cool. All you need now to train a model on your task is simply:
- a dataset of prompts and expected outcomes
- OR, a procedural function that generates a prompt and verifies the model's output as correct/incorrect
And that's it.
I just love what this team is doing.
Looks like it’s confirmed Cursor’s new model is based on Kimi! It reinforces a couple of things:
- open-source keeps being the greatest competition enabler
- another validation for chinese open-source that is now the biggest force shaping the global AI stack
- the frontier is no longer just about who trains from scratch, but who adapts, fine-tunes, and productizes fastest (seeing the same thing with OpenClaw for example).