Moonshot's own K2.6 benchmark table is still the cleanest way to read where the model stands, because it compares Kimi K2.6, GPT-5.4, Claude, Gemini, and K2.5 under one vendor-controlled setup instead of stitching together mismatched charts.
As of April 21, 2026, that table shows a pretty clear pattern: Kimi K2.6 is much stronger than K2.5, highly competitive on coding and agent-style workloads, and still not the top result on every pure reasoning or multimodal benchmark.
What the table says in practice
The biggest takeaway is the within-family jump. Against K2.5, K2.6 posts broad gains on HLE-Full with tools, DeepSearchQA, Terminal-Bench 2.0, SWE-Bench Pro, SWE-Bench Verified, LiveCodeBench, GPQA-Diamond, and MMMU-Pro.
That lines up with Moonshot's positioning: K2.6 is not a small refresh. It is a real upgrade for long-horizon coding and agent execution.
Where K2.6 looks strongest
K2.6 looks best on tasks that resemble real engineering work: tool use, multi-step execution, coding benches, and long agent chains. That is where the model starts looking less like a benchmark toy and more like something you would actually build around.
Where frontier models still lead
Moonshot's own table still shows stronger numbers elsewhere: GPT-5.4 leads on several reasoning-heavy tests, Gemini 3.1 Pro leads on some vision and coding rows, and Claude stays slightly ahead on a couple of SWE-Bench variants.
So the honest summary is not that K2.6 wins everything. It is that K2.6 closes most of the gap while staying meaningfully stronger than K2.5.
Bottom line
If your workload looks like coding, tool-augmented workflows, or long-running agent execution, K2.6 is much easier to take seriously than K2.5. If you only care about raw reasoning crowns, you still need to look at the frontier leaders row by row.
Source article: https://kimi-k25.com/kimi-k2-6-benchmark
Homepage: https://kimi-k25.com/
Model pages:
Top comments (0)