This is real issue in our benchmarks. Beware of OpenRouter providers that don't specify quantizations or use lower ones than you might be expecting. OpenRouter does provide configuration options for this, and it often limits your options significantly. That being said, even with the best providers, Kimi-K2-thinking was underwhelming and slow on our benchmarks, albeit interesting and useful for temperature/variation.
Kimi K2.6, however, is the new open source leader, so far. Agentic evaluations still in progress, but one-shot coding reasoning benchmarks are ready at https://gertlabs.com/?mode=oneshot_coding
Openrouter has an "exacto" [1] option to favour higher quality providers for a given model. Have you found any benefits to using that?
Edit: Kimi K2 uses int4 during its training as well as inference [2]. I wonder if that affects the quality if different gguf creators may not convert these correctly?
[1] https://openrouter.ai/docs/guides/routing/model-variants/exa...
[2] https://www.reddit.com/r/LocalLLaMA/comments/1pzfuqg/why_kim...