I've found the latency and pricing make Mercury 2 extremely compelling for some UX experiments focused around automated note tagging/interlinking. Far more than the Gemini Flash Lite I used before, it made some interactions nearly frictionless, very close to how old school autocomplete/T9/autocorrect works in a manner that users don't even think about the processes behind it.
Sadly, it does not perform at the level of e.g. Haiku 3.5 for tool calling, despite their own benchmarks claiming parity with Haiku 4.5, but it does compete with Flash Lite there too.
Anything with very targeted output, sufficient existing input and that benefits from a seamless feeling lends itself to dLLMs. Could see a place in tab-complete too, though Cursors model seems to be sufficiently low latency already.
If you like Mercury 2 you should try Xiaomi Mimo-v2-flash.
I have an agentic benchmark and it shows Mercury 2 at 19/25 in 58 seconds and Mimo v2 Flash at 22/25 in 109 seconds
https://sql-benchmark.nicklothian.com/?highlight=xiaomi_mimo... (flip to the Cost vs Performance tab to see speed more graphically too)
Thanks for the recommendation and sharing your evals, will take a closer look at them. Yes, the Mimo models are very interesting, end-to-end pricing wise especially, though in my tool call runs, GLM 4.7 Flash did slightly better at roughly equal speed and full run cost. Is of course very task dependent and both are amazing options in the price range, but latency wise, nothing feels like Mercury 2 at the moment.
Yeah the speed is super impressive.
https://chatjimmy.ai/ from Taalas seems down at the moment but if you really want speed.... 18,000 tps is something to experience
Did you get a chance to evaluate coding performance?
Yes, nothing to write home about. It's all relative of course, what stack, what goal, what approach on which models perform best, but for regular day-to-day coding, I do not find it usable given alternatives.
Kimi, Mimimax and GLM models provide far more robust coding assistance at sometimes no cost (financed via data sharing) or for very cheap. Output quality, tool calling reliability and task adherence tend to be far more reliable across all three over Mercury 2, so if you consider the time to get usable code including reviews, manual fixes, different prompting attempts, etc. end-to-end you'll be faster.
Only "coding" task I have found Mercury 2 to have a place for code generation is a browser desktop with simple generated applets. Think artefacts/canvas output but via a search field if the applet has been generated previously.
With other models, I need to hide the load behind a splash screen, but with Mercury 2 it is so fast that it can feel frictionless. The demo at this point is limited by the fact that venturing beyond a simple calculator or todo list, the output becomes unpredictable and I struggle to get Mercury 2 to rely on pre-made components, etc. to ensure consistent appearance and a11y.
Despite the benchmarks, cost and speed figure suggesting something different, I have had the best overall results with Haiku 4.5, simply because GPT-5.4-nano is still unwilling to play nice with my approach to UI components. I am currently experimenting with some routing, using different models for different complexity, then using loading spinners only for certain models, but even if that works reliably, any model that I cannot force to rely on UI components in a consistent manner isn't gonna work, so for the time being it'd just route between less expensive and more expensive Anthropic models.
Coding wise, one more exception can be in-line suggestions, though I have no way to fairly compare that because the tab models I know about (like Cursors) are not available via API, but Mercury 2 seems to perform solidly there, at least in Zed for a TS code base.
Basically, whether code or anything else, unless your task is truly latency dependent, I believe there are better options out there. If it is, Mercury 2 can enable some amazing things.