I tried at it creative writing - and, with thinking off, it was considerably better than Mercury 2 and generally good in fact, not very sloppy. Now with thinking on, it got worse, began hallucinating things; this is something I've noticed with all recent models - enabling reasoning causes hallucinations in creative writing assignments.
Similar, I've found that on most recent models I've tried, reasoning will cause it to spend quite a lot of time thinking about whether even the most wholesome fictional scenario is something it's allowed to write about, which in turn causes it to start hallucinating details. For creative writing I've found the minimum of thinking is the best way.
I’m using this model to „rerank” results from vector store. The model is provided a set of results and asked to produce a string of 1s and 0s where the offset reflects the position in the result set. The prompt goes along the line „do this set of result match the provided query X”. Works like a charm, normally I would use a small non reasoning model, but given how Mercury produces the output its blazingly fast - which is what I was optimizing for - not to increase the search latency. It helped improving our search in a way that reranker could get close to.
"If you do not want us to use your User Submissions to train our models, you can opt-out by setting the ‘Improve the model for everyone’ option under User Settings in the API Platform to OFF."
Interesting model. I tried to make Mercury investigate the hardcoded prompts in my (aider-derived) agent harness and repeatedly got this error:
> server: Upstream error from Inception: I'm sorry, but I can't share details of my architecture or training process. Would you like to learn about how language models work in general instead?
It looks like an overeager IP-protection classifier. However, the model recovered and completed the turn despite the errors (three total).
Inception is one of the most interesting neolabs with their diffusion-based architectures. My understanding is that their primary business is low latency voice applications but they are seriously pursuing coding.
We tested Mercury 2.5 Preview, which is nowhere close to the frontier (and not advertised as such), but it's actually usable as a general-purpose chatbot. It's comparable in problem solving ability to some last-gen open weights models, and the price and cost make it compelling. However, they have not figured out general purpose tool use and agentic coding (their model performs worse on our problems when given a custom harness). If they do, I see a lot of real-time applications that the speed and cost will enable.
Same. Models that are fast (and thus by extension, in some direction, efficient) but not local/self-hosted IMO fill a niche for quick and cheap (and acceptable quality, of course) inference in business contexts.
This should make an excellent choice for arbiter in llm-consortium, mercury-2 was pretty good. One of the main drawbacks of the multi-model system is the added latency of the llm judge, but having a model run at 1100tps goes a long a way to alleviate that.
I used it for testing my voice agent. It was basically what I expected. Good fast model but "generic" or "vanilla" is how i would describe its personality emulation capability as. Gemma models still outperform it in that department. As far as technicals, one thing i found annoying is cash use was not that good, it missed more then i liked, i contacted support and they were fast and responsive and said they were working on that issue, maybe they solved it with 2.5? Anyways, im prolly gonna try 2.5 again see if anything different, but cant deny the speed, thats the biggest thing this company has going for this offering as if you are in the business of classical cascaded voice agent systems, latency is number one priority and this thing is fast....
latency, instruction adherence, reliable tool cools, conversationality are all in tension.
it's great when you can get a 170ms ttft. but if you have 700 ms endpointing on the stt side and 300ms ttfb on the voice side, then you haven't really made something super snappy.
I'd imagine at this point they are likely an acquisition target if they can get a halfway decent model. I can't imagine having diffusion sub-agents (or sub-sub-agents) in an orchestration wouldn't be beneficial.
I'm less bullish, this model and previous are halfway decent and you can do diffusion sub-agents and sub-agent-agents today, and there hasn't been a sea change, or anything noticeable or well-known. Economically, there's ~no moat, diffusion models aren't a mysterious untame-able force.
interesting:
Quality: 40% increase in intelligence from Mercury 2. Comparable to cost-optimized frontier models like GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5.
available : https://openrouter.ai/inception/mercury-2.5
Not just a model - It's a diffusion model. Instead of Next Token prediction it builds the entire page at once and then denoises it. Kind of mind blowing when you watch it happen.
They are comparing it to 2 and 3 version old flash/fast versions of models but purely for tok/s. Then only comparing it to Mercury 2 on intelligence. This is very misleading and I suspect this model is basically useless.
Even pretty dumb models are useful for running subtasks (especially at this sort of speed). Note that in the coding section they only mention using it as a subagent for a smarter model
I tried at it creative writing - and, with thinking off, it was considerably better than Mercury 2 and generally good in fact, not very sloppy. Now with thinking on, it got worse, began hallucinating things; this is something I've noticed with all recent models - enabling reasoning causes hallucinations in creative writing assignments.
Similar, I've found that on most recent models I've tried, reasoning will cause it to spend quite a lot of time thinking about whether even the most wholesome fictional scenario is something it's allowed to write about, which in turn causes it to start hallucinating details. For creative writing I've found the minimum of thinking is the best way.
Got my hopes up when it said widely available GPUs that it would be open weights but it doesn’t seem like it sadly
I’m using this model to „rerank” results from vector store. The model is provided a set of results and asked to produce a string of 1s and 0s where the offset reflects the position in the result set. The prompt goes along the line „do this set of result match the provided query X”. Works like a charm, normally I would use a small non reasoning model, but given how Mercury produces the output its blazingly fast - which is what I was optimizing for - not to increase the search latency. It helped improving our search in a way that reranker could get close to.
I like the model.
FYI:
"If you do not want us to use your User Submissions to train our models, you can opt-out by setting the ‘Improve the model for everyone’ option under User Settings in the API Platform to OFF."
Interesting model. I tried to make Mercury investigate the hardcoded prompts in my (aider-derived) agent harness and repeatedly got this error:
> server: Upstream error from Inception: I'm sorry, but I can't share details of my architecture or training process. Would you like to learn about how language models work in general instead?
It looks like an overeager IP-protection classifier. However, the model recovered and completed the turn despite the errors (three total).
Diffusion is already being used in Drafter in many LLMs.
many people are running Qwen 3.8 27b on TPU at 130tk/s for free on Kaggle TPUs:
https://www.reddit.com/r/Qwen_AI/comments/1w6gv32/qwen3827b_...
I wonder if we are going to see boxes appear soon, which can run these models for dirt cheap.
Inception is one of the most interesting neolabs with their diffusion-based architectures. My understanding is that their primary business is low latency voice applications but they are seriously pursuing coding.
We tested Mercury 2.5 Preview, which is nowhere close to the frontier (and not advertised as such), but it's actually usable as a general-purpose chatbot. It's comparable in problem solving ability to some last-gen open weights models, and the price and cost make it compelling. However, they have not figured out general purpose tool use and agentic coding (their model performs worse on our problems when given a custom harness). If they do, I see a lot of real-time applications that the speed and cost will enable.
Not related to the Mercury language:
https://mercurylang.org/
Do diffusion models let me specify how long i want the answer beforehand? That seems like it could be useful.
Congrats! Happy to see someone seriously pursuing this direction.
Same. Models that are fast (and thus by extension, in some direction, efficient) but not local/self-hosted IMO fill a niche for quick and cheap (and acceptable quality, of course) inference in business contexts.
This should make an excellent choice for arbiter in llm-consortium, mercury-2 was pretty good. One of the main drawbacks of the multi-model system is the added latency of the llm judge, but having a model run at 1100tps goes a long a way to alleviate that.
Anyone here use Mercury 2.0? Curious what your experience with the model is.
I used it for testing my voice agent. It was basically what I expected. Good fast model but "generic" or "vanilla" is how i would describe its personality emulation capability as. Gemma models still outperform it in that department. As far as technicals, one thing i found annoying is cash use was not that good, it missed more then i liked, i contacted support and they were fast and responsive and said they were working on that issue, maybe they solved it with 2.5? Anyways, im prolly gonna try 2.5 again see if anything different, but cant deny the speed, thats the biggest thing this company has going for this offering as if you are in the business of classical cascaded voice agent systems, latency is number one priority and this thing is fast....
latency, instruction adherence, reliable tool cools, conversationality are all in tension.
it's great when you can get a 170ms ttft. but if you have 700 ms endpointing on the stt side and 300ms ttfb on the voice side, then you haven't really made something super snappy.
I'd imagine at this point they are likely an acquisition target if they can get a halfway decent model. I can't imagine having diffusion sub-agents (or sub-sub-agents) in an orchestration wouldn't be beneficial.
I'm less bullish, this model and previous are halfway decent and you can do diffusion sub-agents and sub-agent-agents today, and there hasn't been a sea change, or anything noticeable or well-known. Economically, there's ~no moat, diffusion models aren't a mysterious untame-able force.
interesting: Quality: 40% increase in intelligence from Mercury 2. Comparable to cost-optimized frontier models like GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5. available : https://openrouter.ai/inception/mercury-2.5
Oh great a new model annoncemzzzzzz ZZZZZZZZZ
Not just a model - It's a diffusion model. Instead of Next Token prediction it builds the entire page at once and then denoises it. Kind of mind blowing when you watch it happen.
Ok well that is actually interesting, thank you. Instead of OpenAI beta 6.3pre3 “fairyfloss”
I would have missed this, thanks
I like the text output on logical and historical content that I sampled so far
They are comparing it to 2 and 3 version old flash/fast versions of models but purely for tok/s. Then only comparing it to Mercury 2 on intelligence. This is very misleading and I suspect this model is basically useless.
Even pretty dumb models are useful for running subtasks (especially at this sort of speed). Note that in the coding section they only mention using it as a subagent for a smarter model
This token speed is too fast.
the output is good and fast. like it, not only fast, but a new architectures.
I love this it is so fucking fast!
I thought this was going to be about a boat motor.
I thought it was going to be about Mercury[1] the programming language, and was confused about the domain name.
[1] - https://www.mercurylang.org/