I've just found myself using OpenRouter if we need Google models for a project, it's worth the extra 5% just not to have to deal with the utter disaster that is their product offering.
This is the wrong interpretation of the oxcaml project. If you look at the features and work on it, it's primarily performance or parallelism safety features.
The latter going much further than most mainstream languages.
And even when it does copy other products, it seems to be doing a terrible job of them.
Google's AI offering is a complete nightmare to use. Three different APIs, at least two different subscriptions, documentation that uses them interchangeably.
For Gemini's API it's often much simpler to actually pay OpenRouter the 5% surchargeto BYOK than deal with it all.
I still can't use my Google AI Pro account with gemini-cli..
For example, Google's inexplicable design decisions around libraries and APIs means it's often worth the 5% premium to just use OpenRouter to access their models. In other cases it's about which models particular agents default to.
Sonnet 4 is extremely good for tool-usage agentic setups though - something I have found other models struggle to do over a long-context.
100% this. We actually use OpenRouter (and pay their surcharge) with Gemini 2.5 Pro just because we can actually control spend via spent limit on keys (A++ feature) and prepaid credit.
It's interesting that there's a price nearly 6x price difference between reasoning and no reasoning.
This implies it's not a hybrid model that can just skip reasoning steps if requested.
Anyone know what else they might be doing?
Reasoning means contexts will be longer (for thinking tokens) and there's an increase in cost to inference with a longer context but it's not going to be 6x.
"In addition, S3 Express One Zone has reduced the per-GB charges for data uploads and retrievals by 60 percent, and these charges now apply to all bytes transferred rather than just portions of requests greater than 512 KB"
It's not clear but are there cases where this could be a significant price rise? If you exclusively had small objects (<512kb) being written and read then this could add up quickly.
> Both of our models are trained on top of DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Qwen-32B.
Not to take away from their work but this shouldn't be buried at the bottom of the page - there's a gulf between completely new models and fine-tuning.