One example is Opencode. https://opencode.ai/v2/docs/console/models/

"Privacy# All these models are hosted in the US. Providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:

Big Pickle: During its free period, collected data may be used to improve the model.

DeepSeek V4 Flash Free: During its free period, collected data may be used to improve the model.

MiMo-V2.5 Free: During its free period, collected data may be used to improve the model.

Laguna S 2.1 Free: During its free period, collected data may be used to improve the model.

Ling-3.0-tiny Free: During its free period, collected data may be used to improve the model.

LongCat-2.0 Free: During its free period, collected data may be used to improve the model.

North Mini Code Free: During its free period, collected data may be retained and used to improve the model. Do not submit personal or confidential data. See the provider’s Terms of Use and Privacy Policy.

Nemotron 3 Ultra Free (NVIDIA free endpoints): Trial use only — do not submit personal or confidential data. Your use is logged for security purposes and to improve NVIDIA products and services. The logged session data for improvement purposes is not linked to your identity or any persistent identifier. For more information about data processing practices, see the Privacy Policy. By interacting with this endpoint, you consent to the collection, recording, and use of such information and the NVIDIA API Trial Terms of Service."

openrouter, "together" provider is fastest (165 t/s at the time of writing) and has ZDR and all

https://openrouter.ai/deepseek/deepseek-v4.1-flash?endpoint=...

For caching, only if you don't specify your preferred providers and let OpenRouter route each request itself. I have stuff like this in my OpenCode config for each model I use and I regularly get ~90-95% cache hit rates.

    "order": ["relace", "coreweave", "novita", "baseten", "together"],
    "allow_fallbacks": false

It still won't be quite as high as you'd get by just using DeepSeek because occasionally a request will fail and you'll get routed to a backup provider with nothing cached, but it's close enough not to matter in most instances.

But I can't argue with the lower off-peak pricing when using DeepSeek directly. The downside is they train their models on your input, which might be a deal-breaker for many users (as it is for me).

Define easy. I am running 40+ probes to crack the best compression algorithm, and I spent $3. If you surgically tackle the problems, you can do really complicated work for less money than by assuming a frontier top model will one-shot everything.

Well, at least I spent lots of dollars, and I had to use those models the same way I am using local and cheap models, with the same results.