Cline-pass has been absolutely killer in all my initial tests.

I've now got it set up on all my machines.

I torture tested it with a massive pile of vibe coding prompts and agentic tasks all day yesterday and into the night (wow, I built a lot of applications, and had the LLMs perform all sorts of system configurations, app installations, file management tasks, etc.).

I used every available model to complete multiple tasks which required lots of reasoning, including a building a large collection of games, CRUD apps, and a unique collection of quite a few apps build in the old Rebol 2 programming language. Even the biggest models don't know how to code in Rebol well, so they burn lots of tokens looping around debug error output iterations. See https://aibynick.com/thread/52#post-139 - that was just a small piece of everything I completed in my torture test of cline-pass.

In all that work, I never got above 7% of cline-pass's 5 hour limit, and all that activity burned only 4% of my weekly limit, and only 2% of my monthly limit. That included use of GLM 5.2, Kimi 2.7, and all the other huge models in the cline-pass stable.

The massive volume of work completed with Deepseek V4 Flash seemed to be basically free - I only ever saw the Cline dashboard show 1% of my 5 hour limit reached, while absolutely thrashing that model constantly for hours. Performance of V4 Flash was ridiculously fast.

I should point out that one significant difference between Cline-pass and OpenRouter is that OpenRouter often simply runs requests through the APIs of the original providers of each LLM, such as Deepseek.com (not always, but for a good percentage of all their requests). Because providers like Cline-pass can run the open source models on their own servers, potentially in the US, any ban of foreign services would not necessarily shut them down.

This reassures my worries a bit, because I think there is a likelihood of potential collapse in the very volatile LLM industry. With open source models that are hostable on GPU infrastructure anywhere in the world, there will likely always be some way to use capable LLMs, without necessarily having to resort to self-hosting on local hardware. This is really important because the hardware required to self-host large models is extremely expensive - even if you spend 10s of thousands of dollars, performance won't be nearly as fast or as accessible as the APIs we've all gotten accustomed to.

Cline-pass has been absolutely killer in all my initial tests. I've now got it set up on all my machines.

I torture tested it with a massive pile of vibe coding prompts and agentic tasks all day yesterday and into the night. I used every available model to complete multiple tasks which required lots of reasoning, including a large collection of Rebol 2 apps (even the big models don't know how to code in Rebol well, so they burn lots of tokens looping around debug error output iterations. See https://aibynick.com/thread/52#post-139 ).

I never got above 7% of cline-pass's 5 hour limit, and all that activity burned only 4% of my weekly limit, and only 2% of my monthly limit. That included use of GLM 5.2, Kimi 2.7, and all the other huge models in the cline-pass stable.

The massive volume of work completed with Deepseek V4 Flash seemed to be basically free, and the performance was ridiculously fast. I only ever saw the Cline dashboard show 1% of my 5 hour limit reached, while absolutely thrashing that model constantly for hours.

Also, one significant difference to note between OpenRouter and Cline-pass is that OpenRouter is often simply running requests through the APIs of the original providers, such as Deepseek.com (not always, but for a good percentage of all their requests). Because providers like Cline-pass are running the open source models on their own servers, often in the US, banning foreign services won't necessarily shut them down.

This reassures my worries a bit about the collapse of the very volatile LLM industry. With open source models hosted nearby, there will likely always be some way to use capable LLMs, without necessarily having to resort to self-hosting on local hardware.

Cline-pass has been absolutely killer in all my initial tests. I've now got it set up on all my machines. I torture tested it with a massive pile of vibe coding prompts and agentic tasks all day yesterday and into the night. I used every available model to complete multiple tasks which required lots of reasoning, including a large collection of Rebol 2 apps (even the big models don't know how to code in Rebol well, so they burn lots of tokens looping around debug error output iterations). I never got above 7% of the 5 hour limit, and all that activity burned only 4% of my weekly limit and 2% of my monthly limit. All the work with Deepseek V4 Flash seemed to be basically free, and the performance was fast. I only ever saw the Cline dashboard show 1% of my 5 hour limit reached, while thrashing that model constantly for hours.

Also, one significant difference between Openrouter and Cline-pass is that Openrouter is often simply running requests through the APIs of the original providers, such as Deepseek.com (not always, but for a good percentage of all their requests). Providers like Cline-pass are running the open source models on their own servers, often in the US, so banning foreign services won't necessarily shut them down.

To set up cline-pass as a model provider in Pi (in addition to OpenRouter or any other LLM API provider you already have set up), first create an API key at:

https://app.cline.bot/dashboard

Click: Account > API Keys > Create API Key

The run this prompt in Pi (replace with the API key you created above):

Please set up the cline-pass provider in pi. Here's what to do:

1. Update `~/.pi/agent/models.json` with a provider called `cline-pass` pointing at `https://api.cline.bot/api/v1` using `api: "openai-completions"`, with these 10 models (all using `cline-pass/` prefix):

   | Model | Thinking? |
   |---|---|
   | `cline-pass/glm-5.2` | no |
   | `cline-pass/kimi-k2.7-code` | no |
   | `cline-pass/kimi-k2.6` | no |
   | `cline-pass/deepseek-v4-pro` | yes (thinkingFormat: "deepseek") |
   | `cline-pass/deepseek-v4-flash` | yes (thinkingFormat: "deepseek") |
   | `cline-pass/mimo-v2.5` | no |
   | `cline-pass/mimo-v2.5-pro` | no |
   | `cline-pass/minimax-m3` | no |
   | `cline-pass/qwen3.7-max` | yes (thinkingFormat: "qwen") |
   | `cline-pass/qwen3.7-plus` | yes (thinkingFormat: "qwen") |

2. Then store the API key. **Check if `~/.pi/agent/auth.json` already exists first using `cat ~/.pi/agent/auth.json`** — if it has other provider keys, add the cline-pass key alongside them; don't overwrite the file. Store the key in this format:
   ```json
   {
     "cline-pass": {
       "type": "api_key",
       "key": "<your_api_key>"
     }
   }
   ```

3. Run `pi --list-models` to verify the 10 cline-pass models and all existing models still show.

4. Tell me when it's done.

To set up cline-pass in Pi, first create an API key at:

https://app.cline.bot/dashboard

Click Account > API Keys > Create API Key

The run this prompt in Pi (replace with the API key you created above):

Please set up the cline-pass provider in pi. Here's what to do:

1. Update `~/.pi/agent/models.json` with a provider called `cline-pass` pointing at `https://api.cline.bot/api/v1` using `api: "openai-completions"`, with these 10 models (all using `cline-pass/` prefix):

   | Model | Thinking? |
   |---|---|
   | `cline-pass/glm-5.2` | no |
   | `cline-pass/kimi-k2.7-code` | no |
   | `cline-pass/kimi-k2.6` | no |
   | `cline-pass/deepseek-v4-pro` | yes (thinkingFormat: "deepseek") |
   | `cline-pass/deepseek-v4-flash` | yes (thinkingFormat: "deepseek") |
   | `cline-pass/mimo-v2.5` | no |
   | `cline-pass/mimo-v2.5-pro` | no |
   | `cline-pass/minimax-m3` | no |
   | `cline-pass/qwen3.7-max` | yes (thinkingFormat: "qwen") |
   | `cline-pass/qwen3.7-plus` | yes (thinkingFormat: "qwen") |

2. Then store the API key. **Check if `~/.pi/agent/auth.json` already exists first using `cat ~/.pi/agent/auth.json`** — if it has other provider keys, add the cline-pass key alongside them; don't overwrite the file. Store the key in this format:
   ```json
   {
     "cline-pass": {
       "type": "api_key",
       "key": "<your_api_key>"
     }
   }
   ```

3. Run `pi --list-models` to verify the 10 cline-pass models and all existing models still show.

4. Tell me when it's done.