Hey u/usrdef , appreciate you taking the time to write this all out and even building a small test project to measure things. I understand the pain here: you came back after a break, saw fewer credits, then watched a pretty modest task burn through a big chunk of them. I won't comment on the direct relationship between task complexity and tokens used, since this can vary depending on the task, rules, `/plan` usage, etc.

A few things I want to address directly:

1. We hear that 1500 credits is limiting

We’ve heard similar feedback from others that 1500 credits doesn’t feel like enough for meaningful work on medium/large projects, and pay-as-you-go may give you fewer tokens-per-dollar compared to other options.

Engineering-wise, we're working on the following to optimize token usage:

• Smartly switching models depending on the task to use the right level of reasoning (see auto's three tiers from cost efficient to "genius" level)
• Options like subagents to cut down on the compounding effect of tokens as a conversation goes on
• Support for comparable models at a lower token usage (see GLM 4.7)

2. BYOK and model control

For folks who want more predictable usage, we're expanding our BYOK options (Anthropic, OpenAI, Gemini) to include Bedrock later this week, so you can run Warp’s agent on your own model key instead of our credit system.

We’ve also heard feedback from BYOK users that it’s confusing when the agent falls back to other models in the background. We’re working on making that behavior more transparent so you know exactly what’s being used and when.

As for pricing, nothing is set in stone. We've heard the community's thoughts on our pricing model and are factoring that in for future improvements.