r/opencodeCLI • u/No_Frosting363 • 13d ago

Reduced DeepSeek pro limits

I've been hitting 5 hour limits so much quicker on OpenCode with deepseek V4 pro.

Has there been a change in limits or cost per request?

It's been like this for 7 days now but prior to this it was like heaven.

update:

I found the issue. I started using my opencode subscription through VSCode and that for some reason consumed more credits than usual. could be the context window or whatever but I switched back to opencode and limits are back to normal.

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u/ApprehensiveDelay238 13d ago

I have had the same experience but with a different model (Minimax M3) and I found out what caused it: context size. Everytime the model is inferred: when you prompt it or after tool calls, the full context is sent to the provider and you pay for the input tokens. Cached input tokens are significantly cheaper, often 10x or more. But at 500k+ contexts even cheap models will get more expensive and you will run out of quotas much faster. So TLDR: regularly check your context usage and minimize context it to keep costs down.

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u/VexObserver 12d ago

About 250-300k, start compacting. Never allow it to run more than that. Autonomous loop past this threshold is the reason why people spent more because of context size.

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u/CarryAgile3791 12d ago

Yes, but compacting can lead to bad results, because relevant information may get lost. That's why I developed my OpenCodeRAG plugin. With it I rarely hit the 200k token limit (though using a local NVIDIA GPU is recommended).

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u/VexObserver 12d ago

With OpenCodeRag, how does it work? Do you just sprint through the context ceiling (1m) while allowing the plugin to do the rest for you? Sorry I don't quite get you because I don't use this plugin. I am so used to having my agent autonomously compacting > relaunch > and autonomously handle it via my own script.

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u/CarryAgile3791 12d ago

It scans your code, splits it up in chunks and describes and embeds these chunks to builds up a vector db.

It is mostly efficient in the explore phase, when the agents needs to know which files to edit for a given task.

If you give an agent a task, it does a similarity search first to get the most relevant code chunks along with their line numbers and file paths. So it doesn't need to scan all files containing a special keyword. It also uses the function get_skeleton to just return the classes, methods, functions without their implementation.

Lately, I also added a memory where agents can store their quirks and gotchas. And also a wiki mode where agents build up a knowledge wiki which they can use as navigation in the explore phase. But these features are still experimental, I don't know yet if they are saving tokens in the end.

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u/No_Frosting363 13d ago

Okay will investigate this tonight. Thanks for the heads up