I have been using DeepSeek V4 Flash through OpenCode Go.
The short version: it is very good. I would not call it literally unlimited, but for ordinary coding work, investigation, refactoring, and small implementation tasks, it is almost frighteningly usable.
DeepSeek V4 Flash is extremely useful on OpenCode Go. The main downside is that I cannot paste images into it. My current setup is to use GPT-5.5 as the commander and DeepSeek V4 Flash as a fast execution model.
What feels good
First, it feels fast.
For tasks that do not need a heavier model, I can send a lot of work to DeepSeek V4 Flash: small code changes, reading logs, organizing files, breaking down TODOs, and understanding existing code.
OpenCode Go model listings describe DeepSeek V4 Flash as a text-input model with a very large 1M-token-class context window. The pricing is also low enough that it makes sense to use it repeatedly for small tasks.
It works especially well for:
- reading code quickly
- checking diffs
- proposing small fixes
- summarizing long logs
- acting as a worker model under a stronger coordinator
For that kind of work, it is seriously practical.
The weak point: no image paste
The biggest downside for me is image input.
There are many times when I want to paste a screenshot and say, “look at this.” UI bugs, error screens, admin panels, charts, settings pages. Not being able to send the image directly is inconvenient.
From the public model listings I checked, DeepSeek V4 Flash on OpenCode Go is listed as a text input / text output model.
So the right expectation is: strong for text, code, and logs; not the model I would use when the task depends on reading a screenshot.
How I use it
Right now, I use GPT-5.5 as the commander.
GPT-5.5 handles the big decisions, multi-agent coordination, final judgment, and anything that needs image understanding. DeepSeek V4 Flash handles the fast execution work through OpenCode Go.
That split works very well.
GPT-5.5 is the coordinator. DeepSeek V4 Flash is the fast worker. That is the mental model that fits my workflow.
Quick research notes
Public listings describe DeepSeek V4 Flash as a 284B total / 13B active MoE model with a 1M-token-class context window and low input/output pricing.
OpenRouter lists deepseek/deepseek-v4-flash with a 1,048,576-token context window, $0.14/M input, and $0.28/M output.
The OpenCode Go listing describes DeepSeek V4 Flash as deepseek-v4-flash / deepseek-flash, with text input, text output, tool calling, reasoning, and structured output support.
References:
- OpenRouter: https://openrouter.ai/deepseek/deepseek-v4-flash
- whichllm OpenCode Go listing: https://whichllm.io/models/opencode-go-deepseek-v4-flash
- whichllm DeepSeek listing: https://whichllm.io/models/deepseek-deepseek-v4-flash
Bottom line
DeepSeek V4 Flash is very usable.
But the lack of image paste is a clear limitation. If I need to show a screenshot, I still use GPT-5.5 or another image-capable model.
For text, code, and logs, though, DeepSeek V4 Flash on OpenCode Go is a strong everyday worker.
My current setup:
- Commander: GPT-5.5
- Worker: DeepSeek V4 Flash
- Image review: an image-capable model
That combination is working very well.
