DeepSeek V4

How We Hit 94% Accuracy at 20% of the Tokens Across 46 Repos

The claim that a verifiable IR beats vector search and GraphRAG only matters if the numbers hold up. So here is the run. 46 Kubernetes-ecosystem repositories, 150,000 files, 8 GB of code, measured end to end. What we tested, how we scored it, and why feeding the model 22x more context made it cheaper and more accurate at the same time.

$13.30 to Compile 1,000 Files Into a Verifiable IR, Once

The objection to compiling a codebase into a verifiable IR is always cost. It sounds like Opus pricing across every file. It isn't. At open-source-model rates it runs about $13.30 per 1,000 files, you pay it once, and per-file diffing means you only ever re-pay for what changed. Here is the real economics of the verifiable context layer, and why the expensive thing is not building the IR but living without one.

We Benchmarked 14 LLMs for Code Analysis. The Cheapest One That Works Costs 56× Less Than Claude Opus.

A real cost-vs-accuracy benchmark of DeepSeek, GLM, Kimi, Qwen, Sonnet, Opus, and GPT-5 for indexing codebases. Why per-file LLM analysis isn't actually expensive if you pick the right model.