DeepSeek V4 Flash Beats Claude Fable 5 on Terminal-Bench via Self-Verification at 1/11 the Cost
A GitHub project demonstrates a "self-verification scaling" approach: DeepSeek V4 Flash samples 5 candidate solutions at inference time, then the same model ranks them using LLM-as-a-Verifier to pick the best. According to posts by @yogthos, @Azaliamirh and others, this lifts accuracy on Terminal-Bench 2.1 from 79% to 88%, surpassing the closed-source frontier model Claude Fable 5 at only 1/11 the cost. The takeaway: well-designed verification scaling lets cheap open models rival or beat closed frontier performance.
Confirmed
- Benchmark: Terminal-Bench 2.1; method: sample 5 candidates, rank with LLM-as-a-Verifier
- Accuracy improved from 79% to 88%; cost is 1/11 of Claude Fable 5
- Posts by @ccerrato147 and @TheZachMueller are reposts; the original source is the GitHub project
Why it matters
- Test-time scaling (sampling plus self-verification) is an effective path for low-cost open models to close the gap with closed frontier models
- Verification mechanisms have far lower marginal cost than stacking parameters or using a larger model
- @KingDDev frames it as "scaling self-verification", an instance of the inference-scaling direction
2026-08-18 ~ 2026-08-19 · 5 related posts
Primary sources
- [source] LLM-as-a-Verifier boosts DeepSeek past Claude at 1/11th the cost — Azaliamirh · 2026-08-18
- DeepSeek V4 Flash beats Claude on Terminal-Bench with self-verification at 1/11 cost — ccerrato147 · 2026-08-18
- Self-verification scaling with DeepSeek V4 Flash beats Claude at 1/11 the cost — TheZachMueller · 2026-08-18
- [source] DeepSeek V4 Flash self-verification beats Claude Fable 5 at 11x lower cost — yogthos · 2026-08-18
- [source] DeepSeek V4 Flash with self-verification scaling beats Claude Fable 5 at 1/11 the cost — KingDDev · 2026-08-19