DeepSeek-V4 Flash Benchmarked: One-Sixth Cost, 80% of Luna's Performance
Developer zainhas benchmarked DeepSeek-V4 Flash 0731 against GPT-5.6 Luna on the DeepSWE benchmark for software engineering tasks. The core finding is DeepSeek's exceptional cost-effectiveness: $0.10 per task (about 1/6 of Luna's $0.60) with 80% of Luna's accuracy (53.3% vs 67.2%). Parallel attempts or cascading with Luna can match or exceed Luna while significantly reducing cost. This comparison provides important guidance for model selection, highlighting the potential of low-cost models in software engineering.
Confirmed
- Cost and accuracy: DeepSeek costs $0.10 per task, Luna $0.60 (5-6x difference); accuracy 53.3% vs 67.2%, Luna only 14 points higher. DeepSeek end-to-end time 23 minutes.
- Multiple attempts: DeepSeek's economy allows parallel attempts: pass@2 accuracy 81.6% (cost $0.20), pass@4 90.3%; Luna 70.1% and 80.5% respectively.
- Cascade strategy: DeepSeek first, escalate to Luna on verification failure: accuracy 78.9%, cost $0.385, 37% lower than Luna alone ($0.61), accuracy +11.7%.
- Task overlap: Both solved 87 tasks; Luna solved 15 unique, DeepSeek only 4; correlation 0.50.
- Domain performance: Luna wins in 7/8 domains, leading by 30 points in hard areas like program analysis (69-33), concurrency (70-38), runtime internals (86-59); DeepSeek only better in query & config (78-70). DeepSeek collapses on JavaScript (35-60), weak on Python (49-65), decent on Rust (55-60) and Go (62-79).
- Failure mode: DeepSeek regression breakage rate 9% vs Luna 15%; DeepSeek fails more gracefully, while the pricier model needs regression testing.
Unconfirmed
- Specific test environment, task set details, and model version info not fully disclosed.
Why it matters
This benchmark gives developers a clear cost-performance trade-off: DeepSeek-V4 Flash is a high-value choice for budget-sensitive scenarios, and via multiple attempts or cascading, it can match or exceed top models without significant cost increase, promoting broader adoption of LLMs in software engineering.
2026-08-07 ~ 2026-08-08 · 14 related posts
- Episode 1: DeepSeek V4 Flash Hands-on: Low Cost, High Performance Sparks Community Buzz(2026-07-31, 33 posts)
- Episode 2: DeepSeek V4-Flash: Agent Leap, Open Source, Ultra-Low Price(2026-07-31, 100 posts)
- Episode 3: DeepSeek V4 Series Sparks Debate: Flash Performance Gains and Pro Scale(2026-07-31, 3 posts)
- Episode 4: DeepSeek V4 Flash Tops OpenRouter, Costs 1% of Rivals(2026-08-03, 14 posts)
- Episode 5: DeepSeek-V4-Flash Released Across Major Platforms(2026-08-06, 4 posts)
- Episode 6: DeepSeek-V4 Flash Benchmarked: One-Sixth Cost, 80% of Luna's Performance(2026-08-07, 14 posts)
Primary sources
- [source] DeepSeek-V4 vs GPT-5.6: 1/6 the Cost, 80% the Quality on Coding Tasks — zainhas · 2026-08-07
- Luna Costs 6x More for Only 14pts Accuracy Gain; DeepSeek Wins on Value — zainhas · 2026-08-07
- DeepSeek at $0.10/Task: 5x Cheaper Than Luna — zainhas · 2026-08-07
- [source] DeepSeek's Parallel Attempts Catch Up: pass@2 Beats Luna pass@1 at $0.20 — zainhas · 2026-08-07
- DeepSeek Fails More Gracefully: 9% Regression Rate vs Luna's 15% — zainhas · 2026-08-07
- Luna Wins 7/8 Domains, DeepSeek Only Holds Query & Config — zainhas · 2026-08-07
- DeepSeek's JavaScript Performance Collapses, Rust and Go Fine — zainhas · 2026-08-07
- DeepSeek vs Luna: High Overlap, Luna Solves 15 Unique Tasks, DeepSeek Only 4 — zainhas · 2026-08-07
- [source] DeepSeek+Luna Cascade: 78.9% Accuracy at 63% Cost, Beats Luna Alone — zainhas · 2026-08-07
- DeepSeek + Luna Combo Strategy: 37% Cheaper, 11.7% More Accurate — zainhas · 2026-08-07
- DeepSeek-V4 Flash Delivers 80% of GPT-5.6 Luna Performance at 1/6 Cost — zainhas · 2026-08-07
- DeepSeek Flash Hits 80% of GPT 5.6 Luna Quality at 1/6 the Cost — pbaylies · 2026-08-07
- DeepSeek V4 Beats GPT 5.6 in Coding Cost-Efficiency by 6x — togethercompute · 2026-08-07
- DeepSeek Cascade Beats GPT-5.6 Luna on DeepSWE at 37% Lower Cost — togethercompute · 2026-08-08