DeepSeek-V4 Flash vs GPT-5.6 Luna: Cost-Effective, 80% Quality

Developer zainhas conducted a software engineering benchmark test on DeepSeek-V4 Flash 0731 and GPT-5.6 Luna using the DeepSWE benchmark. The core takeaway is DeepSeek's exceptional cost-effectiveness: the cost per task is only $0.10, about 1/6th of Luna's ($0.60), while achieving 80% of Luna's accuracy (53.3% vs 67.2%). By utilizing a parallel multi-attempt strategy (pass@2 costing $0.20) or cascading with Luna, DeepSeek can match or even surpass Luna while significantly cutting costs. This comparison provides a crucial reference for developers selecting models, highlighting the potential of low-cost models in software engineering scenarios.

Confirmed

• Cost and Accuracy: DeepSeek costs $0.10 per task, while Luna costs $0.60, a roughly 5x difference; accuracy stands at 53.3% for DeepSeek and 67.2% for Luna, with Luna leading by only about 14 percentage points.
• Multi-Attempt Performance: DeepSeek's accuracy reaches 81.6% after 2 attempts (costing $0.20) and 90.3% after 4; Luna's corresponding figures are 70.1% and 80.5%.
• Cascade Strategy: Processing first with DeepSeek and escalating to Luna upon verification failure yields 78.9% accuracy at a cost of $0.385. Compared to using Luna alone (67.2% accuracy, $0.61 cost), this reduces costs by 63% and improves accuracy by 11.7%.
• Task Overlap: The two models jointly solved 87 tasks; Luna uniquely solved 15, while DeepSeek uniquely solved only 4, showing a correlation of 0.50.
• Domain Performance: Luna wins in 7 out of 8 domains, with DeepSeek only leading in Queries and Configurations (78-70). DeepSeek struggled significantly with JavaScript (35-60), showed weaker Python performance (49-65), and was passable in Rust (55-60) and Go (62-79).
• Failure Modes: DeepSeek has a 9% regression breakage rate compared to Luna's 15%, meaning DeepSeek fails more gracefully.

Unconfirmed

• Specific testing environments, task set details, and model version information were not fully disclosed in the original post.

Why It Matters

This hands-on test provides developers with clear cost-performance trade-off data: in budget-sensitive scenarios, DeepSeek-V4 Flash emerges as a highly cost-effective choice due to its ultra-low cost and acceptable accuracy. Furthermore, by employing multi-attempt or cascading strategies, it can match or exceed top-tier models without substantially increasing costs. This helps drive the broader application of large models in software engineering.

2026-08-07 ~ 2026-08-07 · 11 related posts

• [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
• [source] DeepSeek at $0.10/Task: 5x Cheaper Than Luna — zainhas · 2026-08-07
• 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