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DeepSWE
3 posts about DeepSWE. Every summary links to the original.
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DeepSeek-V4 Flash 0731 vs GPT-5.6 Luna on DeepSWE: Cost and Coding
On the DeepSWE coding benchmark, GPT-5.6 Luna achieves 67.2% pass@1 versus DeepSeek-V4 Flash’s 53.3%, a 14-point gap. DeepSeek costs $0.10 per task, roughly six times less than Luna’s $0.61, delivering 532 solves per $100 versus Luna’s 110. A cascade that runs DeepSeek first and escalates only on failure solves 78.9% of tasks at $0.385 each, beating Luna’s solo accuracy and cutting cost by 37%. DeepSeek fails more gracefully (9% broken tests vs 15%) but is slower and dramatically weaker on JavaScript (35% vs 60%). Luna dominates seven of eight task domains, while DeepSeek leads only on query and config languages, illustrating how inexpensive models can combine with flagships for superior price-performance.
Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
GPT-5.6 Sol and Kimi K3 were compared on the DeepSWE software engineering benchmark using 904 graded rollouts across 113 tasks. Sol wins single-shot pass@1 (72.7% vs 68.5%) and reliability (84.5% tasks solved four-for-four versus 76.6%), while Kimi K3 leads pass@4 (89.4% vs 85.8%) with wider coverage at a substantially lower cost—$4.65 per rollout versus Sol’s $8.37, delivering 14.7 solves per $100 against Sol’s 5.3. The two models have a low task correlation of 0.46 and disagree on 18 tasks, enabling a cascade routing strategy that runs Kimi first and escalates to Sol when the test suite rejects the result, achieving 85.6% accuracy, beating both models individually and a perfect one-shot oracle router. Kimi K3 is open-weight and available on Together AI.
Kimi K3 vs Claude Fable 5 on DeepSWE: Cost and Coding
On DeepSWE, Kimi K3 trails Claude Fable 5 by 1.4 points at pass@1 (68.5% vs 69.9%) but leads at pass@2 (82.0 vs 80.2) and pass@4 (89.4% vs 88.5%) across 452 rollouts. Kimi K3 costs $4.65 per rollout versus $13.41 for Fable, delivering 2.8x more solved tasks per dollar. It covers 89.4% of the benchmark, solving 101 of 113 tasks, while Fable solves 100, though Fable is more reliable with 58 tasks solved four-for-four versus Kimi’s 45. The models share the highest cross-vendor similarity (0.72) and fail on the same tasks, with Kimi leading in Go and Fable leading in Python, JavaScript, TypeScript, and Rust. As an open-weight model, Kimi K3 offers a cost-effective alternative for retry-tolerant workloads.
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