Tutorials, deep dives and product notes — built for developers.
DeepSeek V4 Flash ($0.28/1M, MIT) vs GPT-5.4 Mini ($4.50/1M). Mini leads SWE-bench Pro (+1.8) & Terminal-Bench (+3.1). Flash leads LiveCodeBench (91.6%), HLE (+3.6), and is 16× cheaper. The budget coding tier has never been more competitive.
Qwen 3.7 Max leads 5/6 coding benchmarks including SWE-bench Pro (60.6% vs 55.4%). But DeepSeek V4 Pro dominates algorithmic coding (LiveCodeBench 93.5%, Codeforces 3206), is MIT-licensed and self-hostable, and costs 2.2× less ($3.48 vs $7.50/1M). Proprietary agent powerhouse vs open-weight algorithmic specialist.
DeepSeek V4 Flash costs $0.28/1M output — that's 89× cheaper than GPT-5.5. 126.7 tok/s on Artificial Analysis. 337.3 char/s on CodingFleet. 91.6% LiveCodeBench. 79.0% SWE-bench Verified. MIT license. 1M context. The complete review of the model that makes high-volume AI coding free.
MiniMax M3 (59.0% SWE-bench Pro) vs DeepSeek V4 Pro (93.5% LiveCodeBench). M3 wins benchmarks + multimodality. DeepSeek wins price ($0.87/1M), ecosystem (2,150× more adoption), and algorithmic dominance. The generalist vs the specialist — which open-weight Chinese model fits your stack?
Can an MIT-licensed open-weight model beat OpenAI's proprietary GPT-5.4? DeepSeek V4 Pro Max does on SWE-bench — at 4.3× lower cost. Full benchmark and pricing comparison.
Head-to-head: DeepSeek V4 Pro Max vs Kimi K2.6. Both MIT-licensed, both 80%+ SWE-bench. Which open-weight coding model wins on benchmarks, price, and real-world use?