Tutorials, deep dives and product notes — built for developers.
Claude Opus 4.8 (69.2% SWE-bench Pro, $25/1M) vs DeepSeek V4 Pro (55.4%, $0.87/1M). The coding king leads by 13.8 points — but DeepSeek wins LiveCodeBench (93.5%) and Terminal-Bench. Is the 28.7× premium worth it?
GPT-5.5 costs $30/1M output. DeepSeek V4 Pro costs $0.87. That's 34× cheaper — but the SWE-bench Pro gap is just 3.2 points (58.6% vs 55.4%). On LiveCodeBench, DeepSeek leads at 93.5%. When does GPT-5.5 justify its premium? Full data-driven coding comparison.
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?
32B active params vs 10B. $4.00/1M output vs $1.20. 58.6% SWE-bench Pro vs 56.22%. Kimi K2.6 wins on raw performance — but MiniMax M2.7 is the efficiency miracle: 94% of Kimi's coding score at 70% less cost, with only a fraction of the parameters. This is the battle between brute force and architectural genius.
0.2 points apart on SWE-bench Pro. Both open-weight. Both released in April 2026. But the similarities end there. Kimi K2.6 leads on coding (+11.1), agentic tasks (+7.8), and vision. GLM-5.1 counters with pure MIT license, Code Arena #3, and Claude Code compatibility. Here's the definitive comparison.
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.
DeepSeek V4 Pro Max ($0.87/1M, MIT, 1.6T/49B) vs GLM 5.1 ($3.08/1M, MIT, 754B/40B). GLM leads SWE-bench Pro (58.4% vs 55.4%) & HLE w/tools. V4 Pro Max dominates 12/14 benchmarks. 3.5× price gap, 5× context gap. Updated June 9, 2026.
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?