Tutorials, deep dives and product notes — built for developers.
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.
The two best open-weight coding models in the world. MiniMax M3: 59.0% SWE-bench Pro (#1 open-weight), 1M context, native video, $1.20/1M. Kimi K2.6: 58.6% Pro, Agent Swarm (300 sub-agents, 4,000 steps), HLE leader (54%), $4.00/1M. Just 0.4 points apart on Pro but 3.3× price gap. Full benchmark comparison.
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.
Gemini 3.5 Flash ($9/1M, 76.2% Terminal-Bench, 4× faster) vs DeepSeek V4 Pro ($0.87/1M, 93.5% LiveCodeBench). 10× price gap. Flash wins on agent speed — DeepSeek on algorithms and value. Which fits your workflow?
MiniMax M3 (59.0% SWE-bench Pro, $1.20/1M, native video/image input) vs Gemini 3.5 Flash ($9/1M, 76.2% Terminal-Bench, 4× faster than frontier). Open-weight multimodal vs Google speed machine. Which wins for coding?
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.