Tutorials, deep dives and product notes — built for developers.
MiniMax M3 (59.0% SWE-bench Pro, $1.20/1M) beats GPT-5.5 (58.6%, $30/1M) on the hardest coding benchmark at 25× less cost. But GPT-5.5 dominates Terminal-Bench (+16.7), OSWorld (+8.7), GPQA and HLE. 1M context, native video, MSA architecture, open-weight vs proprietary. Full comparison.
DeepSeek V4 Flash ($0.28/1M, MIT) vs Gemini 3 Flash ($3.00/1M). Flash leads Pro (+3.0), GPQA (+6.9), MCP Atlas (+7.0). Gemini leads OSWorld (65.1%), multimodal input, and Toolathlon. 10.7× price gap. Two Flash-tier models, zero overlap.
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.
Qwen 3.7 Max beats GPT-5.5 on SWE-bench Pro (60.6% vs 58.6%) — the hardest coding benchmark. Costs 4x less. But GPT dominates Terminal-Bench, DeepSWE, and ARC-AGI-2. Full comparison.
Claude Opus 4.8 leads SWE-bench Pro by 8.6 points (69.2% vs 60.6%) — but Qwen 3.7 Max fights back on Terminal-Bench (69.7% vs 65.4%) and LiveCodeBench (91.6% vs 88.8%). With native Anthropic API compatibility and 3.33× lower cost, Qwen is the first model you can drop into Claude Code as a replacement.
Qwen 3.7 Max (60.6% SWE-bench Pro — highest proprietary score) vs MiniMax M3 (59.0%, $1.20/1M, open-weight + video). Just 1.6 points apart on Pro but 6.25× price gap. Alibaba's agent powerhouse vs the multimodal challenger.
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?