Tutorials, deep dives and product notes — built for developers.
GPT-5.5 and Kimi K2.6 are tied at 58.6% SWE-bench Pro. But Kimi leads HLE w/tools (54.0%), DeepSearchQA (+13.9), and Agent Swarm (300 sub-agents). GPT counters with OSWorld (+1.9), BrowseComp, Terminal-Bench (Codex CLI 82.7%), and 7.5× higher cost. The most evenly matched comparison of 2026.
Claude Opus 4.8 (69.2% Pro, $25/1M, AA Index #1) vs MiniMax M3 (59.0%, $1.20/1M, open-weight + video). Opus dominates 5 of 6 shared benchmarks by 8-13 points. But M3 is 21× cheaper, open-weight, and wins BrowseComp (-4.2). Full comparison with VP of VentureBeat research plus MiniMax/Minimax blog data.
Qwen 3.7 Max (60.6% SWE-bench Pro, $7.50/1M, Anthropic API compatible) vs Kimi K2.6 (58.6%, $4.00/1M, 300 sub-agent swarms). Qwen leads all 6 shared benchmarks — but Kimi counters with open-weight, BrowseComp Agent Swarm (86.3%), and HLE w/tools (54%). Full comparison with real benchmark data.
MiniMax M3 (59.0% Pro, $1.20/1M, 1M ctx) vs GLM 5.1 (58.4%, $4.40/1M, 200K ctx). Both Huawei Ascend, both MIT, both Chinese. 0.6 pts apart on Pro. M3 leads context + multimodal. GLM leads reasoning + CyberGym #1 + pure MIT + $3/mo plan. Full comparison.
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, 284B) vs Qwen 3.6 Flash ($0.90/1M, Apache 2.0, 35B/3B). V4 leads every coding benchmark (Pro +3.1, HLE +13.4, LiveCodeBench +11.2). Qwen counters with multimodal (text+image+video), speed (90-172 tok/s), and tiny 3B active params. Chinese Flash showdown.
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.
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?