Tutorials, deep dives and product notes — built for developers.
The definitive SWE-bench Pro leaderboard. 31 AI models ranked by real GitHub issue resolution. Claude Fable 5 leads at 80.3%. Includes model size, license, pricing, and source links. Updated June 9, 2026.
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.
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.
What SWE-bench Pro actually measures, how it works (1,865 tasks, 41 repos, 123 languages), why OpenAI abandoned SWE-bench Verified, the DeepSWE audit that found 32% verifier errors, and how to use coding benchmarks correctly. The definitive explainer.
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?
Both $15/1M output. GPT-5.4 is faster (242.5 char/s vs 173.3 on CodingFleet) and stronger on benchmarks (SWE-bench Pro +14, Terminal-Bench +16). Sonnet 4.6 counters with 90% cache discounts, no long-context surcharge, and mature Claude Code ecosystem. The real verdict: use both.