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Claude Opus 4.8 (69.2% Pro, $25/1M) dominates every benchmark vs Kimi K2.6 (58.6%, $4/1M) by 3-11 pts. But Kimi fights back on BrowseComp (-3.9), Agent Swarm (300 sub-agents), DeepSearchQA (92.5%), and is 6.25× cheaper. Full comparison with real benchmark data, 10-point verdict.
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.
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.
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.
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.
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?