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Claude Opus 5 vs Kimi K3: Opus 5 leads the independent BenchLM aggregate 85.88 to 79.98 and posts a 43.3–43.5% Frontier-Bench score that K3 has never even been tested on.
Kimi K3 vs GPT-5.6 Sol: Sol leads 6 of 9 shared benchmarks including DeepSWE and Terminal-Bench 2.1. K3 wins FrontierSWE, BrowseComp, and AA-Briefcase at 40% lower cost. Sol Ultra hits 91.9% on Terminal-Bench. Full comparison with radar charts and pricing.
Kimi K3 vs Claude Fable 5 across 35 benchmarks: Fable wins 22, K3 wins 12, 1 tie. K3 leads Terminal-Bench 2.1, SWE Marathon (+7), BrowseComp, and took #1 on the Frontend Code Arena — all at 70% less cost. Fable dominates FrontierSWE (+5.4), HLE (+9.8), and vision. Full scorecard with radar charts and pricing analysis.
Kimi K3 vs Claude Opus 4.8: K3 leads all 9 shared coding benchmarks and costs 40% less. Opus 4.8 counters with independently verified scores, adjustable reasoning, and mature production tooling. Full comparison with radar charts and pricing tables.
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?