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GLM-4.6 vs Kimi K2-Instruct-0905
Comprehensive side-by-side LLM comparison
Kimi K2-Instruct-0905 leads with 46.1% higher average benchmark score. GLM-4.6 supports multimodal inputs. GLM-4.6 is available on 2 providers. Overall, Kimi K2-Instruct-0905 is the stronger choice for coding tasks.
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Zhipu AI
GLM-4.6 is a multimodal language model developed by Zhipu AI. It achieves strong performance with an average score of 61.2% across 7 benchmarks. It excels particularly in AIME 2025 (93.9%), LiveCodeBench v6 (82.8%), GPQA (81.0%). It supports a 197K token context window for handling large documents. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Zhipu AI's latest advancement in AI technology.
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Moonshot AI
Kimi K2-Instruct-0905 is a language model developed by Moonshot AI. It achieves strong performance with an average score of 64.1% across 29 benchmarks. It excels particularly in MATH-500 (97.4%), MMLU-Redux (92.7%), IFEval (89.8%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Moonshot AI's latest advancement in AI technology.
25 days newer
Kimi K2-Instruct-0905
Moonshot AI
2025-09-05
GLM-4.6
Zhipu AI
2025-09-30
Performance Metrics
Context window and performance specifications
Average performance across 30 common benchmarks
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GLM-4.6
Average Score:14.3%
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Kimi K2-Instruct-0905
Average Score:60.4%(+46.1%)
Provider Availability & Performance
Available providers and their performance metrics
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GLM-4.6
2 providers
DeepInfra
Throughput: 85 tok/s
Latency: 0.7ms
ZeroEval
Throughput: 85 tok/s
Latency: 0.7ms
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Kimi K2-Instruct-0905
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GLM-4.6
Avg Score:14.3%
Providers:2
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Kimi K2-Instruct-0905
Avg Score:60.4%(+46.1%)
Providers:0