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reasoning

Claude Opus 4 is the Hybrid reasoning model that pushes the frontier for coding and AI agents, featuring a 200K context window Claude Opus 4 is our most intelligent model to date, pushing the front

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1
reasoning

Claude Opus 4 is the Hybrid reasoning model that pushes the frontier for coding and AI agents, featuring a 200K context window Claude Opus 4 is our most intelligent model to date, pushing the front

REASON

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model reason

Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of

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reasoning math proof

DeepSeek-Prover-V2 is an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by Deep

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model reason

Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of

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model reason

Qwen3 14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Nu

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model reason

Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of La

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model reason

Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Lay

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model reason

Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of L

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  • kai 2025-05-23 09:25
    5.0
    Interesting:5,Helpfulness:5,Correctness:5

    Claude Opus 4 claims that Claude Sonnet 4 achieves strong performance across SWE-bench for coding, TAU-bench for agentic tool use, and more across traditional and agentic benchmarks. It's astonishing what's the performance compared to OpenAI O4 and other models?

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