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Hybrid reasoning

Hybrid reasoning model with superior intelligence for high-volume use cases, and 200K context window Claude Sonnet 4 improves on Claude Sonnet 3.7 across a variety of areas, especially coding. It o

Top Rated

1
Hybrid reasoning

Hybrid reasoning model with superior intelligence for high-volume use cases, and 200K context window Claude Sonnet 4 improves on Claude Sonnet 3.7 across a variety of areas, especially coding. It o

REASON

1
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

2
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

3
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

4
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

5
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

REASONING

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Reviews

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  • kai 2025-05-23 09:26
    2.0
    Interesting:5,Helpfulness:5,Correctness:5

    Price is $3 per million input tokens $15 per million output tokens. Still a little bit expensive in performing complex tasks.

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