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1
Qwen3 32B
qwen-alibaba/qwen3-32b
MODEL - REASON
5.0 star - 1 Reviews
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 Layers: 64 Number of Attention Heads (GQA): 64 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-32B ## Qwen3 Highlights Qwen3 is the late

2
Qwen3 0.6B
qwen-alibaba/qwen3-0-6b
MODEL - REASON
5.0 star - 1 Reviews
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 Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-0.6B ## Qwen3 Highlights Qwen3 is the latest generation of large language models

3
DeepSeek Prover V2 671B
deepseek/deepseek-prover-v2-671b
MODEL - REASON
4.0 star - 1 Reviews
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 DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thou

4
Grok 4
grok4-xai/grok-4
MODEL - reason
5.0 star - 1 Reviews
reason model

Grok 4 is the latest released model by XAI. It surpasses multiple benchmarks and are trained using corpus from x/twitter.

5
Qwen3 14B
qwen-alibaba/qwen3-14b
MODEL - REASON
0.0 star - 0 Reviews
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 - Number of Layers: 40 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: 32,768 natively and . # Qwen3-14B ## Qwen3 Highlights Qwen3 is the latest generati

6
Qwen3 1.7B
qwen-alibaba/qwen3-1-7b
MODEL - REASON
0.0 star - 0 Reviews
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 Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768 # Qwen3-1.7B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in

7
Qwen3 8B
qwen-alibaba/qwen3-8b
MODEL - REASON
0.0 star - 0 Reviews
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 Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-8B ## Qwen3 Highlights Qwen3 is the latest

8
Qwen3 4B
qwen-alibaba/qwen3-4b
MODEL - REASON
0.0 star - 0 Reviews
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 Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. # Qwen3-4B ## Qwen3 Highlights Qwen3 is the latest gen

accuracy

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