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Most Reviewed

1
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: # Qwen3-235B-A22B ## Qw

2
reasoning math science code

Qwen 3 is the latest large reasoning model developed by Alibaba company. It surpass multiple baselines on coding, math and surpass SOTA model performance on multiple benchmarks. It is said to be released by May, 2025. # Qwen3 Qwen Chat   |    Hugging Face | ModelScope   | Paper | Blog ｜ Documentation Demo   | WeChat (微信)   | Discord   Visit our Hugging Fac

3
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 offers frontier performance that’s practical for most AI use cases, including user-facing AI assistants and high-volume tasks. Claude Sonnet 3.7 is the first hybrid reasoning model and our most inte

4
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 frontier in coding, agentic search, and creative writing. We’ve also made it possible to run Claude Code in the background, enabling developers to assign long-running coding tasks for Opus to handle indepe

5
Coding Model

Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Claude 4 hybrid reasoning models let customers choose between near-instant responses and deeper reasoning. Claude 4 models offer improvements in coding, with Opus 4 as the “world’s best coding model

6
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

7
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

8
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

9
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

10
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: --- library_name: transformers licen

11
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

12
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

13
agent tool use

  Tech Blog     |       Paper Link (coming soon) ## 1. Model Introduction Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding t

Top Rated

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 frontier in coding, agentic search, and creative writing. We’ve also made it possible to run Claude Code in the background, enabling developers to assign long-running coding tasks for Opus to handle indepe

2
Coding Model

Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Claude 4 hybrid reasoning models let customers choose between near-instant responses and deeper reasoning. Claude 4 models offer improvements in coding, with Opus 4 as the “world’s best coding model

3
reasoning math science code

Qwen 3 is the latest large reasoning model developed by Alibaba company. It surpass multiple baselines on coding, math and surpass SOTA model performance on multiple benchmarks. It is said to be released by May, 2025. # Qwen3 Qwen Chat   |    Hugging Face | ModelScope   | Paper | Blog ｜ Documentation Demo   | WeChat (微信)   | Discord   Visit our Hugging Fac

4
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

5
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

6
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: # Qwen3-235B-A22B ## Qw

7
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 offers frontier performance that’s practical for most AI use cases, including user-facing AI assistants and high-volume tasks. Claude Sonnet 3.7 is the first hybrid reasoning model and our most inte

8
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

9
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

10
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: --- library_name: transformers licen

11
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

12
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

13
agent tool use

  Tech Blog     |       Paper Link (coming soon) ## 1. Model Introduction Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding t

AGENT

1
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: # Qwen3-235B-A22B ## Qw

2
model llm

Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: --- library_name: transformers licen

3
agent tool use

  Tech Blog     |       Paper Link (coming soon) ## 1. Model Introduction Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding t

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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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  • 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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  • kai 2025-05-23 09:11
    5.0
    Interesting:5,Helpfulness:5,Correctness:5

    Claude 4 is the most exciting model reason I am expecting in 2025 since OpenAI stop release new capable models. Its coding and AI agents capability is the most desirable features of future workflows and AI automation. Hopefully the API price will not increase too much.

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    AILearner98 2025-05-12 22:54
    5.0
    Interesting:5,Helpfulness:5,Correctness:5
    Prompt: I have a project name for example "project_a" and I want to support both python (pypi) and typescript (npm) services. Additionally, I have some front end plugin which is associated with the APIs (GET). The package support various endpoint and registry service. How can I set the package folder?

    I asked Qwen3 to help me with the coding problem, which is to create a package folder structure for both python and typescript. It should also contains a folder for plugin. Right now. Qwen3 provides the best answer to me compared to DeepSeek and many other.

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  • K
    kevinsmash 2025-05-04 08:47
    5.0
    Interesting:5,Helpfulness:5,Correctness:5

    Qwen 0.6B small size LLM is extremely powerful in realworld applications such as search and recommendation, query intent recognition, etc. And Qwen3 0.6B model is the SOTA one compared to previous counterparts such as Gemini and Llama small size LLM.

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