Qwen 3.1 (235B-2507 Tested) + Free APIs + Cline,Roo: BYE Kimi-K2? IS IT Really BETTER than KIMI-K2?

Key Concepts:

• Quen 3235B: A large language model by Quen.
• Hybrid Thinking Model: A model that can toggle a "thinking layer" for reasoning.
• Instruct Model: A model specifically trained for instruction following.
• Benchmarks: Standardized tests used to evaluate the performance of language models.
• Open Router: A platform for accessing various language models via a single API.
• Rue, Klein, Kilo: Code editors and IDE extensions for using language models.
• Context Length: The amount of text a model can process at once.
• Tool Calling: The ability of a language model to use external tools or APIs.
• Fine-tuning: Customizing a pre-trained model with specific data.

Quen 3235B New Iteration

• Model Details: The updated Quen 3235B model has 22 billion active parameters.
• Hybrid Thinking Discontinuation: Quen has moved away from the hybrid thinking model approach. They will now train instruct and reasoning models separately.
• General Instruct Model Release: Quen has released a general instruct model (non-reasoning) that outperforms previous versions.
• Performance Claims: Quen claims it beats Kimmy K2 in benchmarks, but the speaker disputes this, citing Quen's history of training on benchmark questions.
• Reported Improvements: Supposed improvements include instruction following, logical reasoning, text comprehension, math, science, coding, tool usage, long-tail knowledge coverage, user preference alignment, and 256K long context understanding.

Benchmarks and Their Reliability

• Quen's Benchmark Reputation: Quen is known for training its models on benchmark questions to achieve higher scores.
• ADER Leaderboard Example: The Quen 2.5 model ranked close to 3.5 Sonnet on the ADER leaderboard, an inaccurate reflection of real-world performance.
• Real-World Usage vs. Benchmarks: The speaker finds Kimmy K2 to be superior to the previous Quen 3235B, despite the latter's higher ranking in benchmarks.
• Model Ranking: The speaker's ranking of open models is Kimmy K2 > DeepSseek > Quen > others.
• Personal Testing Emphasis: Benchmarks are unreliable; users should test models themselves.

Setting Up and Using Quen 3235B

• Quen Chat Platform: Free platform for basic chat usage and simple tasks, offering deep research and webdev capabilities.
• Open Router Pricing: Available on Open Router for $0.15 per 1K input tokens and $0.85 per 1K output tokens.
• Cost Comparison: Cheaper than Kimmy and Gemini 2.5 Flash.
• VS Code Integration: Can be used with code editors like Rue, Klein, and Kilo.
• Setup Instructions:
  • Upgrade VS Code extensions.
  • Create a new profile in Klein or Rue.
  • Choose the Open Router option.
  • Select the Quen 3235B model.
  • Use the free variant by shoots via Open Router (if desired).
• Kilo Code Integration: Kilo Code provides $20 in free credits, allowing extended testing of Quen due to its low cost. Select the Kilo code provider in settings and choose the Quen 3 model.

Performance Comparison and Observations

• Kimmy K2 Superiority: The speaker believes Kimmy K2 is better overall, especially in tool calling.
• Quen's Fail Rates: The Quen model has higher fail rates, and edits may be in unexpected formats.
• Price-Performance Ratio: Quen is considered excellent for its price, open weights, and smaller size compared to Kimmy.
• Local Hosting Potential: Theoretically fine-tunable and potentially hostable locally with some investment.
• Future Models: Anticipation for a Quen 3 coder model and hope for the open-sourcing of the Quen Max model.
• Groq Integration: Interest in seeing how quickly Groq will add the Quen model.

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Conclusion:

The new iteration of Quen 3235B presents an affordable and potentially useful option for various tasks, particularly given its open weights and smaller size. However, users should be wary of benchmark scores due to Quen's history of training on benchmark-specific questions. Real-world testing, particularly compared to models like Kimmy K2, is essential to determine its suitability for specific use cases. The availability of the model on platforms like Open Router and the integration with code editors like Rue, Klein and Kilo make it accessible for experimentation and practical application.