1. Overview
  2. FAQ
  3. Open Router Models and its Strengths

Open Router Models and its Strengths

Mixtral 8x7B

  • Ability to follow instructions
  • Complete requests
  • Generate creative text formats
  • Fine-tuned to be a helpful assistant
  • Sparse Mixture of Experts architecture contributes to high performance across different benchmarks

Dolphin 2.5 Mixtral 8x7B

  • Proficiency in coding
  • Uncensored nature
  • Reliable choice for tasks that require accurate and detailed language generation


  • Proficiency in reasoning, code, JSON, and chat applications
  • Large context window
  • Closed-source, flagship status


  • Supports multiple languages including English, French, Italian, German, and Spanish
  • Coding capabilities
  • Versatile choice for multilingual and technical applications


  • Cost-effectiveness
  • Suitable for applications that require efficient and budget-friendly language processing solutions

Open Hermes 2.5

  • Creative and engaging writing
  • Some inconsistencies in instruction adherence and character consistency
  • Excels in real-time interactions
  • Proficiency in a variety of language tasks

Mistral 7B Instruct

  • Processing and generating responses based on specific instructions
  • Useful in tasks that require a high level of precision and adherence to guidelines

Psyfighter v2 13B

  • Ability to provide long, detailed responses
  • Improved prose and logic capabilities
  • Designed to offer high-quality outputs, especially in story mode
  • Valuable tool for various natural language processing tasks

Code Llama 34B Instruct

  • Generate text for code synthesis and understanding
  • Particularly useful in the context of Python

Phind Code Llama 34B

  • Designed for general code synthesis and understanding
  • Specifically for Python
  • An auto-regressive language model that uses an optimized architecture

Goliath 120B

  • Excels in roleplaying
  • Best LLM for this purpose
  • Outperforms smaller models in prose, understanding, and handling complex scenarios


  • Highly regarded for its translation capabilities
  • Cross-language understanding
  • Instruction following
  • Reading between the lines
  • Handling complex scenarios
  • Humor comprehension


  • Exceptional processing speed and efficiency
  • Generates answers within seconds
  • Produces an average of 140 tokens per second

Nous Hermes 70B

  • Provide long and detailed responses
  • Lower hallucination rates
  • Absence of OpenAI censorship mechanisms in its training data

Airoboros L 2 70B

  • Particularly adept at handling detailed coding tasks with specific criteria
  • Create applications based on detailed requirements
  • Write multi-threaded servers
  • Generate optimal responses to instructions utilizing a set of provided tools

Synthia 70B

  • Known for providing correct answers to a high percentage of multiple-choice questions
  • Ability to follow instructions and acknowledge all data input

Mythalion 13B

  • Available in various quantization methods
  • Each with different trade-offs between model size and quality

Yi 34B

  • Large bilingual (English/Chinese) language model
  • Contains 34 billion parameters
  • Trained on a dataset that supports a 4K sequence length
  • Can be expanded to 32K during inference

Yi 6B

  • Might excel in applications where quick and accurate language processing is required but with limited computational resources

Noromaid 20B

  • Performance in creating lewd stories and roleplay scenarios

Llama 2-70B Instruct v2

  • Performance has been compared to other LLMs, such as Llama2-70B
  • Achieves competitive results

Llama 2-13B

  • Strength lies in its ability to handle a high number of requests per second
  • Minimize latency
  • Provide cost-effective solutions for NLP tasks

Google Palm 2 & Google Palm 2 32k

  • Model architecture and objective have also been updated to achieve overall better performance
  • Faster inference
  • Fewer parameters to serve
  • Lower serving cost

Mistral Open Orca 7B

  • Performance has been compared to other LLMs, such as Llama2-70B
  • Achieves competitive results

Neural Chat 7B

  • Ability to conduct natural, flowing conversations
  • Suitable for chatbots and virtual assistant applications


  • Strength lies in its experimental nature
  • Active benchmarking process
  • Tailored to specific user goals


  • Fine-tuned with C-RLFT
  • Achieved the highest average performance among all 13B open models on three standard benchmarks

Zephyr 7B

  • Excels in performance and efficiency
  • Scores closely correlate with human ratings of model outputs
  • Outperforms larger models on benchmarks like MT-Bench and AlpacaEval

Nous Capybara 34B

  • Key strengths include fast performance
  • Suitable for real-time applications and large-scale language processing tasks

RWKV v.5

  • Ability to handle a wide range of languages
  • Performance comparable to transformer models
  • Efficient use of resources such as VRAM during both training and inference

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