Maintainer: openchat

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The openchat-3.5-1210 model is a 7B parameter AI model developed by the openchat team. It is the "Overall Best Performing Open Source 7B Model" according to the maintainers, outperforming ChatGPT (March) and Grok-1 on several benchmarks. The model is capable of both coding and general language tasks, with a 15-point improvement in Coding over the previous OpenChat-3.5 model.

The openchat-3.5-0106 and openchat_3.5 are similar high-performing open-source models from the same team, with the openchat_3.5-awq and openchat-3.5-1210-gguf variants also available. All these models leverage the team's C-RLFT (Constrained Reinforcement Learning from Trajectories) fine-tuning approach to achieve exceptional results from limited training data.

Model inputs and outputs


  • Text prompts: The model can take in text prompts from users, which can include instructions, questions, or open-ended requests.
  • Conversation history: The model is designed to maintain context across multiple turns of a conversation, allowing users to build upon previous exchanges.
  • Conditional inputs: The model supports setting a "condition" (e.g. "Code", "Math Correct") to adjust its behavior for specialized tasks.


  • Generated text: The primary output of the model is coherent, contextually relevant text generated in response to the input prompts.
  • Code generation: The model can generate code snippets when provided with appropriate programming prompts.
  • Numeric outputs: The model can perform basic mathematical reasoning and provide numeric outputs for problems.


The openchat-3.5-1210 model has demonstrated strong performance across a variety of benchmarks, including MT-Bench, HumanEval, and GSM8K. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on several tasks, showcasing its capabilities in areas like coding, mathematical reasoning, and general language understanding.

The model also supports specialized "Coding" and "Mathematical Reasoning" modes, which can be accessed by providing the appropriate conditional input. These modes allow the model to focus on more technical tasks and further enhance its capabilities in those domains.

What can I use it for?

The openchat-3.5-1210 model can be a valuable tool for a wide range of applications, from chatbots and virtual assistants to content generation and code development. Its strong performance on benchmarks suggests it could be useful for tasks like:

  • Chatbots and virtual assistants: The model's ability to maintain conversation context and generate coherent responses makes it suitable for building interactive chatbots and virtual assistants.
  • Content generation: The model can be used to generate creative writing, articles, and other types of text content.
  • Code development: The model's coding capabilities can be leveraged to assist with tasks like code generation, explanation, and debugging.
  • Educational applications: The model's mathematical reasoning abilities could be employed in educational tools and tutoring systems.

Things to try

One interesting aspect of the openchat-3.5-1210 model is its ability to adjust its behavior based on the provided "condition" input. For example, you could try prompting the model with a simple math problem and observe how it responds in the "Mathematical Reasoning" mode, compared to its more general language understanding capabilities.

Additionally, the model's strong performance on coding tasks suggests it could be a valuable tool for developers. You could try providing the model with various coding challenges or prompts and see how it handles them, exploring its capabilities in areas like algorithm design, syntax generation, and code explanation.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models




Total Score


openchat-3.5-0106 is an innovative open-source language model developed by openchat. This model is the overall best performing open-source 7B model, outperforming ChatGPT (March) and Grok-1 with a 15-point improvement in coding over the previous OpenChat-3.5 version. The model has two primary modes - a generalist mode and a mode focused on mathematical reasoning, with experimental support for evaluator and feedback capabilities. Compared to similar models like openchat_3.5, openchat_3.5-awq, and openchat-3.5-1210-gguf, openchat-3.5-0106 offers improved performance across a range of benchmarks, including a 15-point boost in coding tasks. Model inputs and outputs Inputs Text**: The model accepts text inputs, which can be prompts, questions, or any other natural language text. Outputs Text**: The model generates text outputs, which can be responses, answers, or any other natural language text. Capabilities openchat-3.5-0106 demonstrates strong performance on a variety of tasks, including coding, mathematical reasoning, question answering, and more. The model's two distinct modes allow it to excel in both generalist and specialized applications. In the generalist mode, the model can assist with a wide range of tasks such as text generation, summarization, and question answering. In the mathematical reasoning mode, the model shines in solving complex mathematical problems and explaining step-by-step solutions. What can I use it for? The openchat-3.5-0106 model can be used in a variety of applications, such as: Chatbots and virtual assistants**: The model's strong natural language understanding and generation capabilities make it well-suited for building conversational AI systems. Content generation**: The model can be used to generate high-quality text, such as articles, stories, or creative writing. Question answering and knowledge retrieval**: The model can be used to answer a wide range of questions and retrieve relevant information from its training data. Coding and programming assistance**: The model's specialized mathematical reasoning mode can help with tasks like generating code snippets, explaining coding concepts, and solving algorithmic problems. Things to try One interesting aspect of openchat-3.5-0106 is its experimental support for evaluator and feedback capabilities. Users can try providing the model with feedback on its responses and observe how it adapts and improves over time. This feature could be particularly useful for building more personalized and responsive conversational AI systems. Another interesting thing to try is using the model's mathematical reasoning mode to tackle complex problems that require step-by-step explanations. The model's ability to provide detailed solutions and walk through the reasoning process can be a valuable tool for educational or research-oriented applications.

Read more

Updated Invalid Date




Total Score


openchat-3.6-8b-20240522 is the latest open-source language model released by the openchat team. It builds upon their previous 7B model, openchat-3.5-0106, which demonstrated comparable performance to ChatGPT on a variety of benchmarks. The new 8B model further improves on the previous version, outperforming Llama-3-8B-Instruct and other open-source finetuned models across key metrics. Model Inputs and Outputs Inputs Text**: The model accepts natural language text as input, which can include prompts, questions, or conversational messages. Context Length**: The model supports up to 8192 tokens of context, allowing it to engage in more extended interactions. Outputs Text Generation**: Given an input text, the model can generate coherent and contextually relevant output text. This can include responses to prompts, answers to questions, or continuations of conversations. Numerical Outputs**: In addition to text generation, the model can also handle tasks that require numerical outputs, such as mathematical reasoning and problem-solving. Capabilities The openchat-3.6-8b-20240522 model demonstrates strong performance across a wide range of natural language tasks. It excels at general conversation, coding assistance, and mathematical reasoning, often outperforming more parameter-intensive models like Llama-3-8B-Instruct. For example, the model can engage in thoughtful and nuanced dialogue, drawing upon its broad knowledge base to provide insightful responses. It also shows impressive capabilities in writing code, debugging, and explaining programming concepts. Additionally, the model can tackle complex mathematical problems, step-by-step, and provide accurate numerical solutions. What Can I Use It For? The openchat-3.6-8b-20240522 model can be a valuable tool for a variety of applications, from conversational AI assistants to educational and scientific applications. Some potential use cases include: Chatbots and Virtual Assistants**: Integrate the model into conversational interfaces to provide natural and helpful responses to user queries. Code Generation and Debugging**: Utilize the model's coding capabilities to assist developers in writing, understanding, and troubleshooting code. Educational Applications**: Leverage the model's ability to explain concepts and solve problems to create interactive learning experiences. Research and Scientific Computing**: Explore the model's potential in areas like mathematical modeling, data analysis, and scientific communication. Things to Try One interesting aspect of the openchat-3.6-8b-20240522 model is its ability to adapt its language style and tone to the given context. For example, you can prompt the model to take on different personas, such as a helpful assistant, a witty conversationalist, or an authoritative expert, and observe how it adjusts its responses accordingly. Another intriguing area to explore is the model's potential for open-ended reasoning and creative problem-solving. Try posing it with complex, multi-step challenges or open-ended prompts and see how it approaches and tackles these tasks. Overall, the openchat-3.6-8b-20240522 model represents a significant step forward in the development of high-performing, open-source language models. Its versatility and strong performance make it an exciting tool for a wide range of applications and research endeavors.

Read more

Updated Invalid Date




Total Score


The openchat_3.5 model is an open-source language model developed by openchat. It is part of the OpenChat library, which aims to create high-performance, commercially viable, open-source large language models. The openchat_3.5 model is fine-tuned using a strategy called C-RLFT, which allows it to learn from mixed-quality data without preference labels. This model is capable of achieving performance on par with ChatGPT, even with a 7 billion parameter size, as demonstrated by its strong performance on the MT-bench benchmark. Similar models include the openchat_3.5-awq model and the openchat-3.5-1210-gguf model, both of which are also part of the OpenChat library and aim to push the boundaries of open-source language models. Model inputs and outputs The openchat_3.5 model is a text-to-text transformer model, capable of generating human-like text in response to input prompts. It takes natural language text as input and produces natural language text as output. Inputs Natural language text prompts Outputs Generated natural language text responses Capabilities The openchat_3.5 model is capable of a wide range of text generation tasks, including answering questions, summarizing information, and engaging in open-ended conversations. It has demonstrated strong performance on benchmark tasks, outperforming larger 70 billion parameter models in some cases. What can I use it for? The openchat_3.5 model can be used for a variety of applications, such as building chatbots, virtual assistants, and content generation tools. Its open-source nature and strong performance make it an attractive option for developers and researchers looking to leverage advanced language models in their projects. Additionally, the OpenChat team is committed to making their models commercially viable, which could open up opportunities for monetization and enterprise-level deployments. Things to try One interesting aspect of the openchat_3.5 model is its ability to learn from mixed-quality data without preference labels, thanks to the C-RLFT fine-tuning strategy. Developers could explore how this approach affects the model's performance and biases compared to more traditional fine-tuning methods. Additionally, the model's small size (7 billion parameters) compared to its strong performance could make it an attractive option for deployment on resource-constrained devices or in scenarios where model size is a concern.

Read more

Updated Invalid Date




Total Score


The openchat-3.5-0106-gemma model is the highest performing 7B Gemma variant in the world. It was trained by openchat using their C-RLFT approach on the openchat-3.5-0106 dataset. This model achieves similar performance to the Mistral-based OpenChat model, and significantly outperforms the base Gemma-7B and Gemma-7B-it models. Model inputs and outputs Inputs Text prompts and instructions for the model to generate responses to Outputs Coherent, fluent text outputs generated by the model in response to the input prompts The model can produce a wide variety of text outputs including answers to questions, dialogue, summaries, code, and more Capabilities The openchat-3.5-0106-gemma model demonstrates strong performance across a range of benchmarks, including machine translation, code generation, mathematical reasoning, and open-ended language tasks. It outperforms previous open-source large language models like OpenChat-3.5 and ChatGPT (March) on many metrics. The model's robust training on a diverse dataset allows it to handle a variety of use cases effectively. What can I use it for? The openchat-3.5-0106-gemma model can be used for a wide range of text generation tasks. Some potential use cases include: Powering chatbots and conversational AI systems Generating creative content like stories, poems, and scripts Summarizing long-form text like research papers or reports Assisting with coding and software development by generating code snippets Providing informative responses to open-ended questions As an open-source model, openchat-3.5-0106-gemma democratizes access to state-of-the-art language AI capabilities that can be deployed on consumer hardware. Developers and researchers can leverage this model to build innovative applications and explore the boundaries of large language models. Things to try One interesting aspect of the openchat-3.5-0106-gemma model is its strong performance on coding and mathematical reasoning tasks, outperforming previous open-source models. Developers could experiment with using the model to generate code snippets, solve programming challenges, or provide explanations for mathematical concepts. Additionally, the model's robust training on diverse data sources means it may be able to handle specialized domains and tasks better than more narrowly-focused language models. Researchers could explore using openchat-3.5-0106-gemma as a foundation for further fine-tuning or prompt engineering to tackle domain-specific problems.

Read more

Updated Invalid Date