OpenChat
Open-source conversational model fine-tuned using C-RLFT — matches GPT-3.5 with 13B parameters.
Overview
OpenChat is an open-source large language model series developed by Wang Guo at Tsinghua University using a novel training technique called Conditioned Reinforcement Learning Fine-Tuning (C-RLFT). Trained on carefully curated conversational data, OpenChat 3.5 achieved ChatGPT-level performance on multiple benchmarks with only 7 billion parameters, making it a landmark demonstration of efficient open-source model training.
The C-RLFT technique trains the model to distinguish between expert human feedback (high quality responses) and suboptimal responses, creating a model that learns from both good and bad examples rather than only good examples. This more efficient use of training data allows smaller models to punch above their weight.
OpenChat models are particularly valued for their strong performance-to-size ratio and efficiency. The 7B model runs well on consumer hardware while providing response quality competitive with models 3-5x larger. The models are released under the Apache 2.0 licence for unrestricted commercial use, making them popular for production deployments requiring cost-efficient inference.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 8 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
ASEAN Perspective
OpenChat in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
OpenChat is an open-source line of fine-tuned models — most notably its 7B variants — that earned attention for benchmark performance rivalling much larger models, thanks to its C-RLFT training approach. For a small, self-hostable model it offers strong quality-per-parameter and a permissive footing for experimentation.
It suits developers and researchers wanting a capable lightweight model to run locally or fine-tune further. Caveats: the broader open-weights field moves fast and newer Qwen/Llama/Mistral releases have largely caught up or surpassed it, so its relative edge has narrowed; it is a model/codebase, not a hosted product, so there is no first-party API. Free, open, globally usable including for ASEAN self-hosting.
Notable facts
- OpenChat 3.5 matched GPT-3.5 performance on multiple benchmarks with only 7 billion parameters — an 8x parameter efficiency advantage.
- The C-RLFT training technique was invented in a university lab as a research project, then open-sourced — demonstrating that frontier training techniques can emerge from academic settings.
- OpenChat was one of the first models to demonstrate that learning from negative examples (suboptimal responses) is as important as learning from positive examples.
Frequently asked questions
About this listing
This entry was compiled from publicly available data including OpenChat's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with OpenChat unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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