Source: Ollama
OpenChat is an open-source conversational model that achieved ChatGPT-competitive performance using a novel training method called C-RLFT — without relying on human preference labels or expensive RLHF pipelines. It delivers fast, accurate, and naturally fluid responses on consumer-grade hardware.
Developed by researchers at Tsinghua University, OpenChat introduced Conditioned Reinforcement Learning Fine-Tuning (C-RLFT) — a technique that treats mixed-quality instruction data as a conditional optimization problem, allowing the model to learn selectively from high-quality interactions without needing an explicit reward model.
The result was remarkable: OpenChat 3.5 (7B parameters) scored above ChatGPT (gpt-3.5-turbo) on multiple benchmarks including MT-Bench and AlpacaEval, despite being a fraction of the size. OpenChat 3.6 extended this with Mistral 7B and Llama 3 bases, further improving reasoning and code generation.
For organizations that need a private, ChatGPT-equivalent conversational interface — for customer support, internal Q&A, or developer tooling — OpenChat delivers compelling quality at minimal infrastructure cost.
OpenChat 3.5 outperformed gpt-3.5-turbo on MT-Bench at the time of its release — achieving this with 7B parameters via C-RLFT training rather than brute-force scale. Ideal for teams replacing cloud chat APIs.
At 7B parameters with Q4 quantization, OpenChat runs at interactive speeds on CPU-only servers and achieves real-time response on a single consumer GPU — making it one of the most accessible private chat deployments available.
OpenChat performs well on HumanEval and coding benchmarks, supporting Python, JavaScript, SQL, and more — suitable for internal developer assistants, code review tools, and automated documentation generation.
C-RLFT training produces responses that feel natural and coherent across multi-turn exchanges, without the robotic or overly-cautious tone common in heavily RLHF-aligned models. Excellent for customer-facing applications.
Handles system prompt customization well — allowing operators to define persona, tone, and domain focus. Useful for building branded AI assistants with specific behavioral guidelines baked in at the system level.
Fully permissive commercial use. OpenChat can be integrated into proprietary products, SaaS platforms, and enterprise systems without royalty payments or usage restrictions.
| Version | Base Model | Parameters | Context | Notable Features |
|---|---|---|---|---|
| OpenChat 3.5 | Mistral 7B | 7B | 8K | Landmark release; beats GPT-3.5 on MT-Bench |
| OpenChat 3.6 | Mistral 7B | 7B | 8K | Improved reasoning, updated training data |
| OpenChat 3.6 20B | Llama 3 / Yi | 20B | 8K–32K | Higher accuracy, complex reasoning tasks |
OpenChat is designed to run efficiently on minimal hardware — a key advantage for private deployment.
| Model | Quantization | RAM / VRAM | Lightsail Plan | On-Premise |
|---|---|---|---|---|
| OpenChat 3.5 / 3.6 (7B) | Q4_K_M | 4–5 GB | 4 GB Plan | Intel Core / Xeon, 16GB RAM |
| OpenChat 3.5 / 3.6 (7B) | Q8 / FP16 | 7–8 GB | 8 GB Plan | RTX 3060 12GB / any mid-range GPU |
| OpenChat 3.6 20B | Q4_K_M | 12–14 GB | 16 GB Plan / GPU | RTX 3090 / A4000 16GB |
OpenChat is a strong choice if you want a genuinely conversational local AI that feels natural and responsive. Its C-RLFT training gives it excellent instruction-following ability, so it handles detailed, nuanced prompts well without drifting off-topic. It runs efficiently on modest hardware, making it practical for everyday writing, brainstorming, summarization, and general chat. While it no longer leads in raw reasoning or coding benchmarks, it remains one of the most pleasant and capable conversational models in the 7B class.
Teams that need a reliable, self-hosted assistant for internal Q&A, document drafting, meeting summaries, or lightweight customer-facing chat will find OpenChat practical and easy to work with. It produces coherent, on-topic responses with relatively little prompt engineering. However, for complex multi-step reasoning, structured data extraction, or production-grade code generation, newer families like Qwen 2.5, Llama 3.3, or DeepSeek V3 generally deliver stronger results. OpenChat earns its place where conversational fluency and fast, lightweight deployment matter most.
Developers building conversational interfaces, chatbots, or dialogue-driven tools will appreciate OpenChat's predictable behavior and strong instruction adherence. It integrates cleanly with Ollama and responds well to structured prompts. It is less ideal for heavy coding assistance or complex algorithmic work, where more recent models have pulled ahead. Its small footprint makes it especially useful for prototyping and resource-constrained environments.
Researchers studying conversational alignment, instruction following, or the effects of C-RLFT (Conditioned Reinforcement Learning from Fine-Tuning) will find OpenChat historically significant and practically useful as a baseline. It provides a clean reference point for comparing dialogue quality and instruction adherence. For research requiring state-of-the-art reasoning or coding performance, newer model families have largely superseded it. It remains valuable in resource-constrained research settings or studies focused specifically on conversational behavior.
Natural conversational fluency makes OpenChat ideal for front-line chat support — handling FAQs, ticket triage, and escalation routing without any data leaving your environment.
Deploy OpenChat as a private coding assistant — running locally or on a shared team server — for code review, documentation, debugging help, and onboarding acceleration.
Internal knowledge base Q&A, IT helpdesk automation, and policy lookup — powered by a private LLM that never sends employee queries to external servers.
| Model Size | Ollama Tag | Recommended NoCloudGPT Tier | Notes | |
|---|---|---|---|---|
| OpenChat 7B | openchat |
Starter | Best balance of speed and quality. Runs well on 8GB RAM or consumer GPUs. | Get quote → |
| OpenChat 7B (Q4) | openchat:7b-v3.5-1210-q4_0 |
Starter | Quantized for lower memory use. Good for personal or lightweight deployments. | Get quote → |
| OpenChat 7B (Q8) | openchat:7b-v3.5-q8_0 |
Standard | Higher fidelity with modest extra VRAM cost. Recommended when hardware allows. | Get quote → |
Note: OpenChat is a single-size family centered on 7B. It has been surpassed in raw reasoning and coding performance by newer models such as Llama 3.3, Qwen 2.5, and DeepSeek V3, but remains one of the strongest conversational models of its generation.
OpenChat models are released under the Apache 2.0 License, which is one of the most permissive open-source licenses available. This allows commercial use, modification, and redistribution without royalty fees (subject to standard Apache 2.0 attribution requirements).
The underlying base model (Mistral 7B) is also Apache 2.0 licensed, so the full stack is commercially clean with no conflicting upstream restrictions.
Official model card:
Disclaimer: NoCloudGPT provides this licensing summary as a convenience reference only. It does not constitute legal advice. License terms can change. Always review the official model card and consult your legal counsel before deploying any model in a commercial or regulated context.
OpenChat 3.5 or 3.6 (7B Q4) on a $20–$40/month Lightsail instance via Ollama. Full REST API endpoint, private chat UI optional. Operational within hours.
Start Pilot →OpenChat 3.6 20B deployed on your own GPU server for higher accuracy. We handle installation, API setup, and integration with your existing customer support or developer tooling stack.
Request Install →Multi-instance deployments, high-availability configurations, or custom UI integration. We scope and deliver end-to-end private chat infrastructure.
Get a Quote →OpenChat delivers ChatGPT-competitive conversation on a $20/month server. Start your private chat pilot today — no ongoing API costs, no data sharing, no lock-in.