Free Open-Source LLMs
As of June 2026, AIDiveForge tracks 4 free open-source llms. Curated free open-source llms tracked by AIDiveForge. Each tool listed is currently free. Listings are verified against each tool's live website and re-checked regularly.
Last updated June 11, 2026 · 4 tools
1. DBRX Instruct
DBRX Instruct is a free, open-source large language model built by Databricks for instruction-following tasks in software development and enterprise applications. It uses a mixture-of-experts architecture to balance performance with efficiency, and integrates natively with Databricks' data platform—a meaningful advantage if you're already in that ecosystem. The model shows strong results on coding and reasoning benchmarks, but carries real limitations: no vision capabilities, a shorter context window than Claude or GPT-4, and less real-world adoption in mainstream enterprise settings. For teams deeply embedded in Databricks infrastructure, it's a compelling option; for everyone else, it remains a secondary choice.
FreeOpen Source
2. Llama 3
Llama 3 is a large language model family designed to handle standard NLP workloads—text generation, translation, summarization, and sentiment analysis—across a range of scales. Meta released it as open source, meaning you can download weights, fine-tune locally, or run it on your own infrastructure instead of hitting an API. The catch: while free to use, the model is young relative to Llama 2, and local deployment requires real hardware or cloud credits. For teams building production systems, this trades managed convenience for control and lower long-term marginal costs.
FreeOpen Source
3. Mistral
Mistral offers a family of large language models ranging from the lightweight Mistral 7B to the more capable Mistral Large, accessible both as open-source downloads and via paid API. The company positions itself as the cost-conscious alternative to ChatGPT and Claude, with a free tier covering basic use cases but throttled requests that frustrate serious users. Pricing for the API starts around $0.14 per million input tokens—roughly one-third OpenAI's rate—making it genuinely cheap at scale. The catch: public API documentation remains sparse, and the free tier's limitations mean you'll likely hit a paywall faster than expected.
FreeOpen Source
4. Qwen2.5 72B
Qwen2.5 72B is a free, fully open-source large language model built by Alibaba that you can run on your own hardware. It competes directly with Claude and GPT-4-class models on reasoning, code generation, and math—areas where most open alternatives historically lag—while supporting 128,000 token contexts and multiple languages. The catch is computational: you'll need serious GPU investment (roughly $200k+ in hardware) to run it at scale, and like all LLMs, it has a knowledge cutoff and may need customization for niche domains. For organizations that can afford the infrastructure, it eliminates per-API-call costs entirely.
FreeOpen Source
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