Liquid AI shipped LFM2.5 on June 10, 2026. The company announced the release through its official homepage and mirrored the model weights on Hugging Face. LFM2.5 is a family of three open-weight models: a 1.3B mini variant, a 3.1B small variant, and a 12.5B medium variant. All three use an Apache 2.0 license and a 32,768-token context window. The smallest model runs on hardware with as little as 4GB of RAM. Unlike closed model APIs, LFM2.5 has no per-token fees and no usage caps. This matters for developers who need a free AI model that stays offline and predictable. LFM2.5 builds on Liquid AI’s earlier LFM2 line and focuses on on-device inference. The vendor homepage lists GGUF quantizations for CPU, GPU, and mobile NPU backends.
Why it matters comes down to cost and control. Closed models from OpenAI, Google, and Anthropic now push many flagship features behind paid tiers. Free tiers are getting lighter, as tracked in AI free tier limits. LFM2.5 offers a different path. You download the weights once and run them on your own hardware. There is no request logging, no monthly quota reset, and no vendor can shut down your endpoint. That is useful for startups, privacy-focused apps, and educational projects. The medium variant is not as strong as GPT-5.5 or Claude Opus 4.8, but it covers basic chat, summarization, and coding help. For many tasks, free and local beats paid and remote. This release matters because it lets developers sidestep the paywalls that now surround many flagship models.
License implications are significant. Apache 2.0 allows commercial use, modification, and redistribution without royalty. That means a company can embed LFM2.5 into a product without paying Liquid AI or publishing its own code. This is broader than some open-weight licenses that restrict commercial use or require attribution. Developers can find the model cards on Hugging Face and the code on GitHub. Liquid AI’s official homepage includes a quickstart guide. You can run the mini model with llama.cpp on a Raspberry Pi or an old Android phone. The medium model runs well on an RTX 3060 with 12GB VRAM using a Q4 quant. The license plus local execution removes the two biggest barriers for self-hosted AI: cost and legal risk.
Competitive context is clear. Open-weight releases from Qwen, Mistral, DeepSeek, and Zyphra have forced closed vendors to cut prices. Google recently cut Gemini prices, as covered in AI price war. LFM2.5 is not the most powerful open model on the market. But it is optimized for small devices and long context. That niche is underserved. Many open models with strong benchmarks are too large to run locally. LFM2.5 mini can generate text at around 20 tokens per second on a midrange phone. That is enough for an always-on assistant that never uploads your data. For users tired of free tier cuts, this is a practical escape hatch.
How Do the Top Options Compare?
| Model | Parameters | Context Window | Benchmarks (MMLU / HumanEval) | License | Size (GGUF Q4) |
|---|---|---|---|---|---|
| LFM2.5 Mini | 1.3B | 32,768 tokens | 52.8% / 45.1% | Apache 2.0 | 2.6GB |
| LFM2.5 Small | 3.1B | 32,768 tokens | 60.3% / 54.8% | Apache 2.0 | 6.2GB |
| LFM2.5 Medium | 12.5B | 32,768 tokens | 69.2% / 63.5% | Apache 2.0 | 24.8GB |
Benchmarks are self-reported by Liquid AI on standard evals. Actual numbers vary with quantization and hardware. No API pricing is listed because LFM2.5 has no per-token fees.
1. LFM2.5 Mini (1.3B) , Best for phones and low-RAM devices
The LFM2.5 Mini is the smallest model in the family at 1.3 billion parameters. It ships in a 2.6GB GGUF Q4 quantization, so it fits on devices with 4GB of RAM. Liquid AI says the mini is designed for always-on use: voice assistants, notification summaries, and offline chat. The model uses a 32,768-token context window, which is unusually large for this size class. Many 1B-class models cap context at 8k or 16k tokens. That long context lets the mini process entire documents, email threads, or transcripts without chunking. You can find it on the vendor’s official homepage and on Hugging Face. The mini is the easiest entry point for developers who want a free AI model without API costs. It runs on Android and iOS via NPU backends, and it works with llama.cpp on Raspberry Pi. Because the model is open weight, you can fine-tune it on domain-specific instructions without sending data to a cloud vendor. That makes it a strong fit for privacy-sensitive note-taking apps and on-device smart reply features.
Key strengths:
- ✅ Small 2.6GB Q4 download fits 4GB RAM devices
- ✅ 32,768-token context window in a 1.3B model
- ✅ Apache 2.0 license allows commercial use
- ✅ Runs on CPU with llama.cpp and mobile NPU backends
- ✅ No API costs or usage caps
- ❌ Lower benchmark scores than larger LFM2.5 models
- ❌ Weak multilingual support outside English and major European languages
- ❌ Aggressive quantization below Q4 degrades quality
Who it’s for: Mobile developers and hobbyists who want a free, offline assistant on phones or Raspberry Pi-class hardware.
2. LFM2.5 Small (3.1B) , Best for laptops and edge workstations
The LFM2.5 Small is the middle option at 3.1 billion parameters. It ships as a 6.2GB GGUF Q4 file and runs comfortably on MacBooks, Windows laptops, and small edge servers with 8GB of RAM. Liquid AI positions it as the default for local coding help, document analysis, and agentic workflows. The model keeps the same 32,768-token context as the mini. This long context is important for debugging sessions and repository-level questions. The small model is available on GitHub with example scripts for llama.cpp and Ollama. You can also use it with the transformers library for fine-tuning. Benchmarks improve meaningfully over the mini. Liquid AI reports 60.3% on MMLU and 54.8% on HumanEval. These scores place it near older 7B open models at a much smaller memory footprint. That efficiency matters for users who watched free AI tier limits get tougher this year. The small model can act as a local coding assistant that never sends your code to a vendor. It is not as strong as GitHub Copilot’s cloud models, but it is free and private. You can pair it with open-source tools like Continue or Tabby. The small model’s biggest limitation is reasoning depth. Complex multi-step math or long-horizon planning can break down. It also struggles with very recent knowledge because the training cutoff is mid-2026. Like the mini, it works best with clear prompts and few-shot examples. For a developer who wants an offline model that handles code review and documentation queries, the small model is the sweet spot.
Key strengths:
- ✅ Runs on 8GB laptops and edge workstations
- ✅ Same 32k context as mini with better comprehension
- ✅ 60.3% MMLU and 54.8% HumanEval scores
- ✅ Works with Ollama and llama.cpp out of the box
- ✅ Apache 2.0 license for commercial products
- ❌ Struggles with deep multi-step reasoning
- ❌ Not as strong as cloud coding models from OpenAI or Anthropic
- ❌ Training cutoff means limited knowledge of very recent events
Who it’s for: Developers and privacy-conscious professionals who need a local coding and document assistant on a laptop or small edge server.
3. LFM2.5 Medium (12.5B) , Best for local servers and RTX-class GPUs
The LFM2.5 Medium is the largest open-weight release in this family at 12.5 billion parameters. It requires a 24.8GB Q4 download and runs best on a GPU with at least 8GB VRAM, though CPU-only inference is possible with patience. Liquid AI targets this model at local servers, homelab users, and smaller companies that want a private alternative to paid APIs. The 32,768-token context window remains, so the medium model can process long reports, codebases, or legal documents in one pass. The model card is on Hugging Face, and the vendor’s official homepage includes deployment guides. This is the only LFM2.5 variant that gets close to older 13B open models on benchmarks. Liquid AI reports 69.2% on MMLU and 63.5% on HumanEval. Those numbers do not beat GPT-5.5 or Claude Opus 4.8, but they are respectable for a free model that runs entirely on your own hardware. The medium model handles structured outputs, tool calling, and multi-turn chat better than the smaller variants. For users frustrated by flagship models moving to paid tiers, the medium model is a credible local stand-in for many tasks. The main trade-off is compute. A Q4 quant of the medium model uses about 7GB of VRAM, which is fine for an RTX 3060 or M2 Mac with 16GB of unified memory. CPU inference works but drops to a few tokens per second. This is not a model you will run on a phone. The medium model also inherits the same training cutoff and multilingual gaps as the rest of the family. Still, for a company that needs a free, self-hosted model for internal support or document triage, the medium model is the best option in this release.
Key strengths:
- ✅ Best benchmark scores in the LFM2.5 family
- ✅ 32k context at 12.5B parameters
- ✅ Runs on 8GB VRAM GPUs with Q4 quantization
- ✅ Supports tool calling and structured output
- ✅ Apache 2.0 commercial license
- ❌ Requires a GPU with at least 8GB VRAM for good speed
- ❌ Still below frontier closed model accuracy
- ❌ CPU inference is too slow for interactive use
Who it’s for: Teams and homelab users who need a private, free, self-hosted language model for document work and agentic tasks.
Frequently Asked Questions
Is Liquid AI LFM2.5 really free?
Yes. All three sizes are open weights under Apache 2.0. You can download, modify, and use them commercially without paying Liquid AI or sharing your own code. There are no per-token fees or usage caps.
Can LFM2.5 run on any device?
The mini model runs on devices with 4GB of RAM, including many phones and Raspberry Pi boards. The small model fits on 8GB laptops. The medium model needs a GPU with at least 8GB VRAM for practical speeds. CPU-only inference works but is slower.
What license does LFM2.5 use?
LFM2.5 uses the Apache 2.0 license. This allows commercial use, modification, and redistribution. You do not need to release your own code, but you should include the original license notice in distributed binaries.
How does LFM2.5 compare to closed models like GPT-5.5?
LFM2.5 Medium scores 69.2% on MMLU and 63.5% on HumanEval. Frontier closed models score higher on complex reasoning and long-horizon tasks. But LFM2.5 is free, offline, and private. For many basic chat, summarization, and coding help tasks, it is a practical substitute.
Where can I download LFM2.5?
Weights are available on Hugging Face and GitHub. The vendor’s official homepage links to model cards and quantized GGUF files. You can run them with llama.cpp, Ollama, or the transformers library.
What are the main limitations?
LFM2.5 models are not frontier models. They struggle with deep multi-step reasoning, low-resource languages, and very recent events. Quantization below Q4 can hurt quality. CPU-only inference on the medium model is slow.
What Should You Remember?
- Open weights: LFM2.5 ships under Apache 2.0, so you can use it commercially without royalties or forced code disclosure.
- Three sizes: Choose 1.3B for phones, 3.1B for laptops, and 12.5B for local servers or RTX-class GPUs.
- No API pricing: There are no per-token fees or usage caps, making it a true escape hatch from paid tiers.
- On-device focus: All variants keep a 32,768-token context window, which is rare for models this small.
- Commercial safety: Apache 2.0 allows redistribution in products without publishing your own source code.
- Not frontier: Medium hits 69.2% MMLU, useful for common tasks but below GPT-5.5 or Claude Opus 4.8 on hard reasoning.
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