Li
LiquidAI: LFM2.5-Embedding-350M
Free allowance
Up to50requests / day
via OpenRouter · < $10 credits
- 20 requests / minute
LFM2.5-Embedding-350M is a text embedding model from Liquid AI. It produces 1,024-dimensional embeddings for retrieval and semantic search. Successful OpenRouter requests and embeddings may be retained and used to train...
- Input
- text
- Output
- embeddings
Providers
Available through 1 free provider. Use the model ID and API settings below to connect.
* The provider match may differ from this model. Check its docs for details.
Benchmarks
Compare scores and explore results by category.
No benchmark results are available for this model yet.
Frequently asked questions
Free access, setup, and what to expect.
LFM2.5-Embedding-350M is available with up to 50 requests / day free through OpenRouter (< $10 credits). Additional limits apply at the same time: 20 requests / minute. Free allowances can change. Check the LFM2.5-Embedding-350M provider limits before you start.
To use LFM2.5-Embedding-350M, choose one of the listed LFM2.5-Embedding-350M API providers and follow the provider’s account and API-key setup guide. Copy the LFM2.5-Embedding-350M model ID and connection settings for your chosen provider into your app or API client. Model IDs can differ between providers. API requests go directly to the provider; Free Models is a comparison site.
LFM2.5-Embedding-350M accepts text as listed input formats. Listed output formats include embeddings. Image or audio input support does not imply image or audio generation. Check the chosen provider’s LFM2.5-Embedding-350M documentation for supported file types and request formats.
LFM2.5-Embedding-350M has a listed context window of 512 tokens. Tokens are small pieces of content rather than a word count. Instructions, conversation history, and other inputs use context space, and the response also needs room. The maximum output length for LFM2.5-Embedding-350M may be smaller than the context window and can vary by provider.
Neither reasoning nor tool calling support is listed for LFM2.5-Embedding-350M. Reasoning enables additional processing for complex problems. Tool calling allows a model to request functions that your app executes. Check the chosen provider’s documentation for capability availability and setup.
No benchmark results are listed for LFM2.5-Embedding-350M yet. Missing scores don’t establish poor performance. Compare scores within the same benchmark: coding indexes, Elo ratings, and win rates measure different things. The index comparison charts include only scored free models listed on Free Models. Evaluate LFM2.5-Embedding-350M on examples from your own task alongside context size and provider limits.