Our newest decision model is the world's best decision model. Introducing GLiDE v1.2, the #1# model on the @huggingface Decision Index. With an overall score of 71.5, GLiDE v1.2 also has one of the lowest calibration errors on the entire Decision Index, meaning its confidence more closely reflects how often its answers are correct. Beyond improved benchmark performance, it also adds support for multimodal inputs, enabling decision-making using text, JSON, and images. We are excited to release the model weights on Hugging Face. For hosted inference, you can also run GLiDE v1.2 through the Fastino API at $0.04 / million input tokens. Use promo code GLIDEIS1 for $10 in API credits. Model weights: API: Blog:

A lot of new demos for decision models have been circulating over the last two weeks. Decision models have encouraged people to experiment with use cases beyond just parsing text, which inspired our team to build a few demos of our own. See what our team has been able to create with GLiNER:

Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning structured decisions with probability distributions and confidence scores. We evaluated the model’s performance on Fast Decisions, an unseen, internally generated classification suite based on 17 datasets testing real-world use cases across routing, triage, classification, sentiment, and content understanding. Measured against similar decision models, GLiNER2.5-Decide leads in 9 of the 17 datasets, achieving the highest average score: - GLiNER2.5-Decide: 60.1% - SemIf: 56.4% - JevK5: 57.5% - Laya: 46.6% This performance makes the model a strong fit for use cases like tool calling, model routing, browser and computer use, and LLM-as-a-judge. GLiNER2.5-Decide’s lightweight encoder architecture makes it easy to fine-tune the model for specific tasks, while being efficient enough to run locally on consumer-grade CPUs or in air-gapped environments, giving users greater control over where their data goes and where the model runs. To make building and experimenting with GLiNER2.5-Decide as easy as possible, we're also offering hosted inference. You can now use our API to run inference and fine-tune GLiNER models on specialized tasks right inside your own coding agent: As with previous models, we’re also releasing the model weights on @huggingface under the Apache 2.0 license:

GLiNER2.5 attaches structured judgments to individual spans. A structured judgment is a span attribute: a label from a group you declare, such as sentiment, scored on the mention it describes. In one extract call, the model: - Extracts companies, products, people, or other types you name - Scores that group's labels on each retained span the group applies to - Returns the judgment on that mention, with the span text and offsets One article can praise one company and criticize another. Each company span keeps its own sentiment, tied to the text that describes it, so mixed coverage stays specific to the mention. Watch the full conversation with @urchadeDS and @george_onx to learn more:

Today we’re launching GLiNER2.5, the most significant upgrade to the GLiNER model architecture to date. Across a suite of 16 benchmarks spanning diverse classification and extraction tasks, GLiNER2.5 achieves higher overall average F1 scores in comparison to GLiNER2, including a significant 24.75-point gain on XNLI. The gains come from a new architecture that predicts entity boundaries directly rather than enumerating every possible span. That change allows inference to scale linearly with document length and removes the max-entity-width cap for longer span length. Still built for entity extraction and classification, GLiNER2.5 adds five new capabilities: • Long-Context Extraction - full documents in a single call, so no information gets missed • Unlimited Span Length - entities of any length, including addresses, clauses, and titles are captured whole instead of cut off • Joint Information Extraction - entities and relations are decoded together for construction of unified, coherent knowledge graphs • Constrained Classification - labels stay logically consistent across tasks to avoid contradictions • Span Attributes - extracted spans come back with additional context in the same pass GLiNER2.5 comes in three variants: • GLiNER2.5-small (73.9M parameters) • GLiNER2.5-base (0.2B parameters) • GLiNER2.5-multi (0.3B parameters) Every day this week, we're releasing an interactive demo for each of the five new capabilities. Model weights are live on @huggingface under Apache 2.0: Read the full release blog: