Fastino LabsDecision modelOpen source
GLiNER2.5-Decide-1B
PopularityRank 378 of 875
34out of 100-4 vs. last month
Summary
GLiNER2.5-Decide-1B is the larger English classifier in the GLiNER2 decision family. You set labels for intent, routing, sentiment, or priority at call time. It returns single-label or multi-label results and ordinal scores in one pass, without generating free tokens or reasoning text.
The checkpoint has approximately one billion parameters and uses fastino/gliner2-xl-0111 as its base. Apache-2.0 weights run locally on CPU or GPU through gliner2.
All benchmark results
Every published result with its source, test setup and comparison models.
Show all 39 published benchmark results
Page 1 of 4
Measurements without matching peer values
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
fastino/fast-decisions: exact-match average accuracy (Show measurement, test conditions, and source)
- Source value
- 59.6
- Score
- 59.6
- Metric
- exact-match average accuracy
- Unit
- %
- Category
- fastino/fast-decisions
- Direction
- Higher is better
- Sample
- 5100
- Source type
- vendor-reported
- Evaluator
- Fastino GLiNER2.5-Decide-1B model card
- Status
- active
- Retrieved at
- 2026-10-04
- Methodology
- Vendor-reported result. Language=English; Domains=17; Held-out examples per domain=300; Same texts and candidate labels=yes; Sample size=5100
MASSIVE 1.1 test, scenario classification, de, prompt mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 41.1
- Score
- 41.1
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, scenario classification, de, prompt mode, macro-F1 (Show measurement, test conditions, and source)
- Source value
- 41.1
- Score
- 41.1
- Metric
- Macro-F1: mean F1 over labels seen in gold or prediction.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, scenario classification, de, intext mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 40.2
- Score
- 40.2
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. intext mode: instruction is appended to the passage as Question: .... The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, scenario classification, de, intext mode, macro-F1 (Show measurement, test conditions, and source)
- Source value
- 39.7
- Score
- 39.7
- Metric
- Macro-F1: mean F1 over labels seen in gold or prediction.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. intext mode: instruction is appended to the passage as Question: .... The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification (gold-scenario oracle), de, prompt mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 61.6
- Score
- 61.6
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification (gold-scenario oracle), de, prompt mode, macro-F1 (Show measurement, test conditions, and source)
- Source value
- 53.2
- Score
- 53.2
- Metric
- Macro-F1: mean F1 over labels seen in gold or prediction.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification (gold-scenario oracle), de, intext mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 56.8
- Score
- 56.8
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. intext mode: instruction is appended to the passage as Question: .... The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification (gold-scenario oracle), de, intext mode, macro-F1 (Show measurement, test conditions, and source)
- Source value
- 46.3
- Score
- 46.3
- Metric
- Macro-F1: mean F1 over labels seen in gold or prediction.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2974
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. intext mode: instruction is appended to the passage as Question: .... The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification, 2+ options, de, prompt mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 56.4
- Score
- 56.4
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test, intents restricted to 2+ choices; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2622
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification, 2+ options, de, prompt mode, macro-F1 (Show measurement, test conditions, and source)
- Source value
- 50.7
- Score
- 50.7
- Metric
- Macro-F1: mean F1 over labels seen in gold or prediction.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test, intents restricted to 2+ choices; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2622
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. prompt mode: instruction is in the task prompt. The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
MASSIVE 1.1 test, intent classification, 2+ options, de, intext mode, accuracy (Show measurement, test conditions, and source)
- Source value
- 51
- Score
- 51
- Metric
- Accuracy: share of answers equal to gold; unanswered rows count as wrong.
- Unit
- %
- Benchmark version
- BKS-Lab lab-bench route1, run 1; MASSIVE 1.1 test, intents restricted to 2+ choices; written 2026-10-09
- Direction
- Higher is better
- Sample
- 2622
- Source type
- independent-evaluator
- Evaluator
- BKS-Lab lab-bench route1 score table
- Status
- active
- Retrieved at
- 2026-10-24
- Methodology
- Independent evaluation. Exact arm gliner2.5-decide-1b, model path fastino/GLiNER2.5-Decide-1B (English-only Decide 1B); gliner2 2.0.0 and PyTorch 2.11.0+cu128 on the local RTX 4090 workstation. One single-label task per question; the state is the named utterance section; label and description are the readable option text and the predicted label maps back to its option key. intext mode: instruction is appended to the passage as Question: .... The English-only model's German MASSIVE rows are explicitly labelled an extra, not a fair German result.
- Context
- One full run only; the report publishes no confidence interval or repeated-run uncertainty. The method says each GLiNER row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score page does not expose its value. The public test data were not checked for training exposure; Fastino's own Decide training suite may include MASSIVE. Fast-Decisions is Fastino-built and described as the classification suite behind GLiNER2.5-Decide, so it is a familiar/possibly training-adjacent cohort, not a neutral generalization test.
Page 1 of 4
Popularity compared
How often the model is searched for, used and mentioned, compared with every model of its category.
Popularity index
0 to 100, higher is more popular
20 of 37 models
AutoTrust JEV-27B-VL, AutoTrust AI: 70
AutoTrust JEV-27B, AutoTrust AI: 63
GLiNER2.5-Decide, Fastino Labs: 56
Decider 2B, Mapika: 52
Clef, Cloudflare: 51
Clef-Flash, Cloudflare: 51
Laya Multilingual, Convai Innovations: 46
Kev 0.8B, Jared Palmer: 45
GLiNER2.5-multi-Decide, Fastino Labs: 45
Decider 4B, Mapika: 43
Jev, TypeSafe AI: 43
pplx-decider-v1.1-27b, Perplexity: 42
Intern-Decision-4B, InternLM: 40
Laya Typed Decisions, Convai Innovations: 40
Kev 9B, Jared Palmer: 40
pplx-decider-v1-27b, Perplexity: 39
Decider 0.8B, Mapika: 39
Julia 1, Supersonic Labs: 39
Intern-Decision-0.8B, InternLM: 38
GLiNER2.5-Decide-1B, Fastino Labs: 34
As of November 2026 · Source: gradually.ai popularity index
Over time
Weekly values from October 18, 2026 to November 5, 2026
GLiNER2.5-Decide-1BDecider 35B A3BNanoJevJeff 1Decider 2B Vision
All specifications
Architecture
- Model class
- English specialist classifier for operational decisions, not a general-purpose model
- Source: Model class
Input and output
Watermarking and provenance
AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.
- AI watermarking
- Unknown
- no source
- Watermarking technique
- Not known
- no source
Licence and access
Further details
- Output schema
- Single-label or multi-label classification and ordinal scores without generated tokens
- Source: Output schema
Sources and data date
Show all 13 sources
Benchmark sources at a glance
Which independent evaluators tested this model. This is coverage, not a quality ranking.
No active result from the reviewed external evaluators can currently be attributed to this model. Other source evidence remains available below.
Without vendor claims and historical or disputed results; those stay labelled in the single values.
| Type | Evidence and data date |
|---|---|
| Research status | Research date October 4, 2026. 8 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication. |
| Not published | per-domain-heldout-scores Note The pinned public card and dataset report an overall exact-match average but no per-domain held-out score table. |
| Not found in the inspected sources | hanno-decisionbench-exact-checkpoint-result Note The complete results tree at pinned commit a512f9ec09abac2c4ebd2c2208c92ae339a3a056 contains no exact DecisionBench run for this checkpoint. Results for GLiNER2.5-Decide, GLiNER2.5-base, GLiNER2.5-multi-base, or another sibling are not transferred. |
| Unresolved | Per-run immutable GLiNER snapshot revision Note The route1 method says each GLiNER raw row stores the Hugging Face snapshot commit SHA, but the reviewed aggregate score table does not expose the SHA value. The exact run-specific weight revision therefore remains unknown. |
| Not published | Confidence intervals and repeated-run uncertainty Note The route1 score report is run 1 of each arm and does not publish confidence intervals or a repeated-run uncertainty estimate. None was calculated or inferred. |
| Unresolved | Identity of the selected GLiNER variant in pv1 Note The public report is summarized as selecting the best of nine variants, but the retained final excerpt only shows the displayed model label and metric cells. It does not re-expose run-date, hardware, runtime, task-setup, or the selected variant's exact prompt/configuration. Retain those setup details as previously reported but not reverified by this excerpt; the winning variant configuration remains unknown. |
| Not found in the inspected sources | Exact model result in the public Decision Index 0.4.1 panel Note This index edition has no row that can be mapped to the profile repository/checkpoint exactly. This does not imply the model lacks results in other benchmarks. |
| Additional source | Fastino model index (retrieved October 3, 2026) |
| Additional source | GLiNER2.5-Decide-1B model card (retrieved October 3, 2026; October 4, 2026) · GLiNER2.5-Decide-1B model card · Editorial description reviewed October 3, 2026 |
| Additional source | BKS-Lab lab-bench route1 score table (retrieved October 24, 2026) |
| Additional source | BKS-Lab decision-model test bench report (retrieved October 24, 2026) |
| Additional source | Model metadata (retrieved October 16, 2026)12 |
| Additional source | Model metadata (retrieved October 24, 2026) |
| Additional source | OpenRouter: Popularity index (derived), CC BY 4.0 |
| Additional source | Vercel AI Gateway: Popularity index (derived), CC BY 4.0 |
| Additional source | Ollama: cumulative downloads for the popularity index |
| Additional source | Replicate: cumulative runs for the popularity index |
| Additional source | Google Trending Now: limited feed selection for Germany and the US |
| Additional source | Creative Commons Attribution 4.0 |