DecisionTune's 395M decision model: ModernBERT-large fine-tuned with a 4 KB scoring head that scores a marker per option, answering a choice or yes/no question in one encoder pass with a probability for every option. Runs locally on CPU or GPU via PyTorch, MLX or ONNX.
A 396M ModernBERT-large cross-encoder that answers typed questions about a text or a JSON state: one or several options, scores, yes/no with "not stated", spans with evidence quotes, rankings and numeric bins, each with a confidence and an act-or-escalate signal.
Decides
choice, noul, route, classify
choice, score, noul, rank, extract
Architecture
decisiontune
solvi
Fine-tuned from
answerdotai/modernbert-large
answerdotai/modernbert-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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59.4%
Calibration error
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0.210
Valid action rate
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Median latency
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p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between decisiontune and solvi?
decisiontune is from DecisionTune and solvi from solvi. Both have open weights you can download and run. Both answer choice and noul questions. Only decisiontune answers route and classify. Only solvi answers score, rank and extract. decisiontune reads up to 8K tokens of state, against 512 tokens for solvi. decisiontune is the smaller model, at 395M parameters to 396M.
Which is more accurate, decisiontune or solvi?
Only solvi publishes an accuracy figure (59.4% on Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or solvi?
decisiontune: Free (open weights). solvi: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or solvi locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull solvi-ai/solvi download the weights.
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Evaluation suite
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Fast Decisions public dev split (100 rows x 17 domains, macro), the maker's own harness, zero-shot
