Caliber Flex 1B by OnePass. Decision-only model, exactly 1,000,000,000 parameters.
Nandakishor M's typed-decision model. A frozen Qwen3.5 backbone is read, never trained, and cut off mid-stack; a small trained physics engine settles each choice, score or yes/no question into calibrated probabilities, a conformal set and an abstain flag. 0.8B and 4B.
Decides
choice, noul, score
choice, score, noul, classify, route
Architecture
llm
vega
Fine-tuned from
qwen/qwen3.5-0.8b-base
qwen/qwen3.5-0.8b
License
caliber-flex-non-commercial-1.0
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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55.6%
Calibration error
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0.151
Valid action rate
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Median latency
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p95 latency
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Evaluation suite
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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 caliber-flex-1b and vega?
caliber-flex-1b is from onepass and vega from Nandakishor M. Both have open weights you can download and run. Both answer choice, noul and score questions. Only vega answers classify and route. caliber-flex-1b is licensed other; vega, apache-2.0.
Which is more accurate, caliber-flex-1b or vega?
Only vega publishes an accuracy figure (55.6% on His 11 October 2026 benchmark, published with its results and its harness (a notebook in vegaml): macro accuracy over 43 public tasks for the 0.8B, 0.717 for the 4B; with JevBench (231 items) and DecisionBench (1,075 rows), 3,841 decisions in all, seed 20261011. On his own set of 2,050 choice decisions over six intent workflows (esci, clinc150, appliances, banking77, when2call, apibank), one choice question each, which he has not published with its question wording, he reports 0.763 (calibration error 0.026) for the 0.8B with its public_intents adapter, 0.389 without the adapter and 0.803 for the 4B with it. He publishes no accuracy for the typed_decisions adapters); caliber-flex-1b does not, so there is no comparison to make without your own test.
Which is cheaper, caliber-flex-1b or vega?
caliber-flex-1b: Free (open weights). vega: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run caliber-flex-1b or vega locally?
Yes, both: systemone pull onepass/caliber-flex-1b and systemone pull nandakishorm/vega download the weights.
His 11 October 2026 benchmark, published with its results and its harness (a notebook in vegaml): macro accuracy over 43 public tasks for the 0.8B, 0.717 for the 4B; with JevBench (231 items) and DecisionBench (1,075 rows), 3,841 decisions in all, seed 20261011. On his own set of 2,050 choice decisions over six intent workflows (esci, clinc150, appliances, banking77, when2call, apibank), one choice question each, which he has not published with its question wording, he reports 0.763 (calibration error 0.026) for the 0.8B with its public_intents adapter, 0.389 without the adapter and 0.803 for the 4B with it. He publishes no accuracy for the typed_decisions adapters
