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Standard One 8B SH
Standard Thinking's 8B decision model with a joint schema head that reads the scenario once and scores every option of every question; v4 self-scores 57.40 on the Decision Index 0.3 public index, up from v3's 48.34, with a 70.6M-parameter head.
- Reading time
- 3 min
- Published by
- Community
- Author
- Standard Thinking
- Last verified
- Oct 2026
Used inJev for Software & DevTools
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- Open sourceSelf-hostedRuns locally
Highlights
- Decision Index 0.3 public index 57.40, self-scored with the official kit, up from v3's 48.34; Tools area 84.0 and Arts 42.5.
- The float32 head is 70.6M parameters, reads the final layer and decoder layer 25, and answers choice (2-255 options), noul and score questions.
- JevBench public hard split 75.68 against v3's 74.77, while the original split slipped to 94.44 from 95.83; long-document set 88.00.
- Backbone: Standard One 8B v2.2 (mistral3, Ministral 3 8B text plus Pixtral vision tower); Apache-2.0; FP8 and GGUF builds are separate repositories.
- Contexts up to 32,768 tokens; the chart section claims the same decisions in 21 languages, and eval runs for every benchmark are linked.
Watch out
Card reviewed 2026-10-17; the Decision Index rows are self-scored with the official kit rather than leaderboard submissions, the card notes public train splits or generators of some benchmarks (HoVer among them) fed training, and the 21-language chart is not quantified in the fetched text.
Questions about this entry
Short answers drawn from the linked project or page.
Standard One 8B with a joint schema head: it reads the scenario once, scores every option of every question in a single forward pass, and returns calibrated probabilities through the same POST /v1/systemone contract as Standard One. It does not generate text.
A letter readout limits a question to the label alphabet and needs one pass per question. The schema head attends over the hidden states of the whole request, places every question and option in one joint graph, and returns a calibrated distribution per question at once.
Choice with 2 to 255 options, noul yes/no and score questions on an ordinal scale, over contexts up to 32,768 tokens per request. The prompt format is the chat template with no default system message.
Decision Index 0.3 public index 57.40 against 48.34 for v3, area scores of 36.9 knowledge, 57.9 language, 66.4 retrieval, 84.0 tools and 42.5 arts, and 88.00% on the long-document set. The index rows are self-scored with the official kit.
The card states the Decision Index rows come from its own runs of the public 0.3 suite, and that for some benchmarks the public train split or generator was part of training (HoVer among them), though evaluated items never were. No third-party reproduction is linked.
Standard One 8B v2.2 (mistral3: Ministral 3 8B text plus a Pixtral vision tower), fine-tuned for the head and merged into this repository under Apache-2.0. FP8 and GGUF builds are separate repositories.
Same schema-head design on a larger backbone: the 8B reports a 57.40 public index against the 3B's 51.53, with a 70.6M-parameter head reading layer 25 instead of the 3B head's layer 19.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
X: Standard One SH (StandardThinking)
LinkedIn: What multimodal decision models actually change, and what they do not.
LinkedInJevDirectory.org
Hacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Pointer to a DiffusionGemma plus vLLM tutorial for a Jev-shaped classifier.
1 pointHow to build a Jev-style classifier with DiffusionGemma and vLLMRead the thread on Hacker NewsMore Jev resources like this
Standard Thinking's 3B decision model with a joint schema head: it scores every option of every question in a scenario in one forward pass through POST /v1/systemone, and v3 self-scores 51.53 on the Decision Index 0.3 public index, up from v2's 42.90.
Jev Models & Reimplementations#community#open-models#local
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A LoRA plus 24-slot decision head over Qwen3.8-27B from Ming Xu's train-jev project: it serves TypeSafe's System One API with 0.851 Kev transfer-v4 accuracy and ECE 0.053, and needs an 80 GB GPU.
Jev Models & Reimplementations#community#open-models#local
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A 24 GB-GPU alternative from the train-jev project: Qwen3.5-4B plus a rank-16 LoRA and 24-slot decision head scoring 0.647 PK accuracy, 40.4 Decision Index and 43 ms p50 latency, ahead of Kev-4B at the same size.
Jev Models & Reimplementations#community#open-models#local
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Singularity Compute's fine-tune of Google's diffusiongemma-26B-A4B-it, a discrete-diffusion mixture-of-experts, served from an 18 GB NVFP4 checkpoint that answers choice, noul and score questions with calibrated distributions on one 48 GB GPU.
Jev Models & Reimplementations#community#open-models#local
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Mapika's NVFP4 fine-tune of Gemma-4-31B-it answers typed Choice, Noul and Score questions with calibrated probabilities and no generated text, self-reporting a 64.27 chance-corrected public Decision Index and 39 ms median latency on a B300.
Jev Models & Reimplementations#community#open-models#local
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Standard Thinking's 8B LoRA adapter for the Standard One decision model: merge it with the supplied recipe and serve it, and it scores choice, noul and score labels over a scenario with calibrated probabilities via POST /v1/systemone, never generating text.
Jev Models & Reimplementations#community#open-models#local
Community
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