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Decider is an open-source model interface for software that needs a choice, score, or yes/no probability from a defined set of answers. Instead of generating prose for an application to parse, it takes a state and typed questions, then returns structured decisions and probability distributions in one model pass. Those probabilities can help route work or rank options, but they do not guarantee that an answer is correct.
How Decider turns a question into a decision
A conventional text-generation workflow asks a model to produce an answer, then uses code to interpret that text. Decider takes a different approach: the caller defines the valid answer space in advance. The model scores the available answer slots, and the software returns structured results rather than a free-form response.
The documented implementation places answer slots alongside the questions, runs the model once, reads logits at those slots, applies a fitted temperature, and normalizes the scores across valid options. The output can include the selected answer, its confidence, and the distribution over the available answers. This can simplify downstream software when the task is classification or routing, though it does not remove the need to test the model on the application’s own data.
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Decider’s answer types describe different shapes of decision. Select the one that matches the task and define the permitted options or levels clearly.
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| Type | What it represents | Example use |
|---|---|---|
| Choice | A selection among named options. | “Which team should handle this?” |
| Score | A rating among described levels. | “How frustrated is the customer?” |
| Noul | A yes/no probability. | “Does this need a refund action?” |
These are constrained decisions, not prompts for open-ended explanation. If an application needs a rationale in prose, it will need a separate generation step or another workflow; the primary Decider result is the decision data.
Where it can fit—and where it may not
Useful for fixed-answer software workflows
Decider is a natural fit when software needs to classify a state into known categories, assign a level, or estimate whether a condition is true. The probability distribution can support thresholding, prioritization, or escalation rules. For example, a support system could route a customer message to a team or flag a possible refund action, provided the categories and operating thresholds are defined and evaluated for that use.
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Not a substitute for multi-step reasoning or prose generation
Project author Mark Marosi says the one-pass approach does not handle multi-step arithmetic or multi-hop reasoning well; complex judgments should be decomposed into smaller questions. The same author reports that rule-following depends on patterns represented in training data, and that released models’ text-state game results are not solved zero-shot. These are author-described limitations, not independent evaluations. The author’s September 22, 2026 write-up also described English-only support at that time, so language coverage should be checked for the specific model release in use.
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Decider uses fitted temperature scaling and normalized scores to produce probability distributions, and its author reports calibration measurements. Calibration concerns how confidence behaves across predictions; it is distinct from whether a particular prediction is true. As Marosi cautions, “a model can be well calibrated and wrong.” Treat confidence as one input to a decision policy, not proof that a single answer is correct. Set thresholds using representative held-out data, monitor consequential errors, and provide a fallback or human review path where mistakes carry meaningful cost.
Reported results: read the conditions, not just the number
Published figures describe different models, tasks, and evaluation conditions. They are reported project or author results, not independent verification, and should not be collapsed into a single ranking.
| Reported result | Model and evaluation | How to interpret it |
|---|---|---|
| 0.784 and 0.810 held-out regression accuracy | Decider-4b and decider-35b-a3b, respectively; Mapika project repository, 2026. | Repository results for its stated regression task, not Decision Index scores. |
| 54.3 score; fourth place | Decider-35b-a3b on Decision Index edition 0.1; multimodalart, 2026, as reported by Mark Marosi. | The author says the index ran open reproductions on one RTX PRO 6000 and counted unanswered requests as wrong. The author did not run these third-party leaderboard results. |
| 3.1 calibration-error points; 0.4% wrong at 95%+ confidence | Decider-35b-a3b on the Decision Index calibration comparison; as reported by Mark Marosi, 2026. | Calibration comparison figures, not a guarantee about future predictions or a measure interchangeable with accuracy. |
| 18 ms and 41 ms median per decision | Decider-2b and decider-35b-a3b, respectively, on one B300 in bf16, batch size one, without CUDA graphs or compilation; Mark Marosi, 2026. | The author says the package’s default CUDA-graph engine is faster than these baseline figures. These timings are not universal and should not be compared directly with measurements from other hardware or runtime conditions. |
| 91.5% and 87.3% correct | Decider-35b text weights and purpose-trained decider-2b-vision, respectively, on 71 held-out image questions; Mark Marosi, 2026. | A small, specific image-question evaluation, not a general vision benchmark. |
The distinctions matter: leaderboard placement, task accuracy, calibration, and latency answer different questions. To decide whether a model works for your application, evaluate its errors, confidence behavior, and speed on your own representative inputs.
Choose a model and deployment route
Model size affects memory needs and runtime, while the right deployment path depends on available hardware and serving setup. The project repository gives these approximate memory figures for the specified model and precision conditions:
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| Model / serving condition | Approximate memory guidance |
|---|---|
| Decider-2b, bf16 | 4 GB |
| Decider-4b, bf16 | 8.4 GB |
| Decider-35b-a3b, bf16 | 65 GB |
| Decider-35b-a3b, NVFP4 with vLLM | 19.6 GB |
These are repository guidance figures for the named configurations, not a promise of total system memory use. The project documents CUDA inference, Apple Silicon/MPS for some dense models, GGUF files served through llama.cpp, and CPU inference. It also documents a CPU/laptop route for the 2B model, so a dedicated GPU is not mandatory. Check the repository’s current instructions for the model and runtime you intend to use.
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The documented Python package is decider-ai, imported as decider. The project also documents a system_one interface and an HTTP serving path.
- Install the package: run
pip install decider-aiin the Python environment intended for the application. - Choose the model and runtime: use the project repository’s model-loading instructions for the relevant hardware path; the repository shows weights loaded from Mapika model repositories.
- Define the state and typed questions: specify Choice, Score, or Noul and enumerate the valid options or levels before inference.
- Evaluate before routing real work: measure task errors and confidence behavior on held-out examples that reflect your production inputs, then set escalation and fallback rules.
The package is identified as open source on PyPI, and the decider-2b model card specifies Apache-2.0. Mapika describes the project as an independent reproduction of the System One model class and explicitly says it is not affiliated with or endorsed by TypeSafe AI. Decider should therefore not be represented as an official Jev or TypeSafe AI product.
Quick Recap
Sources and project details
- Mapika/decider project repository for installation, model and runtime guidance, memory estimates, and repository task results.
- decider-ai on PyPI for package details.
- Decider-2b model card for the model’s stated license.
- Mark Marosi’s technical article, published September 22, 2026 for the implementation description, author-stated limitations, and reported benchmark, calibration, and latency figures.
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