CUDOS

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Singularity Compute, which runs on <a href="https://x.com/SingularityNET" rel="noopener noreferrer nofollow">@SingularityNET</a>'s tech, has released Super Decider, a model that anyone can download and use for free under an open license. Instead of writing text, it picks from options a developer sets, such as which team should handle a support ticket, and gives each option a probability in about 0.3 seconds.
The model is live on ASI:Cloud (<a href="https://x.com/CUDOS_" rel="noopener noreferrer nofollow">@CUDOS_</a>). In the company's own testing, it scored 85.6 on NVIDIA's When2Call benchmark, ahead of TypeSafe's Jev and Cloudflare's Clef, but 1.6 points behind Clef on a tool-selection test. The figures come from the developer and have not been independently verified.
<h2 id="how-super-decider-works">How Super Decider works</h2>
According to the project's <a href="https://github.com/Singularity-Compute/super-decider" rel="noopener noreferrer nofollow">GitHub repository</a>, Super Decider answers typed questions about a state, covering choices, yes/no questions and scores, each with a calibrated probability distribution returned in a single call. The repository describes it as a fine-tune of Google's DiffusionGemma 26B-A4B, a discrete-diffusion mixture-of-experts model with 4 billion active parameters. It reads every answer slot in one denoising step and runs on a single NVIDIA L40S GPU.
The same page says the API follows the Jev / TypeSafe "System One" request format and is also compatible with OpenAI-style chat completions. The code is released under the Apache-2.0 licence. The developers state that no benchmark test items were used for training.
For developers, the appeal is a structured output rather than free-form text. A classifier-style model that reports its own confidence can be used to route support tickets, triage requests or gate automated actions, with low-confidence cases passed to a human.
<h2 id="what-the-benchmarks-measure">What the benchmarks measure</h2>
When2Call is an NVIDIA benchmark. Its <a href="https://aclanthology.org/2025.naacl-long.174/" rel="noopener noreferrer nofollow">peer-reviewed paper</a> says it evaluates tool-calling decision-making: when to generate a tool call, when to ask follow-up questions and when to admit a question cannot be answered with the tools provided. That focus on the decision, rather than the accuracy of the call itself, is why it suits a model built to choose between options.
The result sits within a broader push by the ASI Alliance into AI infrastructure. <a href="https://www.singularitycompute.com/" rel="noopener noreferrer nofollow">Singularity Compute</a> describes itself as part of SingularityNET and the ASI Alliance, and delivers inference through the ASI:Cloud platform. The alliance links Fetch.ai, SingularityNET and CUDOS under a shared token, according to <a href="https://crypto.com/en/university/what-is-the-artificial-superintelligence-alliance" rel="noopener noreferrer nofollow">Crypto.com Academy</a>.
Because the benchmark results are self-reported and the comparison with Clef on tool selection is narrowly negative, developers may want to test the model on their own workloads before adopting it.
Sources
<a href="https://github.com/Singularity-Compute/super-decider" rel="noopener noreferrer nofollow">Super Decider repository, Singularity Compute (GitHub)</a> <a href="https://aclanthology.org/2025.naacl-long.174/" rel="noopener noreferrer nofollow">When2Call: When (not) to Call Tools, ACL Anthology</a> <a href="https://crypto.com/en/university/what-is-the-artificial-superintelligence-alliance" rel="noopener noreferrer nofollow">What Is the Artificial Superintelligence Alliance?, Crypto.com</a>

ASI Alliance’s new open AI picks an answer and says how sure it is

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