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How Decision Models Are Being Evaluated: Rival Indexes, a Pac-Man Benchmark and a Prompt-Injection Test

Reporting from TechCrunch, The Register and Artificial Analysis shows decision models judged by competing indexes, a game benchmark, vendor-run tests and a security study, with no single shared comparison.

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Background

Decision models are a category of AI model that returns structured answers instead of free text. TechCrunch reported on September 18 that TypeSafe had that week released Jev, a transformer-based model that is not a large language model (LLM) and “produces probabilities, or what the company calls ‘calibrated decisions.’” TechCrunch wrote that because users define the outputs in advance, the model cannot hallucinate, and that its output tokens are free while input tokens are metered by the billion.

Early adopters quoted in the same report described the trade-offs in terms of speed, cost and accuracy. According to TechCrunch, a Vercel software engineer said that replacing OpenAI’s Luna with Jev in a command-safety classifier produced results five to 18 times more quickly and with greater accuracy. The CTO of Bryo AI said that in a test classifying business emails, Gemini was slightly more accurate but 10 to 20 times more expensive. Armin Ronacher, CTO of Earendil, said: “it delegates the hallucination problem a little bit to the user.” TechCrunch also reported that TypeSafe’s co-founder Diogo Almeida was tight-lipped about the model’s architecture, which outside observers suspect is built on top of an open-weight LLM.

The Herald has previously reported on OpenAI’s Decisions API in beta and on its usage tiers. This analysis looks instead at how developers are being asked to compare the growing field of such models.

Current situation

A crowded field

According to The Register on October 10, the attention around Jev prompted other companies to announce decision models, and “more than 100 such models are now vying for attention.” The outlet listed entrants from OpenAI, Cloudflare, Strands, Liquid AI, Perplexity, Snowflake, Surogate and H2O.ai, as well as Microsoft’s Microsoft-Decision-1.

The Register reported that Microsoft-Decision-1 is based on Qwen3.5-9B, from the Qwen family developed by Alibaba Cloud, and that H2O.ai’s H2O-Lightning-4B is built on Qwen3.5-4B. It also reported that Microsoft, “for reasons not disclosed,” said it will soon rebase Decision-1 on its own models and those from OpenAI.

TechCrunch reported on October 22 that OpenAI’s version took ChatGPT Luna and reconfigured it to speed up inference and lower cost, rather than building a specifically calibrated decision model. Nikunj Handa, OpenAI’s product lead for APIs, told TechCrunch that the speed gain was “shrinking things from one to three seconds to 100, 120, 180 milliseconds.” According to TechCrunch, the Decisions API became available to all customers on Thursday, October 22. OpenAI’s own guide, read by the Herald on October 25, says the API returns typed answers “about 10x faster than the Responses API” and that gpt-6-luna is “the only currently supported model.”

Vendor-run numbers

Several of the figures in circulation come from the companies selling the models. The Register wrote that Microsoft claims Decision-1 is 2.5 times faster than H2O-Lightning-4B and 2.8 times faster than Jev in its latency test, leads on accuracy (83.5 percent) across 36 benchmarks, and is more than 20 times cheaper than OpenAI’s GPT-6 Sol on text classification. The Register gave Microsoft’s input price as $0.042 per million tokens with free output, as of its October 10 report; pricing may have changed since. TechCrunch’s September report attributes Jev’s speed and cost advantages to TypeSafe’s claims and to individual customers’ tests.

Public indexes and a game

TechCrunch’s October 22 report describes several comparison efforts. It says that TypeSafe CEO Diogo Almeida “distrusts benchmarks and urges people to trial the models in their own work,” yet TypeSafe released its own rankings the previous week. It adds that the AIM System One Index and the Artificial Analysis Decision Index “tend to favor D1 Ultra,” a model Liquid released the week before, and Base Lab’s Elephant-1, with the latest Jev model “in seventh and third” and OpenAI’s Decision Model “eighth and fourteenth, respectively.” TechCrunch also says TypeSafe’s offerings are the most widely queried on OpenRouter, and that the latest Jev model tops JevMan, a benchmark built by Opper AI in which decision models play PacMan. TechCrunch’s own assessment is that it is “not clear exactly what makes a good decision model, other than ‘it works for you’.”

The Artificial Analysis Decision Index page states that the index is a weighted average of AA-Omniscience-Decision, HLE-MultiChoice, When2Call and WildGuardTest, and that “All evaluations are conducted independently by Artificial Analysis.” It reports cost and median time per task, and notes that “Decision models are billed for input tokens only, and language models for input and output tokens.” Its calibration chart carries the note “Calibration for language models coming soon.” The page, read by the Herald on October 25, shows no publication or update date, and the leaderboard values were not available in the text the Herald could read, so the placements above rest on TechCrunch’s report.

A security test

The Register reported on October 15 that some of the excitement about decision models follows from TypeSafe’s claim that Jev cannot hallucinate and that “the model never makes type errors.” It then described an evaluation by the security company HiddenLayer of Jev and of Qwen3.8-27B tuned to act like a decision model. HiddenLayer’s researchers wrote: “Though System One models cannot arbitrarily output tokens, they still face many of the same risks that an autoregressive model might face.”

In the test The Register described, an open-source browser agent built on jev-ultrafast was told to buy what was in a simulated store’s cart. A sponsored ad offering a “Confirm Price” button caused the agent to click it instead. According to The Register, the injection worked on the Qwen model as well, though Jev succumbed less often. The researchers concluded: “A model that returns a decision is still a language model, and its decision still becomes an action in your systems.” The outlet said TypeSafe AI did not immediately respond to a request for comment. HiddenLayer is a security vendor reporting its own research, and the Herald has not reviewed the underlying study.

Implications

The points below describe what the cited reporting indicates; they are not predictions.

Rankings depend on the yardstick. In TechCrunch’s account, the latest Jev model sits at different places on the two public indexes, and OpenAI’s model also differs between them. Artificial Analysis lists its four component evaluations, which cover knowledge and hallucination, multiple-choice reasoning, tool calling and safety classification. A developer’s own workload may weigh these differently, which is the point TechCrunch attributes to Almeida when he urges people to trial models themselves.

Who runs the test matters. The sources contain three kinds of measurement: vendor-run figures (Microsoft’s latency and accuracy claims, TypeSafe’s rankings, OpenAI’s “10x” statement), a third-party index that states it runs its evaluations itself, and a game benchmark built by a company, Opper AI. The Register presents Microsoft’s numbers as Microsoft’s claims. Because Artificial Analysis says calibration results for language models are not yet available, one property that distinguishes decision models, the probability attached to each answer, cannot yet be compared against LLM alternatives on that index.

Underlying weights are part of the evaluation. The Register reports that at least two entrants are based on Alibaba’s Qwen models and that Microsoft plans to rebase Decision-1. TechCrunch reports that outside observers suspect Jev is built on an open-weight LLM, which the reporting does not show TypeSafe confirming. HiddenLayer’s finding, as quoted by The Register, is that these models are “under the hood, LLMs.” It follows from these reports that a benchmark result measured on one base model may not carry over to a version built on another; the sources do not say how Microsoft will version the change.

A single vendor may offer one decision model. OpenAI’s guide says its Decisions API supports only gpt-6-luna, so a developer comparing OpenAI’s offering with specialised entrants is comparing against that one general-purpose model, which TechCrunch says was reconfigured rather than purpose-trained.

Security claims and security tests address different things. The marketing claim reported by The Register (no type errors, no hallucination) concerns output format, while HiddenLayer’s test concerned whether an injected instruction could redirect the action the model selects. Both can hold at once, since a model can return a valid option that is the wrong one.

Speed and price comparisons use different baselines. Handa describes OpenAI’s gain against its own earlier responses, the Vercel engineer compared Jev with Luna, and Microsoft compared latency against Jev and H2O-Lightning-4B. These are not the same comparison, and none of the cited sources reports a single workload run across all of them.

What the sources do not establish

  • None of the cited sources publishes a head-to-head test of every model named here on one task set.
  • The Herald could not read numerical leaderboard values on the Artificial Analysis page, so TechCrunch’s rankings are not independently confirmed here.
  • The sources do not state the base model behind Jev, and the HiddenLayer study was not reviewed directly.