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I built <em>Jev</em>Bench because <em>Jev</em> kicks ass, and the world deserves to know how the serious open source and fake lookalike projects <i>really</i> perform in comparison.<p><em>Jev</em>-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.<p><em>Jev</em>Bench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.<p>A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.<p>Leaderboard right now:<p><pre><code>  #1 - <em>Jev</em>            74.4\n  #2 - SemIf          73.1\n  #3 - djev           73.0\n  #4 - Winnow-12B Q8  71.2\n  #5 reflex 4B        70.3.\n</code></pre>\nMIT harness, public items, frozen artifacts, scoring code and public per-task outcomes:<p><a href=\"https://github.com/fstandhartinger/jevbench\" rel=\"nofollow\">https://github.com/fstandhartinger/<em>jev</em>bench</a><p>Two no-signup demos:<p><a href=\"https://who-is-right.app.mintapis.com\" rel=\"nofollow\">https://who-is-right.app.mintapis.com</a><p><a href=\"https://is-it-ai-slop.app.mintapis.com\" rel=\"nofollow\">https://is-it-ai-slop.app.mintapis.com</a><p>Limitations: English-only; latency from one German server; local/demo latency gets a disclosed \u00d72 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.<p>Wdyt?"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["jev"],"value":"Show HN: <em>Jev</em>Bench, a reproducible benchmark for typed decision models"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["jev"],"value":"https://benchmarkheaven.com/<em>jev</em>-models"}},"_tags":["story","author_florianstandhar","story_49800574","show_hn"],"author":"florianstandhar","children":[49800575,49807791,49808495,49808592,49809071,49809676,49809896,49810168,49810509,49810808,49811050,49811269,49811399,49812306,49812398,49812631,49813649,49816723,49821653,49823304,49841547,49878842],"created_at":"2026-09-22T13:01:03Z","created_at_i":1790082063,"num_comments":39,"objectID":"49800574","points":154,"story_id":49800574,"story_text":"Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects <i>really</i> perform in comparison.<p>Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.<p>JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.<p>A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.<p>Leaderboard right now:<p><pre><code>  #1 - Jev            74.4\n  #2 - SemIf          73.1\n  #3 - djev           73.0\n  #4 - Winnow-12B Q8  71.2\n  #5 reflex 4B        70.3.\n</code></pre>\nMIT harness, public items, frozen artifacts, scoring code and public per-task outcomes:<p><a href=\"https:&#x2F;&#x2F;github.com&#x2F;fstandhartinger&#x2F;jevbench\" rel=\"nofollow\">https:&#x2F;&#x2F;github.com&#x2F;fstandhartinger&#x2F;jevbench</a><p>Two no-signup demos:<p><a href=\"https:&#x2F;&#x2F;who-is-right.app.mintapis.com\" rel=\"nofollow\">https:&#x2F;&#x2F;who-is-right.app.mintapis.com</a><p><a href=\"https:&#x2F;&#x2F;is-it-ai-slop.app.mintapis.com\" rel=\"nofollow\">https:&#x2F;&#x2F;is-it-ai-slop.app.mintapis.com</a><p>Limitations: English-only; latency from one German server; local&#x2F;demo latency gets a disclosed \u00d72 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.<p>Wdyt?","title":"Show HN: JevBench, a reproducible benchmark for typed decision models","updated_at":"2026-10-18T17:02:41Z","url":"https://benchmarkheaven.com/jev-models"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"flxflx"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["jev"],"value":"We found an approach to get <em>Jev</em>-like properties from standard LLMs like GLM-5.3-Flash.<p>The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.<p>In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against <em>Jev</em> and Laya. We find that our setup is on-par with <em>Jev</em> in terms of accuracy and speed and that it substantially outperforms Laya.<p>Still, in terms of costs per decision, <em>Jev</em> is several x better than our setup. In turn, our setup supports vision inputs."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["jev"],"value":"Turning GLM-5.3-Flash into a <em>Jev</em>-like decision model"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.privatemode.ai/blog/system-one-from-glm-flash"}},"_tags":["story","author_flxflx","story_49857656"],"author":"flxflx","children":[49857657,49857986,49862056,49862229,49862454,49862639,49862653,49862676,49862973,49863427,49863924,49864408,49866699,49866803,49867196,49874800],"created_at":"2026-09-26T15:49:04Z","created_at_i":1790437744,"num_comments":59,"objectID":"49857656","points":138,"story_id":49857656,"story_text":"We found an approach to get Jev-like properties from standard LLMs like GLM-5.3-Flash.<p>The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.<p>In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.<p>Still, in terms of costs per decision, Jev is several x better than our setup. 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