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GLM-5.3-Flash as a decision model
Privatemode's write-up of typed decisions from GLM-5.3-Flash in one forward pass, with latency, cost, and accuracy set next to Jev and Laya.
- Format
- Article
- Published by
- Community
- Author
- Johannes Hötter
- Last verified
- Sep 2026
Highlights
- From Germany, Privatemode answered in 180 ms and Jev in 264 ms. From the US, Jev was 164 ms and Privatemode 299 ms.
- At list prices, one million decisions cost about EUR 62 with GLM-5.3-Flash and about EUR 16 with Jev.
- From 6 to 42 options, Jev fell from 92.1% to 85.6% and GLM-5.3-Flash from 91.2% to 79.6%.
- On CLINC150 with a second request, GLM-5.3-Flash reached 87.5% against Jev at 78.4%, in 719 ms against 249 ms.
Watch out
24 September 2026. The numbers are Privatemode's own benchmark. The post also says the setup can decide on images, which Jev's text API does not.
Questions about this entry
Short answers drawn from the linked project or page.
Not quite. The trick is that you never ask the model to write the answer: you number the options, end the prompt with the assistant turn prefilled as choice_index:, set max_tokens to 1 and temperature to 0, then read one row of logits over the whole vocabulary. No training data, no JSON generation.
Region decides the winner. From Germany, GLM-5.3-Flash answered in 180 ms against Jev's 264 ms; from the US, Jev was 164 ms and Privatemode 299 ms. On CLINC150 with a second request GLM reached 87.5% against Jev's 78.4%, but at 719 ms against 249 ms. These are Privatemode's own benchmark.
About EUR 62 per million decisions with GLM-5.3-Flash against about EUR 16 with Jev, at list prices. That is the argument for a dedicated decision model, and it also means you need a served inference endpoint rather than a plain API key.
Because it reports the distribution before the restriction is applied, so formatting tokens such as a leading space occupy the top slots and some options fall off the list and appear to have probability zero. vLLM's logprob_token_ids returns exactly the ids you ask for, though those ids depend on the model's tokenizer.
The prompt numbers the options as JSON and ends with choice_index: so the first generated token is an index. The code then reads and renormalizes the log probabilities of the option indexes at that single position, rather than reading generated text.
It uses /chat/completions with continue_final_message and add_generation_prompt: false so the model continues the prefilled assistant turn. allowed_token_ids limits the output vocabulary as a guardrail, and logprob_token_ids returns the exact option-token probabilities.
Because it runs through the chat-completions path and can pass images next to the text, the setup enables typed decisions on images, which the post says is not possible with Jev.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: Privatemode compares GLM-5.3-Flash typed decisions with Jev on latency, cost, and accuracy.
flxflx129 pointsTurning GLM-5.3-Flash into a Jev-like decision modelRead the thread on Hacker NewsLinkedIn: 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
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