Question 1

How is this different from sending the image to a hosted vision model?

Accepted Answer

The output shape. A general vision model returns prose; imajev returns a probability over exactly the options you listed, plus unknown_probability and an abstained flag, which is what you need to write a conditional on. The README's own example is a photo of beige shoes checked against a record claiming red, with the listing held as a result. The tradeoff is that you enumerate the options and write the questions yourself.

Question 2

Is it genuinely better than Jev, or better than other open vision models?

Accepted Answer

Against the Jev-class boards the author quotes, imajev-4b edges ahead of Jev 1.13.0, and ahead of the 12B Jev-Omni on the image board. Those boards are maintained by others and ranks shift as systems land. On DecisionBench it is third of 56, behind the maintainers' own Bosun models, and on the reasoning split it sits behind GLM-5.3 Flash and GPT-5.6 Luna. No head-to-head against other open vision-language models is published.

Question 3

What does it take to run locally?

Accepted Answer

The README shows one POST to a local /v1/systemone endpoint with the image as a file part and the typed questions as JSON, and the live Space runs imajev-4b on a GPU. What is missing is any hardware table, quantisation guidance or memory floor. The only timing figure is about 1.15 seconds for the listing example on a Mac Studio with 224 input tokens, averaged over four option orders with calibration applied. Check the model card for real requirements.

Question 4

Can it be trusted to say it does not know?

Accepted Answer

That is the stated design intent, and abstentions are counted in the benchmark's label set rather than scored as errors, with the choice answer carrying a real unknown_probability instead of forcing an option. But the evidence is aggregate: leaderboard scores plus demo examples, one of which is a stylist request it declines to guess on. No per-domain abstention rate is published, so calibrate on your own data before gating an automated action on it.

Question 5

Which leaderboards place it, and by how much?

Accepted Answer

It reports first of 91 on JevBench v1.4.2.2 for text at 67.37 against Jev 1.13.0's 63.29, and first of 49 on Image JevBench v0.1.3 at 76.39 against the 12B Jev-Omni's 73.10. On DecisionBench for English it is third of 56 at 79.65, and third on the reasoning split at 80.58.

Question 6

What does an answer look like besides the choice?

Accepted Answer

Each answer carries a probability over the options you allowed plus a confidence, an unknown_probability, and an abstained flag. In the listing example the choice names listing.color at 0.95 with a confidence of 0.919 and an unknown_probability of 0.007, which is the shape you branch on rather than parsing prose.

Question 7

What are the model sizes and the license?

Accepted Answer

Open weights in 2B, 4B, and 9B sizes with a companion ImajevBench dataset, and the repository is Apache-2.0-licensed. The 4B is the one behind the headline leaderboard numbers and runs in the live Hugging Face demo, while the 2B appears separately on the image board.

Imajev

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