JEV COMPARISON · JEVBENCH V1.3.0 · UPDATED SEPTEMBER 22, 2026
Jev vs djev
The fastest decision API, against the best calibrated one.
Jev is built for reliable probabilities and difficult decisions. djev is built for speed and native multimodal input.
On this site
Jev 1.13.0
TypeSafe · System One model
Leads the benchmark on calibrated probabilities and hard cases.
Alternative
djev
Maisa · DiffusionGemma decision API
The fastest model on the board, and the one that can look at a picture.
The benchmark
JevBench v1.3.0, measured the same way
One benchmark ran all 52 systems under one method, so these bars are comparable with each other in a way that vendor-published figures are not.
Composite score
Higher is better
JevBench v1.3.0
The top three finish within 1.4 points of each other, and then the board falls away sharply. The composite hides where systems actually differ, so the two charts below break it apart.
Accuracy by difficulty
Where the decisions separate
Easy
72 straightforward cases
Standard
96 everyday cases
Judge
146 evaluation-style calls
HardHard
220 genuinely ambiguous cases
Easy and standard decisions separate almost nothing. The hard tier, highlighted, is where these systems stop agreeing.
What the composite leaves out
The composite also weighs a fourth axis: cost, scored from each system's public list price. Jev places last on that axis, while djev is currently free during preview.
See Jev pricingEvery system on the board
The ranking and deployment detail
The capability scores are charted above; this is the ranking and the deployment detail behind them. Jev and djev are highlighted.
JevBench v1.3.0 scored 52 systems on the same 534 decisions (72 easy, 96 standard, 146 judge, 220 hard) on September 21, 2026, one request at a time.
Feature by feature
Jev and djev side by side
What each is better at
The practical trade-off
djev is remarkably close in the aggregate. The meaningful difference is calibration, input modality, and how much infrastructure you want to operate.
Where Jev wins
- Calibration: 82.7 against 65.4.
- Hard-tier accuracy: 74.1% against 69.5%.
- A real price, rate limits, and a pinnable version.
- Focused text decisions with structured output.
Where djev wins
- Speed: 91.4 against Jev's 83.3.
- Native image input, image choices, and live camera sampling.
- Free while the preview lasts.
- Intelligence close enough that easy decisions rarely separate.
The analysis
The gap is not simply intelligence
In JevBench v1.3.0, djev scores 91.4 on speed against Jev's 83.3. Its intelligence score of 82.7 is close behind Jev's 85.7, so easy and standard decisions rarely show a dramatic difference.
The gap is calibration: 65.4 against Jev's 82.7. Maisa's own documentation describes djev probabilities as experimental and uncalibrated. That matters when code uses confidence to auto-approve, escalate, weight, or route.
The commercial difference is just as clear. djev is a free preview with announced pricing, while Jev is a hosted production API with a version you can pin and a usage model you can plan around.
Operating model
djev trades API spend for GPU ownership when you self-host; Jev is ready as a hosted API.
Input boundary
djev accepts native image and camera input. Jev stays focused on text and structured decisions.
Decision shape
Both are strongest when software needs a typed choice, score, or no/yes judgment.
From the GitHub repository
What djev actually is
djev is a focused, open implementation of typed decisions on DiffusionGemma and vLLM. DiffusionGemma refines answer positions together, then the API reads probabilities for allowed labels and returns a validated response instead of generating and reparsing prose.
The repository adds one-step structured reads, compact canvases, exact label probabilities, reusable prefixes, multimodal attention, and deliberate batching. It does not introduce new model weights.
Read djev-dev on GitHubSelf-hosted
Run the model on your own GPU or rent one when volume justifies it.
Multimodal
Evaluate native image inputs, image choices, and live camera frames.
Direct probabilities
Return typed option probabilities without a full autoregressive output loop.
Reference hardware
The project recommends one NVIDIA B200, Linux, CUDA 13, and BF16.
The djev-dev project is independent and is not affiliated with or endorsed by TypeSafe or Jev AI.
So which should you use?
Choose around your constraints
Pick Jev if
- Your code branches on the probability, not just the winning label.
- Inputs include long, messy, or genuinely ambiguous cases.
- You need forecastable cost and a version held steady across a release.
Pick djev if
- Your decision is about an image, frame, or scanned document.
- Latency is the binding constraint and you only need the winning label.
- You are comfortable building against a preview whose terms may change.
Try it on your data
Try Jev on the cases you are actually arguing about.
A scoreboard is useful for orientation. Your own awkward cases are the better test.
FAQ
Jev vs djev FAQ
Is djev open source?+
Partly. The community djev-dev repository is Apache-2.0 and shows how to run DiffusionGemma decisions on vLLM. Maisa's hosted djev service has not released its hosted implementation.
Is djev free?+
It is free during the preview. Maisa has announced $0.026 per 1,000 decisions for afterwards, but that price is not currently being billed.
Can Jev read images like djev?+
No. Jev accepts text, JSON objects, and text arrays. Convert images to text or structured fields first, or use a model that accepts pixels.
Which is faster, Jev or djev?+
djev, on the hosted APIs measured by JevBench: 0.24s median against Jev's 0.65s. Both are designed for request-time use; neither should be assumed to fit a tight unbatched loop.
Can I use djev probabilities as thresholds?+
Treat them as a ranking signal until you validate them on your own labelled data. Maisa documents the hosted preview probabilities as experimental and uncalibrated.
Figures are quoted from the public sources above as read on September 22, 2026. djev and every other product named on this page belongs to its respective owner.