The live road HD · 840 × 960
PAUSEDStart a live run, or take one decision with the road paused.
What speeds up? What does the model see?
1×, 2×, 4×, 8× and 16× change road speed between decisions. API latency is real and is never divided by the speed setting. Only one request is in flight at a time.
Image mode sends a PNG. Structured scene mode sends obstacle lanes and coordinates as JSON, with no image. Jev latest and Jev 1.14 accept both input modes; the older Jev 1.13 option remains a JSON baseline. Compare models using the same input mode to compare their decision latency. A text result is not a vision accuracy result. Both modes include the selected one, two or three questions. The display stays HD; the API image can be compact or full resolution. Image mode sends no obstacle coordinates. Neither mode supplies the chosen answer label. The chosen lane becomes a left / stay / right steering command in game code. One answer controls the entire lane change. The default takes two looks: when the barrier appears and before it reaches the car. Teaching tour adds a middle look; Fast pass takes one. These distance checkpoints arrive sooner at higher speeds. The countdown is computed locally, excludes API waits, and is never sent to the model.
Classroom mode freezes both the road and game clock during every API request. It resumes from the same position when the answer arrives. Real-time mode keeps moving during requests and skips rechecks that cannot finish within the measured latency plus steering budget. It always requests a first decision for a new barrier. Neither mode changes a model’s chosen lane. When a response arrives, code checks whether the captured obstacle row still exists and the run is still active. Old responses are logged and skipped. A valid response can still be wrong and cause a collision.
Each run stops on a collision, an API error, or after 30 calls. Pause stops new calls. Calls already in flight are still recorded. The two other questions describe the scene; only the open-lane choice steers.
Decision inspector
Three lanes. Three options.
The model chooses an open lane; the car steers smoothly to that lane. This thumbnail is the exact image embedded in the request.
Save this frame ↓Historical frames stay unchanged while the live road moves.
The display shortens image bytes by default. Copy and export always contain the complete image and all questions.
Start a run to receive an actual response.
Make a live call to inspect its timing, usage and provenance.
Three questions, three answer shapes.
The dropdown chooses how many types to include. Only choice is needed to steer.
- Chat LLM / VLM baseline
- GPT-4.1 and Mini use ordinary chat completions with a JSON schema. Image input tests vision; structured JSON tests language reasoning. Chat returns a lane, a boolean and a level index. No class probabilities or confidence are requested or invented. Full JSON shows the exact request and raw response; Full frame also contains the app’s parsed answers. Chat supports structured outputs too; latency depends on the model, provider and workload.
- choice → open lane
choiceis one supplied label.probabilitiesmaps every label to a probability. A separateconfidenceis also returned.- noul → middle lane blocked?
noul: 0.9means estimated 90% probability of “yes”. It is a number, not a boolean. Noul has no separate confidence field.- score → vertical position
- Three ordered levels have indices 0, 1, 2.
score = Σ(index × probability). It may be fractional; it is not P(danger). - null, missing, false and zero
- In our local metadata,
provider_processing_ms: nullmeans unknown. It does not mean zero time. Missingmiddle_blocked.confidencemeans the field is absent. Neither is Jev’snoultype. - metadata belongs to its source
- Model, provider, request ID, tokens and cost are displayed when the selected provider supplies them. Full JSON preserves the OpenRouter response for every model. Frame ID, speed, timestamps, network duration and application outcome are measured by this app.
- OpenRouter format vs direct OpenAI
- OpenRouter uses
state, keyed questions,criteria, andnoul. Direct OpenAI usesinput, a named questions array,choices / levels, andpredicate. OpenRouter translates the model’s interface.
Direct OpenAI request equivalent (not sent)
Image bytes shortened here. The native schema is a teaching comparison; this preview applies only to Luna on OpenRouter.