Jeff

The inference latency of the front-end lightweight model typically needs to be kept within 200 milliseconds to match the human perceptual threshold for real-time interaction.

The narrator said that without Brian and Jeff, he would not be here today.

The remaining work on Jeff's studio includes installing ceiling lights, wiring, replacing the garage door power supply, and adding insulation.

Jeff confirmed that after fainting he had no concussion or vision damage.

Jeff stressed that this fainting was not a heart problem but the combined result of fatigue, a cold, his height, and a low resting heart rate.

Jeff is 6 feet 4 inches tall (about 193 cm) and has a resting heart rate of about 55 beats/min.

Jeff of Craft Computing installed remote-controlled track lights, which support adjusting color temperature, brightness, and beam angle, for his newly renovated studio.

The launcher supports all ten test checkpoints, including the Cloudflare Clef series, Perplexity PPLX, TokenRhythm NeoHorse, Jeff, and Convai Innovations Laya; each key selects a locked snapshot and a family-specific backend.

The Jeff model is a decision-specific model series developed in-house by Bespoke Labs, with parameter counts ranging from 0.8B to 1.4B, while the Nimble series is fine-tuned on a Qwen base.

The benchmark published by Bespoke Labs covers 33 public datasets and 3,880 manually labeled decisions; Jeff 1.1B achieves the highest average accuracy at 76%, Nimble 9B at 75.7%, Jeff 1.4B at 73.3%, and the 0.8B version at 63.5%.

In a shuffled management hierarchy tracing test, the Jeff model gave the answer 'Eli' almost instantly with no visible reasoning process, but the correct answer was 'Gus'.

In experiments where the chain was progressively lengthened, all three types of models were correct at one step, but from the second step onward the two 'one-glance models' began making errors, and after a few more steps were almost entirely wrong, while the thinking model performed near-perfectly throughout with only its token count increasing.

Jeff's wrong answers are mostly not random; regardless of how many levels up it is asked to go, its most common answer is 'the person three levels up'.

Jeff-class products restrict output to selecting from a predefined label set, so they will only answer incorrectly with confidence rather than fabricating options from thin air (no hallucination).

When Jeff is directly asked 'Is this email a phishing email?', accuracy on difficult datasets is close to a coin flip, but accuracy improves significantly when the question is broken down into smaller questions targeting specific features and then combined using code.

Each of Jeff's answers comes with a confidence score; on chain questions, when the question depth exceeds what can be grasped at a glance, confidence drops along with accuracy.

Jeff is a fast decision-making model released by Anthropic, positioned as a System 1-style intuitive judgment model.

The project explicitly identifies where the benchmark no longer matches real-world workloads.

The Jeff project distilled and fine-tuned two base models, Qwen3.5 and Gemma 4, into decision models with 0.8B and 2B parameters respectively.

The project team publicly released the corresponding run commands, enabling others to reproduce the latency tests.