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 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%.

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.

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 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.

The 0.8B and 2B models share the same Jev request format and can be switched on demand without restructuring the calling logic.

Jeff is a Jev-compatible decision model with 0.8B parameters that can be trained on consumer hardware with an inference latency as low as approximately 30 milliseconds.

Jeff's declared compatibility with the Jev ecosystem is intended to lower the adoption threshold for developers and integrate into existing workflows.

According to benchmark data released by OpenRouter, the Jeff model takes only 154 milliseconds to make a decision, while the second-place model requires 860 milliseconds.

Reviewer Jeff believes that 64GB of memory is unnecessary for a gaming PC, even when playing flight simulators across three 4K monitors.

Jeff suggests that GMKTec sell the Neo X1 Pro in a barebone configuration, pre-installed with the motherboard-CPU combination, liquid cooling, and power supply, allowing users to source their own memory and storage.

Jeff prefers installing Jellyfin on bare metal rather than Docker because enabling hardware transcoding is more straightforward and it can be upgraded alongside Ubuntu updates.