These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.
JEVBench · public 231
86.15 / 100
Micro accuracy across 48 Easy, 72 Original and 111 Hard-public decisions using the typed-decision serving bundle. Not an official leaderboard rank.
Benchmark efficiency: 67.601 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: Publisher-run JEVBench public set: the selected Q4_K_M answered 199 of 231 items correctly using the bundled llama.cpp typed-decision server. The card reports 94.4% top-decision parity with the original weights on the same 231 items; the original/BF16 model answered 195 of 231.
Artifact-specific specialist result, rounded from 199/231. This is not an independent test, an official leaderboard rank, a general-chat score or a speed result. Plain chat is not the evaluation path: option-letter readout and per-type temperatures live in the bundled gguf_server.py, and llama.cpp b10454 or newer is required. The 1,274,396,256-byte GGUF contains the text decoder only; it excludes images, runtime memory and context memory. CC BY-NC 4.0; commercial use requires a separate StartLux license. The publisher recommends Q8_0 for higher decision fidelity even though this Q4 scored higher on the public set.
Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.
The 1.274 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.
These complete configurations pass the initial weight-size check for the selected 1.27 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.
Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.
Listing evidence: 2026-10-03. Apple US launch price announced 22 September 2026: USD 1,699. Dated manufacturer reference, not an Amazon offer; tax and shipping may differ.
Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.
Memory matches are estimates, not tested compatibility or speed. As an Amazon Associate I earn from qualifying purchases.
Confirm model architecture and quantization support in your runner. The selected weights use 1.274 GB; this excludes context cache, runtime buffers and other programs.
These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.