quant benchmarks · measured numbers

Quant benchmarks

This is a catalog of measured quantization benchmarks — quality deltas versus the full-precision baseline and serving throughput — for the current generation of open models. Every number is sourced from official vendor model cards, quantization publisher cards and serving recipe pages, with gaps called out where nothing is published.

Where the community benchmarks report what people say, these reports measure what the numbers are: for each model, the full-precision (BF16/FP8) baseline scores sit next to the scores of its published quantized checkpoints, and the gap between the two is the quality delta — what you give up in accuracy when you shrink the model to fit your hardware.

A delta row lists one benchmark, the baseline score, the quantized score and the difference; a null baseline means the source card published the quantized number without a reference to diff against, so the row shows the absolute score only. Every number is sourced from official vendor model cards, quantization publisher cards and serving recipe pages — no community opinions — and gaps are called out explicitly where nothing is published.

How to read them: deltas under one point are effectively lossless for most use cases, a few points of drift is the usual cost of 4-bit formats, and anything larger is a real trade-off to weigh against the memory and serving-speed gains the smaller checkpoint brings.

All quant models

ModelOrgParamsContextLicenseRelease
GLM-5.3-FlashZ.ai / THUDM320B total / 18B active1MMIT2026-08-25
GLM-5.3Z.ai / THUDM753B total / 40B active1Mglm-5.3 (custom)2026-08-25
Qwen 3.8 27BAlibaba27B dense + vision262K→1Mapache-2.02026-08
Qwen3.6-35B-A3BAlibaba35B total / 3B active262K→1Mapache-2.02026-04
Gemma 4Google30.7B dense256K (12B) / 128K (E2B/E4B)Apache-2.02026-04 (12B Jun, QAT Jun 5)
MiMo-V2.6-FlashXiaomi309B total / 15B active1MMIT2026-10
MiMo-V2.6-ProXiaomi1.02T total / 42B active1MMIT2026-10
Hy4-previewTencent770B total / 49B active (+10B MTP)1MApache-2.02026-08-27

For community sentiment on some of the same models, see the community benchmarks →