migtissera/Tess-4.5-27B (Qwen finetune)
Tess-4.5 reaches the same score with a quarter fewer tokens.
Proof1 community thread
Key points
- It is leanest where the work is hardest: on CLI-40 it used 32% fewer tokens and still scored higher, and on data extraction 43% fewer.2
- Tokens to the same score Tess-4.5-27B Qwen3.8-27B
- Fewer tokens means answers sooner, less context spent on its own thinking, and longer sessions before the window fills.
- On your own GPU it compounds with the draft head: fewer tokens, each one faster.
Sources (1)
- [1]migtissera/Tess-4.5-27B (Qwen finetune)r/LocalLLaMA (top, daily) · Oct 20, 12:09 AM
Tess-4.5 reaches the same score with a quarter fewer tokens.
It is leanest where the work is hardest: on CLI-40 it used 32% fewer tokens and still scored higher, and on data extraction 43% fewer.2
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms
- Oct 19, 2026unslothai/unsloth v0.1.909-beta: DeepSeek-V4.1 & FLUX-3
- Oct 19, 2026Calling all GPUs: help us benchmark SonicBoom v0.0.3 — a compiler-first AI runtime that beats llama.cpp on decode
- Oct 19, 2026TorchNeuron: running HF models on AWS Trainium is easier than ever
- Oct 18, 2026Agens Volundr 32B v1: we fixed what you flagged in the Preview. 0/50 loops, session continuation, and an MIT engine that runs it with 262K context (measured to 239K) within a 16 GB budget
- Oct 17, 2026Why not run Qwen 3.8 Flash Next with the full n-gram-table?
