Compare · 2026-10-14

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) vs Qwen Decision 27B noncausal LoRA

As of Oct 14, 2026, decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) leads Qwen Decision 27B noncausal LoRA in 1 of 1 shared categories: Decision models.

Categorydecisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted)Qwen Decision 27B noncausal LoRA
Decision models
#4 · Near-SOTA · Decision models
Margin 2.2727272727272663
JevBench · Oct 8, 2026
#28 · #28 in Decision models
Margin 12.727272727272734
Jev Decision Index · Oct 14, 2026
Price per 1M tokens

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted): price per 1M tokens is unset in the catalog.

Qwen Decision 27B noncausal LoRA: price per 1M tokens is unset in the catalog.

Open weights

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted): open weights.

Qwen Decision 27B noncausal LoRA: open weights.

Which to pick

Decision models: As of Oct 14, 2026, decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) leads Qwen Decision 27B noncausal LoRA in Decision models at #4 against #28.

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) · Qwen Decision 27B noncausal LoRA

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) or Qwen Decision 27B noncausal LoRA?

As of Oct 14, 2026, decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) leads Qwen Decision 27B noncausal LoRA in 1 of 1 shared categories: Decision models.

decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) or Qwen Decision 27B noncausal LoRA for Decision models?

As of Oct 14, 2026, decisio v0.8.0 on gemma-4-31B-it (frozen, FP8 on load, one prefill per state, self-hosted) leads Qwen Decision 27B noncausal LoRA in Decision models at #4 against #28.