Qwen3.8-27B scores 29/30 on AIME 2026 with FP8, matching frontier flagships

No_Run8812 · reddit · 2026-08-21

A community benchmark of Qwen3.8-27B on MathArena/aime2026 comparing BF16 vs FP8 weights at medium and xhigh reasoning effort:

FP8 xhigh hits 29/30 (96.7%), tying BF16 xhigh while decoding jumps from 28 to 76 tk/s with much faster pre-fill;
FP8 medium scored 26/30 vs BF16 medium's 28/30 — slight quantization cost at medium effort;
On problem 7, both xhigh runs exhausted the token budget with no final answer (empty, not wrong).

Against frontier models (single pass@1): GPT-5.6 Sol xhigh reported 99.9%, GLM-5.2 and GPT-5.4 99.2%, Gemini 3.1 Pro 98.3%, Claude Opus 4.6 and DeepSeek V4 Pro 96.7% — a 27B FP8-quantized model matching several flagships.

Settings: exact-match scoring, temperature zero, sampling disabled, identical chat template and prompt format.

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Qwen3.8-27B scores 29/30 on AIME 2026 with FP8, matching frontier flagships

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