Qwen 3.8's High Token Cost is a Fair Trade for Performance
Altruistic_Heat_9531 · reddit · 2026-08-17
- Argument: The 16K+ reasoning tokens in Qwen 3.8 27B are a necessary cost to compete with 1T+ parameter models, aligning with Karpathy's "tokens to think" theory.
- Data: SWE-Rebench notes Qwen Next averages 8.12M tokens/problem; VibeThinker 3B also consumes heavy tokens despite its size.
- UX: Inference may be slow (e.g., 1 hour on a 4060 Ti), but it consumes machine time, not user time.
- Position: Qwen acts as the "Anti-OpenAI"—Apache-licensed, local-first, and exposes extensive reasoning traces.
- Tip: For daily search, consider Gemma 4 26B or GPT-OSS 20B; use Qwen when top-tier reasoning is needed.
More from Models
- GLM 5.5 rumored to drop soon, coding ability expected to match frontier models — bindureddy · 2026-10-16
- Five rounds of adversarial prompting make Opus 5.5 and Argon converge on identical output — LanceCharger · 2026-10-16
- DIY benchmark ranks 18 models on subjective judgments, GLM-5.3 tops consensus match — arkuto · 2026-10-16
- Bindu Reddy: Google's Argon excels at complex doc writing at half the cost of Opus — bindureddy · 2026-10-16
- MAI-2 is 2x bigger than V4-Pro while V4.1F is 3x smaller: Microsoft's scaling ladder questioned — teortaxesTex · 2026-10-16
- Step 5 Preview hits DeepInfra day-zero: 600B MoE, 1M context, $1/M input tokens — StepFun_ai · 2026-10-16