Qwen 3.5 Chat Template Bug Causes Massive Cache Inefficiency
Is this a scandal?
No longer — the story has resolved. Noise 0/100, cooling down, across 0 sources.
Alibaba's Qwen team is likely to integrate this template fix into their official Hugging Face repositories and model configs within days to maintain their lead in open-source efficiency benchmarks. Developers of local inference engines will likely add temporary overrides or warnings for Qwen 3.5 users until the official templates are updated.
Noise 0/100 — louder than 85% of tracked AI controversies.
Why it matters
This technical oversight leads to significant latency and computational waste in local AI deployments, undermining the efficiency of prefix caching in agentic workflows.
Key points
- A bug in the Qwen 3.5 chat template causes empty reasoning tags to be emitted, breaking prefix cache reuse.
- The issue affects multiple inference backends including oMLX.ai and llama.cpp, particularly during tool-heavy or agentic workflows.
- Affected users experience unexpected latency spikes where tens of thousands of tokens are reprocessed unnecessarily during follow-up turns.
- The proposed fix involves adding a conditional check to the template to ensure historical blocks only render if reasoning content exists.
The story
A significant technical flaw has been identified in the official chat template for the Qwen 3.5 model series, leading to massive cache misses during inference. The issue, discovered by developer onil_gova, stems from the template emitting empty historical reasoning blocks even when no reasoning content is present. This behavior causes 'prompt drift,' where identical conversation histories are serialized differently across requests, preventing inference engines like llama.cpp and oMLX from reusing previously processed tokens. Consequently, follow-up turns after tool-heavy interactions often trigger the reprocessing of tens of thousands of tokens. The developer has proposed a simple one-line logic fix to the Jinja template to ensure historical blocks are only rendered when they contain actual content. This discovery highlights the critical role of template consistency in maintaining performance for large language model applications.
Who's involved
Identified the technical flaw and proposed a one-line code fix to prevent prompt drift and cache misses.
Creators of the Qwen 3.5 model and the original chat template currently under scrutiny for efficiency issues.
Noise Level
The timeline
Root cause identified
The issue is traced back to unnecessary empty blocks in the Jinja chat template, and a fix is shared publicly on Reddit.
Investigation begins
Developer onil_gova begins investigating unexplained cache misses on an M5 Max system while using Qwen 3.5.
The full record
What's being under-reported
No defender-side coverage yet
The critic side is sourced here; no defending voice has been captured yet.
- Coverage: 0 social posts, 0 news-outlet items.
- Voices: 1 critic, 0 defenders.
The forecast
Alibaba's Qwen team is likely to integrate this template fix into their official Hugging Face repositories and model configs within days to maintain their lead in open-source efficiency benchmarks. Developers of local inference engines will likely add temporary overrides or warnings for Qwen 3.5 users until the official templates are updated.
Forecast, not fact — an editorial estimate we score when this resolves.
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