Unverified50% confidenceFactExact time
The capability gap between different models on tool calling is far greater than the gap on ordinary text generation.
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50%
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Long-term
Relevance
10/12/2026
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UnverifiedThe capability gap among leading large language models is narrowing, with mainstream models performing very similarly on common tasks such as writing, coding assistance, and information summarization.80% similarUnverifiedSwitching models mid-session can lead to inconsistent tool-call states.72% similarUnverifiedThe researchers acknowledge that the model still falls far short of classical language models trained on trillions of tokens on text tasks.70% similarUnverifiedCurrent mainstream large language models perform excellently when processing plain text, but have significant bottlenecks in understanding non-text formats such as tables, flowcharts, and screenshots.70% similarUnverifiedNLP research shows that style transfer is far more difficult on short texts than on long texts, because there are fewer style signals available for the model to capture.70% similar
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