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Unsloth benchmarks DeepSeek-V4.1, claims >20% lead over GSQ-RCO

By PulseAugur Editorial · [1 sources] ·

Unsloth has released benchmarks for its DeepSeek-V4.1 model, claiming it outperforms GSQ-RCO by 10-20% across various tasks. The benchmarks were conducted with a 512K context window for agentic tasks and 128K for general tasks, utilizing significantly more data (35.1 billion tokens) than previous evaluations. Unsloth also addressed issues with benchmark noise and grader accuracy, employing improved methods and official graders for tasks like Terminal Bench and DeepSWE. AI

IMPACT This research provides performance insights for local LLM users, potentially guiding model selection for specific tasks.

RANK_REASON The item details benchmark results and comparisons for an AI model, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Unsloth benchmarks DeepSeek-V4.1, claims >20% lead over GSQ-RCO

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The item details benchmark results and comparisons for an AI model, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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Clearly on-topic for AI-industry coverage.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/danielhanchen ·

    Unsloth vs GSQ-RCO - 512K context DeepSeek-V4.1 GGUF benchmarks Terminal Bench

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xm102d/unsloth_vs_gsqrco_512k_context_deepseekv41_gguf/"> <img alt="Unsloth vs GSQ-RCO - 512K context DeepSeek-V4.1 GGUF benchmarks Terminal Bench" src="https://preview.redd.it/mjqrl0p8z7zh1.png?width=640&amp…

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