Open-Source LLM and Leaderboard 2026 Collaboration
benchmarks deepseek llama open-source qwen reasoning
| Source: Mastodon | Original article
Open-source large language models are ranked in a new leaderboard. Top models' performance is measured across various benchmarks.
The Open-Source LLM Leaderboard 2026 has been released, providing a comprehensive comparison of open-source and open-weight LLM benchmarks. Granite 4.0 1B has been measured independently, with scores including 28.1% on GPQA, 32.5% on MMLU-Pro, 5.1% on Humanity's Last Exam, and 4% on Long Context Reasoning. This leaderboard tracks the performance of various models, including Llama, DeepSeek, and Qwen, across tasks such as reasoning, coding, math, and multilingual tasks.
The release of this leaderboard matters as it provides a transparent and independent assessment of open-source LLMs, allowing developers and users to make informed decisions about which models to use. With the increasing importance of LLMs in various applications, a reliable and up-to-date leaderboard is essential for the community.
As the landscape of open-source LLMs continues to evolve, it will be interesting to watch how the rankings change over time. With new models being developed and existing ones being updated, the leaderboard will likely see significant shifts in the coming months. Additionally, the availability of such leaderboards may drive further innovation and improvement in the field of LLMs, as developers strive to create better-performing models.
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