Resolution criteria

This market resolves YES if, before December 31, 2026, an open-source LLM model is publicly released that demonstrably outperforms Claude Opus 4.5 on standard benchmarks. Performance will be evaluated using publicly available benchmark results from sources such as:

• Hugging Face Open LLM Leaderboard

• LMSys Chatbot Arena

• Artificial Analysis

• Specialized benchmarks (SWE-bench, ARC-AGI, GPQA, etc.)

The model must be "open-source" or "open-weight" (weights publicly available for download and self-hosting). Proprietary API-only models do not qualify. The model must show superior performance on at least one major benchmark category where Opus 4.5 currently leads, or demonstrate clear overall superiority across multiple benchmarks. The market resolves NO if no such model is released by year-end 2026.

Background

Claude Opus 4.5 achieved state-of-the-art results for complex enterprise tasks on benchmarks, outperforming previous models on multi-step reasoning tasks that combine information retrieval, tool use, and deep analysis. With a score of 80.9% on SWE-bench Verified, it surpasses both GPT-5.1 and Gemini 3 Pro, demonstrating a strong ability to resolve real-world software issues from GitHub repositories.

The open-source LLM landscape has advanced significantly in 2025. DeepSeek came to the spotlight during the "DeepSeek moment" in early 2025, when its R1 model demonstrated ChatGPT-level reasoning at significantly lower training costs. The latest release, DeepSeek-V3.2, builds on the V3 and R1 series and is now one of the best open-source LLMs for reasoning and agentic workloads. DeepSeek-V3.2 effectively ties with proprietary models on MMLU (94.2%), making it the most reliable choice for general knowledge and education apps.

Considerations

Defining "better" requires careful interpretation of benchmarks. The frontier LLMs are a lot harder to differentiate between. Benchmarks like SWE-bench Verified show models beating each other by single digit percentage point margins, but what does that actually equate to in real-world problems that I need to solve on a daily basis? Different benchmarks measure different capabilities—a model might excel at coding while another dominates reasoning or knowledge tasks. The resolution will depend on which benchmark categories are prioritized and whether marginal improvements count as "better."