Open-weight models gain token share while leading closed models score higher
OpenRouter reports that open-weight models rose from 27% to 72% of the top 50 models’ tokens on its platform between October 2025 and late September 2026. Its token-weighted pricing data lists open-weight models at $0.28 per million input tokens and $1.62 per million output tokens, compared with $1.27 and $6.36 for closed-weight models. On the Artificial Analysis Intelligence Index, Claude Opus 5.5 scored 57.6, compared with 46.3 for MiMo-V2.6-Pro, the leading open-weight model in the comparison. The report says these scores depend on reasoning-effort settings, the 11-point gap is a snapshot, and token counts use providers’ own tokenizers. OpenRouter recommends testing an open-weight model on a team’s tasks and using closed-weight models for steps where tests show extra quality matters.
The figures present a trade-off for developers: open-weight models had lower token-weighted prices and a larger share of usage on OpenRouter, while leading closed-weight models scored higher on the index’s hardest tasks. The report says the benchmark gap does not indicate how much the difference will matter for a particular workload, supporting workload-specific testing rather than a universal choice.
Open-Weight vs Closed-Weight Models: What's the Difference?
OpenRouter ·10/31/2026
An open-weight model is one whose trained weights are published, so anyone can download it, run it on their own hardware, fine-tune it, or serve it to others. A closed-weight model keeps its weights private, so the only way to use it is through the company that built it or a cloud it licenses to.
Across OpenRouter, open-weight models went from 27% of the top 50 models’ tokens in October 2025 to 72% in late September 2026.
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