OpenCode Benchmark Dashboard Launches to Help Developers Compare Local LLM Performance
Key Takeaways
- ▸OpenCode Benchmark Dashboard is a new open-source tool for comparing local and remote LLM performance beyond simple speed metrics
- ▸The dashboard measures 'useful tokens' rather than just tokens per second, providing more accurate real-world performance indicators
- ▸Smaller quantized models like Qwen 3.5 35B (3B active) can outperform larger models in both accuracy and speed for local deployment
Summary
Developer grigio has released OpenCode Benchmark Dashboard, an open-source tool designed to help developers evaluate and compare large language models running locally on their hardware. The dashboard goes beyond traditional metrics like tokens per second, instead focusing on "useful tokens" and actual problem-solving capability to provide a more accurate picture of real-world performance.
The tool allows users to test both local and remote LLM models across various parameters, with interactive visualizations showing the trade-off between accuracy and speed. According to benchmark results shared by the developer, smaller quantized models like Qwen 3.5 35B (3B active parameters) can outperform larger models in both accuracy and speed, while remote models through services like OpenRouter often exceed their quantized local counterparts in performance.
The dashboard includes comprehensive testing capabilities, allowing developers to filter and compare models based on their specific use cases—whether coding, data extraction, or general knowledge tasks. Top performers identified in testing include Qwen 3.5 35B for local deployment and Step 3.5 Flash for remote access. The tool is available on GitHub and requires the Bun runtime, with configuration through OpenCode's system files.
- The tool helps developers optimize their AI setup based on specific hardware constraints and use case requirements
- Remote models generally perform better than quantized local versions, but local models offer privacy and cost advantages
Editorial Opinion
This tool addresses a critical gap in the local LLM ecosystem. As developers increasingly seek to run AI models on their own hardware for privacy, cost, or latency reasons, having an objective benchmarking framework becomes essential. The focus on "useful tokens" rather than raw speed is particularly valuable—it acknowledges that fast token generation means nothing if the model isn't producing accurate or relevant output. This kind of practical, use-case-driven benchmarking could become increasingly important as the field matures beyond headline metrics.
