A community benchmark table provides clear numbers: Ripple 65B scored 80.5 on Terminal Bench 2.1, ahead of Qwen 3.8 27B’s 73. It also led 79.1 to 61.7 on SWE-Bench Pro v2.0 Hard. Our judgment is that competition among local models is shifting from “how large is the model” to “can the model complete the task,” but this comparison alone is not enough to determine which model to select.
What This Is
This is a comparison of coding capabilities among models designed to run locally, meaning AI models that can operate on your own devices or servers. Terminal Bench, DeepSWE and SWE-Bench are all used to evaluate how models handle real-world software tasks, such as using terminals, modifying code and fixing defects.
The table also includes Qwen 3.8 Flash Next. Its DeepSWE 1.1 score of 58.7 exceeded Ripple’s 44.3 and Qwen 3.8 27B’s 42.2, showing that different models do not have consistent strengths. However, SWE-Bench Pro used v2.0 Hard for Ripple while the specific Qwen version is unknown. As a result, not all the results can be treated as a strictly like-for-like comparison.
Industry View
We believe the most important takeaway is not any single first-place finish, but that 65B models have already clearly approached or surpassed smaller-parameter models on some coding benchmarks. For enterprises that need code completion, automated testing and troubleshooting, how easily a model can be deployed may bring it closer to practical value than its ranking on a leaderboard.
The risks are equally clear: community preview results do not necessarily translate to production performance, inconsistent versions weaken the credibility of the conclusions, and 65B models generally impose higher requirements on VRAM, speed and cost than 27B models. Benchmark tasks are also removed from everyday business operations. Enterprises still need to retest models using their own codebases, permission systems and evaluation data.
Impact on Regular People
For enterprise IT: The potential of local models for handling sensitive code and supporting controlled deployments continues to expand. But model selection should not rely on leaderboards alone; teams must also account for compute costs, response speed and maintenance capabilities.
For individual professionals: More capable coding models will continue to lower the barrier to using automation scripts and testing tools. The real differences will increasingly come from whether a model understands the business context and can verify its results.
For the consumer market: As smaller and mid-sized models gradually approach the coding performance of large models, automation features are likely to become more widespread in office software and developer tools. In the near term, however, the impact will be concentrated in professional tools rather than a wholesale replacement of underlying models across mainstream consumer applications.