Ollama Cloud models tested on code review: DeepSeek v3.1 beats Qwen and GPT-OSS

A hands-on test shows that cloud models running through Ollama can already handle part of the code review workload on real pull requests, not just on demo examples. In a comparison of Qwen 3.5, GPT-OSS and DeepSeek v3.1, DeepSeek delivered the deepest analysis and the most actionable recommendations, though with an important configuration caveat.

How the test was set up

The author didn't test the models on abstract tasks. Instead, he used pull requests from his own legacy Python project, which is about four years old. Each model got its own PR, but the conditions stayed identical: the same prompt, the same access to project context, and RAG enabled so the system could pull in additional files rather than work from the diff alone. That matters, because a lack of context is often what makes AI reviews shallow.

The configuration was aligned as closely as possible: temperature 0.2, a 4000-token limit, a high severity threshold for comments, checks for security, performance and style issues enabled, and permission to suggest fixes. The models analyzed not only the diff but also the related code context.

The test covered Qwen 3.5, GPT-OSS and DeepSeek v3.1, three prominent open-weight models that developers often consider as alternatives to SaaS tools.

The models were scored on a five-point scale. The key criteria were:

• accuracy in finding real problems in the code
• understanding of security risks
• tendency to hallucinate
• depth of analysis and understanding of how changes ripple through the code
• practical usefulness of suggested fixes

The author also looked separately at the human acceptance rate: how likely a developer is to actually accept the model's comments rather than dismiss them as noise.

Results by model

Qwen 3.5 was a pleasant surprise. It scored 3.8 overall and struck a solid balance between accuracy, a low hallucination rate and practical advice. According to the author, the model tied its comments to specific lines well, frequently offered real fixes and generally behaved like a useful first reviewer. Its weak spots: limited depth in architectural analysis and fairly sparse use of the available tools to fetch extra context.

GPT-OSS, by contrast, performed noticeably worse, scoring 2.9. The main complaint is that its comments were too generic. The model found some real problems, but it did a poorer job of linking comments to specific changes in the PR, suggested applicable auto-fixes less often and more frequently drifted into speculation without sufficient grounds. Its clear response style was a plus, but that wasn't enough for practical code review: developers need precise, useful comments, not polished wording.

DeepSeek v3.1 posted the strongest technical result. Without the penalty, it scored 4.25: the model explained the root causes of problems better than the others, caught security risks more often, proposed technically sound fixes and analyzed the consequences of changes in more depth. Formally, the author lowered its score to 3.25 because the model couldn't use tools without think mode enabled. Even with that caveat, DeepSeek was named the deepest and most practical option of those tested.

"Cloud models through

Ollama can genuinely be used for code review tasks."

Where Ollama makes sense

The article's main takeaway isn't that Ollama automatically replaces dedicated services like CodeRabbit, Claude Review or QoDo. If anything, the opposite: the quality of AI review depends heavily on the chosen model, its settings and how much context it gets. Pick a weak model or restrict it to the diff without access to project files, and the output quickly degrades into a pile of superficial comments.

Where Ollama shines is in setups where a team values control and flexibility. The author specifically notes that this approach is especially appealing for projects with sensitive code, NDA restrictions, or a desire to keep source code off external infrastructure. The platform also makes it easy to switch between models, build custom pipelines on top of the API and, if needed, move from the cloud to running models locally.

If a team has no strict privacy requirements and budget isn't a concern, off-the-shelf SaaS tools may still deliver more consistent results out of the box. They integrate more tightly into the workflow, offer more ready-made automation and require less manual setup. If anything, the experiment shows that open models are catching up with this class of products faster than many expected.

What it means

For development teams, this is a sign that AI code review is no longer a toy and can serve as a working pre-review layer for pull requests. It doesn't replace a human reviewer, but with the right model, good context and tool access, it can take over some of the routine work, find real problems and suggest fixes before the PR ever reaches a colleague.