Best Free AI Coding Model for Real Flutter Work? A PDF Splitting Test
Most AI model comparisons focus on easy outputs: a landing page, a small Python script, or a neat benchmark score. Those are useful snapshots, but they do not tell you much about how a model behaves in real product work.
What matters more is whether a model can read package documentation carefully, identify the limits of an abstraction, follow the dependency chain underneath it, and still produce working code.
That is why this Flutter test stands out.
The Real-World Flutter Task
The assignment was straightforward on paper: build a simple Flutter app that accepts a PDF and splits it into two PDF files.
The hidden difficulty was in the package structure.
The package initially provided to the models was a high-level Flutter PDF viewer. It could display PDFs inside the app, but it could not split them directly. To solve the task properly, a model needed to recognize that limitation and then trace the implementation down to the lower-level PDF engine that actually supports PDF manipulation.
That sounds simple, but it exposed a weakness in many free AI coding models. Several of them read the top-level package docs too loosely and answered with more confidence than accuracy.
The First Question Most Models Missed
Before any code generation, the models were asked one basic question: Can this high-level Flutter package split PDFs?
Most of the free models tested answered incorrectly and said yes.
Only Codex and GLM5 initially answered correctly and said no.
That early question mattered because it measured something important: whether the model could distinguish between a UI-facing wrapper package and the lower-level engine it depends on. In real engineering work, that distinction is routine. A lot of implementation mistakes happen because tools at the top of the stack look more capable than they really are.
The Coding Test Setup
After the documentation question, each model received a very small Flutter app, roughly ten lines long, that simply displayed a PDF using the high-level package.
The next prompt was to modify that app so it could split the PDF into two files.
A few constraints make this comparison more meaningful:
- All models were used in thinking or reasoning mode.
- Only free, publicly available versions were included.
- The latest available free version of each model was used at the time of testing.
- No paid models were part of the comparison.
That matters because several models improve substantially once reasoning mode is enabled. Without that mode, some of the weaker results would likely have looked even worse.
Ranking the Free AI Coding Models
1. Kimi 2.5 Thinking
Kimi 2.5 was the strongest performer in this test.
It produced working code quickly, with no syntax errors and no logic failures. Just as importantly, it used only the packages that were actually necessary. That combination of correctness and restraint is rare. Many models can eventually produce a working result, but far fewer can do it cleanly on the first pass.
For this task, Kimi behaved the most like an experienced engineer reading the package tree before writing code.
2. Sonnet 4.6 Extended
Sonnet came very close to the top spot.
Its output was nearly correct on the first attempt, with one small syntax issue that required removing a const. That is a minor correction, especially compared with models that generated multiple invalid constructs or needed several rounds of repair.
If the evaluation weighted near-perfect output almost the same as perfect output, Sonnet would have been right next to Kimi.
3. GPT-5 Thinking Mini
GPT-5 Thinking Mini generated code that worked without breaking errors.
The reason it landed in third place was efficiency rather than correctness. It introduced some unnecessary packages and imports. That does not stop the app from working, but extra dependencies are still a signal. In production code, unnecessary packages add noise, maintenance overhead, and sometimes confusion for less experienced developers.
It was a solid result, just not the leanest one.
4. Grok Expert
Grok delivered something usable, but it included around three minor syntax issues.
None of them were severe enough to make the attempt a total failure, and a developer could clean them up without much effort. Still, compared with the top three, it made more avoidable mistakes and required more intervention.
5. Gemini 3.1 Pro Thinking (High)
Gemini’s first attempt contained too many errors for comfort.
There were several issues overall, including a couple that were especially strange because they referenced keywords that do not exist in Dart or in the relevant package APIs. After feedback, the answer improved, but the revised version still contained a problem that could easily mislead a beginner Flutter developer.
That made the result feel less dependable than the models above it.
6. DeepSeek DeepThink
DeepSeek eventually reached a working outcome, but only after multiple correction rounds.
The first output contained several errors that were difficult to parse cleanly, which slowed down the process. In practical terms, that means more back-and-forth, more debugging time, and less confidence in the first pass.
7. GLM5 DeepThink
GLM5 had an interesting split between analysis and execution.
It correctly identified early on that the high-level package could not split PDFs, which many other models missed. But it failed to convert that understanding into a working solution. Even after repeated feedback, it kept getting trapped by the same keyword-related mistake.
That is a useful reminder that reading docs correctly and implementing code correctly are related, but not identical, capabilities.
8. Codex
Codex failed in a different way.
It correctly answered that the high-level Flutter package could not split PDFs. But when asked about the lower-level engine, which actually could support the split operation, it still answered no. So while it recognized the viewer package limitation, it did not follow the package stack far enough to find the real implementation path.
That is not the most common failure mode in AI coding tests, but it is a meaningful one.
Why This Test Matters More Than Simple Demos
The most useful part of this comparison is not the ranking by itself. It is what the task measures.
Many AI models can generate respectable HTML, CSS, JavaScript, or Python in low-friction settings. The difficulty rises quickly when the task depends on real package documentation, layered APIs, wrapper libraries, or lower-level engines that are not obvious from the first URL.
That is much closer to how software work actually happens.
Developers regularly deal with packages that expose only a subset of an engine’s capabilities. They work through incomplete docs, examples that are too shallow, and abstractions that hide the underlying tool doing the real work. A model that cannot reason through that stack will often sound confident while taking the wrong path.
This is exactly where Kimi 2.5 separated itself from the rest in this test.
What Product Teams Should Take Away
For agencies, startups, and product teams evaluating free AI coding tools, this experiment highlights a useful benchmark: do not just test whether a model can write code. Test whether it can understand the architecture around the code.
In a real client build, the quality bar is not “did the model return something plausible?” The real question is whether it can:
- Identify what a package can and cannot do
- Follow a dependency chain to the right implementation layer
- Avoid inventing unsupported APIs
- Produce working code with minimal cleanup
Those are the traits that save engineering time.
On this specific Flutter PDF splitting task, Kimi 2.5 delivered the strongest overall result because it combined documentation awareness, implementation accuracy, and clean output in one pass. That does not mean every other model is unusable. It means this test surfaced the difference between general code generation and practical engineering support.
Final Verdict
Based on this Flutter package and PDF engine test, Kimi 2.5 was the best free AI coding model in the group.
It was not just the model that produced working code. It was the model that produced working code fastest, with the least friction, and with the smallest amount of unnecessary baggage.
That is the standard that matters in production environments.
If your team is comparing AI tools for real development workflows rather than lightweight demos, tests like this are far more revealing than generic benchmark tables.
Related Reading
If you are evaluating AI coding workflows for client or product work, these articles add useful context: