Testing Claude 4 in the Wild: Sonnet 3.7 Vs Opus 4 Vs Sonnet 4
The pace of progress in foundation models over the past year has been astonishing. With each new version, language models demonstrate stronger capabilities in reasoning, writing, and code generation.
But at AutonomyAI, we’re not just chasing benchmarks—we’re building AI agents that reliably work in real-world frontend codebases.
That means we care about more than clever completions. We evaluate how well models can:
- Understand and follow design intentions
- Reuse existing code components
- Fit into team workflows and organizational infrastructure
- Follow code style and project conventions
So when Claude 4 Opus and Claude 4 Sonnet dropped, we put them to the test—side-by-side with Claude 3.7 Sonnet, the model we’ve been using in production.
Our Agentic Flow: A Realistic Evaluation Pipeline
Before diving into model comparisons, it’s important to understand how our agents actually work. This is an simplified overview of our agentic flow that gives that will give an idea of some of the critical steps in the flow that a change in model can critically impact.
0. Visual interpretation of the design
The agent starts by examining the Figma or frontend design visually, breaking it down into structured UI elements. This step varies in reliability depending on the model and the complexity of the design, and it’s especially influenced by the fidelity of the design.
1. Scan and assess
The agent reviews all local components in the project and filters for those relevant to the requested implementation. This is the retrieval step—choosing what to use before building anything.
2. Enable selective reuse
Only the filtered components are made available for use. We don’t force the model to use them—just like human devs, sometimes a component isn’t appropriate or the selection itself was flawed.
3. Output validation
Once the code is generated, we run internal checks to resolve errors, fill design gaps, and improve integration. This step includes validating the final result and, if needed, adapting it based on output quality or component mismatch.
This full cycle lets us evaluate models not just as text generators, but as decision-makers operating within a toolchain.
The TripleR Framework – Our Evaluation Lens
To make our benchmarks meaningful, we evaluate models using our internal TripleR framework:
- Retrieval – Does it choose the right components?
- Representation – Is the data structured cleanly?
- Reuse – Does it behave consistently?
When agents can do all three well, they become reliable teammates—not just assistants.
Stress Testing the Models: Difficult Scenarios Where 3.7 Fell Short
📐 Visual Interpretation of Design (Repeat – Visual)
Our agents begin by interpreting Figma designs visually, identifying elements like layout grids, icons, containers, and text blocks. This step tests the model’s consistency across multiple design parsing runs.
To quantify this, we used a metric we call Element Grouping Consistency (EGC)—which measures how similar the extracted set of elements is from run to run. A higher score means the model is consistently recognizing the same visual structure across trials.
In our tests:
- Claude 4 Sonnet produced the most developer-aligned groupings of components
- Claude 4 Opus had the highest numerical consistency across runs (EGC = 54%) but occasionally used vague or redundant names
- Claude 3.7 Sonnet was the least stable, with inconsistent groupings and naming across runs
This makes Sonnet 4 especially useful for Figma-to-code flows, where understanding designs in a consistent manner and in an intuitive structure help downstream reuse.
🔍 Choosing the Right Components (Retrieval)
We tested each model on difficult scenarios where Claude 3.7 had consistently failed to select the correct local component.
- Claude 3.7 Sonnet: 0%
- Claude 4 Sonnet: 40%
- Claude 4 Opus: 60%
This shows that Claude 4 models are significantly better at understanding and detecting elusive project components that should be used but are not obvious to a developer not from the project—a key strength in Retrieval.
🧠 Implementing with Local Components (Reuse)
Once the right components are identified, does the model use them correctly?
- Reuse of local components:
- Claude 3.7 Sonnet: 85%
- Claude 4 Sonnet: 55%
- Claude 4 Opus: 100%
Claude 4 Opus stands out for its reliability in component usage, though Sonnet 4’s performance and style are often more intuitive and cleaner in implementation. Furthermore, we also took a look at model preference to use in-project libraries instead of writing logic from scratch. This matters, leveraging internal libraries helps teams avoid redundant logic, maintain consistency, and reduce tech debt over time While all models struggled somewhat to recognize in-project libraries, Claude 4 Opus showed modest improvement—successfully identifying and using them 54% of the time, compared to 40% for both Sonnet 4 and Sonnet 3.7. Opus still struggles slightly in recognizing custom libraries, which is an area we compensate for in our infrastructure.
✅ Output Quality: Clean and Modular
We also measured how often models produced error-free output and how well they broke the implementation into manageable files.
- Error-free runs:
- Claude 3.7 Sonnet: 60%
- Claude 4 Sonnet: 100%
- Claude 4 Opus: 100%
We observed a clear progression in how models structure their output: Claude 3.7 < Claude 4 Sonnet < Claude 4 Opus. As models improve, they tend to produce code that’s increasingly broken into modular chunks, reflecting a more scalable and organized development approach.
Claude 4 models produce cleaner, more modular output—a huge win for engineering teams maintaining large systems.
Adaptive Agents: Choosing the Right Model, Step by Step
At AutonomyAI, we don’t just “pick the best model.” We let our agents decide dynamically which model to use for each step in the flow. That means:
- Claude 4 Opus might be used for implementation
- Claude 4 Sonnet for interpreting design and layout
- Claude 3.7 for lightweight or familiar tasks
Agents can even switch models mid-run, adapting to task complexity or confidence.
This strategy gives us reliability, speed, and flexibility—without having to wait for a “perfect model.”
Some developers have noted that the jump from Claude 3.7 to Claude 4 feels incremental—but our experience tells a different story at scale. Claude 3.7 was a huge leap forward for one-page implementation workflows, as used in platforms like Lovable or Replit. But for organizations like ours—working with huge codebases containing hundreds or even thousands of components—Claude 4, and especially Opus, offers a meaningful shift in capability. It retrieves more relevant information, structures output more reliably, and scales better across large frontend architectures.model.
Final Thoughts: Model Upgrades, Agent Intelligence
Claude 4 is a clear upgrade—especially Opus for precision and Sonnet for balanced behavior. But at AutonomyAI, it’s not just about model improvements.
Our strength lies in how we build around these models:
- Structuring inputs
- Validating outputs
- Choosing tools dynamically
With ACE and TripleR, our agents are becoming true co-developers—not just smarter autocomplete.
Want AI that builds as well as it thinks? Let’s talk.
#AIagents #FrontendDev #Claude4 #AutonomyAI #TripleR #LLMengineering #DesignToCode
