React Native Evals: The Mobile App Coding Benchmark Explained

React Native Evals is one of the clearest examples of where AI coding benchmarks are heading next: less abstract algorithm work, more framework-specific product implementation. It is an open benchmark from Callstack focused on real React Native tasks, not generic Python patches or contest problems.

That makes it useful for a very specific reason. Benchmarks like SWE-bench Verified, SWE-bench Pro, and LiveCodeBench tell you a lot about general coding strength. They do not tell you enough about whether a model understands the quirks of a production mobile stack.

What React Native Evals tests

The public React Native Evals dashboard describes itself as an evaluation framework for AI coding agents on React Native code generation tasks. It emphasizes three things:

• working app behavior
• recommended architecture choices
• strict constraint adherence

The current public dashboard groups tasks into areas like navigation, animation, and async state. It also shows repeated runs, token usage, and cost, which makes it more operational than many older benchmark pages.

That is important because React Native work is rarely about one isolated function. It usually involves lifecycle behavior, state hydration, platform-friendly patterns, and library-specific integrations that are easy to get almost right but still ship broken UX.

Current public leaderboard

As of the public March 24, 2026 overview snapshot, the top React Native Evals rows are:

Table 1 Download CSV

Model	RN Evals score
Composer 2	96.1
Composer 2 Fast	94.9
Apex	87.8
Claude Fable 5	87.0
GPT-5.4	85.3
Claude Opus 4.8	84.7
GPT-5.5	84.7
Claude Opus 4.6	84.1
Claude Opus 4.7	82.8
Claude Sonnet 4.6	80.6

The remaining public rows are GPT-OSS 120B at 66.4, GPT-OSS 20B at 64.3, Qwen2.5 Coder 32B Instruct at 42.7, and DeepSeek R1 Distill Qwen 32B at 31.8.

That snapshot is useful because it makes the benchmark concrete. React Native Evals is not just another abstract coding score: it already separates frontier models quite aggressively on actual mobile implementation tasks, with Cursor's Composer 2 models opening a very large gap over the rest of the visible field.

Why this fills a real gap

Generic coding benchmarks still matter:

• SWE-bench Verified is strong for repository bug-fixing.
• LiveCodeBench is strong for fresh problem-solving under contamination risk.
• HumanEval is still a useful floor check.

But none of those are designed around React Native-specific implementation quality. A model can look strong on repository repair or algorithmic reasoning and still make poor choices in app state, navigation, or mobile UI behavior.

React Native Evals is more like a framework benchmark than a general coding benchmark. That makes it narrower, but also more predictive if your actual product work lives inside the React Native ecosystem.

Side by side:

Table 2 Download CSV

Benchmark	Best for	What it misses
SWE-bench	Real repository bug-fixing	Framework-specific product behavior
LiveCodeBench	Fresh algorithmic and reasoning signal	Product architecture and mobile integration
React Native Evals	React Native app implementation	Broad cross-language software engineering coverage

That means React Native Evals should not replace the main coding benchmarks we weight. It should sit beside them.

If you are choosing a model for a general coding assistant, the weighted coding leaderboard is still the right first stop. If you are choosing a model for a React Native team, React Native Evals becomes a valuable second filter.

How we use it, and how you should

We currently track React Native Evals as a display benchmark, not a weighted coding input. That is the right posture for now.

Why:

• it adds useful mobile-specific visibility without distorting the main coding score
• it is too ecosystem-specific to replace broad coding benchmarks
• it can still become more important later if coverage expands and model reporting becomes more consistent

In practice, that means you should read React Native Evals as a specialist benchmark. It is not the answer to "what is the best coding model overall?" It is closer to the answer for "which model is strongest for React Native implementation work?"

So if your team ships React Native apps, put it next to the usual SWE-bench and LiveCodeBench signals rather than relying on either alone.

Use the coding leaderboard for the broad picture. Use React Native Evals when mobile app implementation quality is part of the decision.

→ See the coding leaderboard · Benchmark page · What benchmarks actually measure

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