LLM Benchmark Results for Swift Developers | 2025 Insights
While LLMs prove impressive code generation capacities, up to date benchmarks like HumanEval-XL and MultiPL-E mainly focus on Python and are not adequate for Swift because of language-specific concerns. MacPaw Researchers (Developers) successfully filled this vital gap with SwiftEval, a ground-breaking benchmark.
The team adopted a systematic, quality-first approach, moving beyond automated LLM translations of Python tests, which put scale prior to quality. To construct SwiftEval, the first Swift-specific benchmark, they manually created 28 particular Swift problems.
This carefully developed suite was then utilized to thoroughly assess 44 top Code LLMs, giving the community a much-needed, trustworthy gauge of actual Swift coding prowess. For Swift developers, SwiftEval seems a significant advance toward accurate and properly LLM evaluation.
According to the document, the best performing LLMs for the Swift programming language are as follows based on the SwiftEval benchmark ranking (Table I, Pages 3-4):
Top 5 High-Performance LLMs:
- GPT-4o
- SwiftEval Score: 88.9%
- Ranking: 1.
- Note: Highest Swift performance among all models.
- GPT-4 Turbo
- SwiftEval Score: 87.1%
- Ranking: 2.
- GPT-4o Mini
- SwiftEval Score: 85.6%
- Ranking: 3.
- DeepSeek Coder V2 Instruct (236B parameters)
- SwiftEval Score: 82.4%
- Ranking: 4.
- Note: Best performance among open-source models.
- GPT-4
- SwiftEval Score: 82.2%
- Ranking: 5.
Other Notable Models:
- Qwen2.5 Coder Instruct (32B)
- SwiftEval Score: 79.1% (Ranking: 7.)
- Codestral (22B)
- SwiftEval Score: 77.8% (Ranking: 8.)
- GPT-3.5 Turbo
- SwiftEval Score: 81.3% (Ranking: 6.)
Summary of LLMs Recommended for Swift:
Rank Model Type Swift Score
- GPT-4o Closed Source 88.9%
- GPT-4 Turbo Closed Source 87.1%
- GPT-4o Mini Closed Source 85.6%
- DeepSeek Coder V2 (236B) Open Source 82.4%
- GPT-4 Closed Source 82.2%
Source: Macpaw Research