Phi-4 14B vs Gemma 3 27B: Which is Better in 2026?
Phi-4 14B (Microsoft, 14.66B parameters) and Gemma 3 27B (Google, 27B parameters) are both frontier-class models competing for the same developer and enterprise audience in 2026. This page compiles benchmarks, task grades, and practical guidance to help you decide which model fits your workflow.
Last updated: October 2, 2026
Quick Verdict
Across the benchmarks where both models have published scores, Phi-4 14B leads on 2 of 2 shared evaluation tasks. Gemma 3 27B remains competitive, particularly in areas aligned with its training focus. For general-purpose quality, Phi-4 14B currently holds an edge — but the right choice depends heavily on your specific use case, budget, and whether you need API access or self-hosted deployment.
Side-by-Side Comparison
The table below covers every benchmark for which at least one model has a published score. Higher scores are better on all metrics except where noted. Bold values indicate the higher score in each row.
| Benchmark | Phi-4 14B | Gemma 3 27B | Winner |
|---|---|---|---|
| MMLU-Pro | 70.4 | 67.5 | Phi-4 14B |
| HumanEval | 82.6 | 48.8 | Phi-4 14B |
| MATH-500 | 80.4 | — | Phi-4 14B |
| GSM8K | — | 82.6 | Gemma 3 27B |
| ARC-Challenge | — | 70.6 | Gemma 3 27B |
| MMMU | — | 64.9 | Gemma 3 27B |
| GPQA Diamond | — | 42.4 | Gemma 3 27B |
| LiveCodeBench | — | 29.7 | Gemma 3 27B |
| Arena Elo (Text) | — | 1358 | Gemma 3 27B |
| Arena Elo (Vision) | — | 1165 | Gemma 3 27B |
| AIME 2024–25 (Epoch AI) | — | 22.5 | Gemma 3 27B |
| Aider Polyglot | — | 4.9 | Gemma 3 27B |
Task Performance
Per-task grades are sourced from the id8 LLM leaderboard evaluations. Each grade reflects observed output quality across real-world prompts in that category. A dash (—) means grades are not yet published for that model.
| Task | Phi-4 14B | Gemma 3 27B |
|---|---|---|
| Coding | B | B |
| Math | B | B |
| Content Writing | B | B |
| Reasoning | B | B |
| Studying | B | B |
| Chat / Conversation | B | B |
| Summarization | B | B |
| Agents / Tool Use | C | C |
| Vision / Multimodal | C | B |
| Data Analysis | C | B |
| Hindi / Multilingual | D | C |
| Interview Prep | B | B |
| overall | B | B |
Grade scale: S = Exceptional A = Strong B = Good C = Fair D = Weak
Key Differences
- Both models offer open weights for self-hosting: Phi-4 14B from Microsoft (14.66B parameters) and Gemma 3 27B from Google (27B parameters).
- In terms of raw scale, Gemma 3 27B (27B parameters) is significantly larger than Phi-4 14B (14.66B parameters). Larger parameter counts often correlate with stronger reasoning, though efficiency improvements mean smaller models can punch above their weight.
- On HumanEval (code generation benchmark), Phi-4 14B outperforms with a score of 82.6% vs 48.8%.
Which Should You Choose?
Choose Phi-4 14B if benchmark-verified reasoning and coding performance are your top priority. Its published scores on GPQA Diamond and SWE-Bench make it a strong choice for knowledge-intensive and software engineering workflows.
Choose Gemma 3 27B if you need a capable open-weight model at 27B scale with solid benchmark performance across reasoning and STEM tasks, especially if self-hosting is part of your deployment plan.
Still unsure? The LLM Leaderboard lets you sort and filter models by benchmark — useful for narrowing down the right model for a specific workload.
Explore Further
Use these tools to dig deeper into either model's hardware requirements and leaderboard ranking.
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