Gemma 4 26B (MoE) vs Qwen 3 32B: Which is Better in 2026?
Gemma 4 26B (MoE) (Google, 25.2B parameters) and Qwen 3 32B (Alibaba, 32B 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, Gemma 4 26B (MoE) leads on 4 of 4 shared evaluation tasks. Qwen 3 32B remains competitive, particularly in areas aligned with its training focus. For general-purpose quality, Gemma 4 26B (MoE) 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 | Gemma 4 26B (MoE) | Qwen 3 32B | Winner |
|---|---|---|---|
| MMLU-Pro | 82.6 | 81.2 | Gemma 4 26B (MoE) |
| GPQA Diamond | 82.3 | 63.3 | Gemma 4 26B (MoE) |
| LiveCodeBench | 77.1 | 60.3 | Gemma 4 26B (MoE) |
| AIME 2024–25 (Epoch AI) | 82.2 | 66.9 | Gemma 4 26B (MoE) |
| HumanEval | — | 95.2 | Qwen 3 32B |
| Arena Elo (Text) | — | 1340 | Qwen 3 32B |
| Aider Polyglot | — | 40 | Qwen 3 32B |
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 | Gemma 4 26B (MoE) | Qwen 3 32B |
|---|---|---|
| Coding | A | A |
| Math | A | A |
| Content Writing | A | B |
| Reasoning | A | A |
| Studying | A | A |
| Chat / Conversation | A | B |
| Summarization | B | B |
| Agents / Tool Use | A | B |
| Vision / Multimodal | B | C |
| Data Analysis | B | B |
| Hindi / Multilingual | C | C |
| Interview Prep | A | B |
| overall | A | A |
Grade scale: S = Exceptional A = Strong B = Good C = Fair D = Weak
Key Differences
- Gemma 4 26B (MoE) is an open-weight model you can download and self-host; Qwen 3 32B is available exclusively through Alibaba's API, which means no model weights are publicly released.
- In terms of raw scale, Qwen 3 32B (32B parameters) is significantly larger than Gemma 4 26B (MoE) (25.2B parameters). Larger parameter counts often correlate with stronger reasoning, though efficiency improvements mean smaller models can punch above their weight.
- For graduate-level science and reasoning (GPQA Diamond), Gemma 4 26B (MoE) leads with 82.3%, indicating stronger performance on expert-level knowledge tasks.
Which Should You Choose?
Choose Gemma 4 26B (MoE) if your primary use cases involve overall, Coding, Math. Gemma 4 26B (MoE) scores highly on these tasks and is especially well-suited for teams that need consistent, high-quality outputs at scale through Google's API.
Choose Qwen 3 32B if your work centres on overall, Coding, Math, and self-hosting a 32B-parameter model is feasible for your infrastructure. It offers a strong value proposition for those use cases.
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.
Related Comparisons
Related Tools