# Qwen3.8-27B vs. Gemma (same class) "Same class" here means the open-weight, dense, local-deployable multimodal models in the ~27–31B range. The two relevant Gemma models are **Gemma 3 27B** (the exact same parameter count) and **Gemma 4 31B** (Google's current-generation flagship dense model in this class). Here's the comparison. ## Quick spec comparison | Spec | **Qwen3.8-27B** | **Gemma 3 27B** | **Gemma 4 31B** | |---|---|---|---| | Developer | Alibaba/Qwen | Google DeepMind | Google DeepMind | | Release | Aug 14, 2026 | Mar 12, 2025 | Apr 2, 2026 | | Parameters | 27B dense | 27B dense | 30.7B dense | | Context window | 262K native (→1M via YaRN) | 128K | 256K | | Modality | Multimodal (text+image) | Multimodal (text+image) | Multimodal (text+image) | | License | Apache 2.0 | Gemma Terms of Use | Apache 2.0 | | Architecture | Dense | Dense, 46 layers, hidden 4096, 64Q/16KV heads | Dense, 60 layers, hidden 5376, 32Q/16KV heads, sliding-window attention | Sources: [AI Release Tracker – Qwen3.8-27B](https://aireleasetracker.com/model/qwen/qwen3.8-27b), [APX ML – Gemma 3 27B](https://apxml.com/models/gemma-3-27b), [APX ML – Gemma 4 31B](https://apxml.com/models/gemma-4-31b) ## Key differences **Context window — Qwen3.8-27B wins.** It ships with a 262K native context, extendable to 1M via YaRN, versus 128K for Gemma 3 27B and 256K for Gemma 4 31B. For long-document work, Qwen3.8-27B has the largest documented context of the three. ([OfficeChai](https://officechai.com/miscellaneous/alibaba-releases-qwen-3-8-27b-beats-muse-glimmer-30b-on-many-benchmarks/), [BenchLM comparison](https://benchlm.ai/compare/gemma-4-31b-vs-qwen3-8-27b)) **Licensing — Qwen3.8-27B and Gemma 4 31B are Apache 2.0** (permissive, commercial-friendly); Gemma 3 27B uses Google's more restrictive Gemma Terms of Use. ([APX ML – Gemma 3 27B](https://apxml.com/models/gemma-3-27b), [APX ML – Gemma 4 31B](https://apxml.com/models/gemma-4-31b)) **Positioning — Qwen3.8-27B is explicitly built for local/agentic coding and office work.** Alibaba positions it as a single-GPU-friendly "for builders" model, and it was released alongside the open-weights Qwen3.8-Max (2.4T-param MoE). Its launch benchmarks emphasize coding and agentic office tasks. ([OfficeChai](https://officechai.com/miscellaneous/alibaba-releases-qwen-3-8-27b-beats-muse-glimmer-30b-on-many-benchmarks/)) ## Benchmark comparison (Qwen3.8-27B vs. Gemma 4 31B) The only direct head-to-head data I found is on BenchLM, which notes the evidence is **sparse and not fully comparable** (different benchmark sets per category), so it does not name an overall winner. On the shared benchmarks: - **GPQA Diamond** (science reasoning): Qwen3.8-27B **89.2%** vs. Gemma 4 31B 84.3% - **Humanity's Last Exam** (hard reasoning): Qwen3.8-27B **30.8%** vs. Gemma 4 31B 26.5% - **Coding** (SWE-Bench Pro basis): Qwen3.8-27B **61.7%** vs. Gemma 4 31B 41.6% - **Multimodal**: Qwen3.8-27B **90.2%** vs. Gemma 4 31B 76.9% - **Knowledge**: Gemma 4 31B 52.9% vs. Qwen3.8-27B 38.7% (directional only) Source: [BenchLM – Gemma 4 31B vs Qwen3.8-27B](https://benchlm.ai/compare/gemma-4-31b-vs-qwen3-8-27b) ## Qwen3.8-27B headline scores (from Alibaba's release) - SWE-Bench Pro: **61.7%** · DeepSWE 1.1: **42.2%** · QwenSWEBench: **79%** · LiveCodeBench v6: **90.3%** · Terminal-Bench 2.1: **73%** · JobBench: **33.4%** · CoWorkBench: **70.7%** · GPQA Diamond: **89.2%** · HLE (no tools): **30.8%** · IFBench: **79.5%** · Agent's Last Exam: **20.4% pass@1** Sources: [AI Release Tracker – Qwen3.8-27B](https://aireleasetracker.com/model/qwen/qwen3.8-27b), [OfficeChai](https://officechai.com/miscellaneous/alibaba-releases-qwen-3-8-27b-beats-muse-glimmer-30b-on-many-benchmarks/) ## Bottom line - **Qwen3.8-27B** is the newest and most capable on paper in this class, with the largest context window (262K→1M), Apache 2.0 licensing, and strong coding/agentic-office benchmark scores — it even beats Google's current Gemma 4 31B on the shared coding, reasoning, and multimodal benchmarks BenchLM tracked. - **Gemma 4 31B** is Google's current-generation flagship dense model — slightly larger (30.7B), 256K context, Apache 2.0, and ranked #3 for coding on APX ML's leaderboard, but it trails Qwen3.8-27B on the shared head-to-head benchmarks. - **Gemma 3 27B** is the exact same parameter count but is now a generation old (Mar 2025), has a smaller 128K context, and uses the more restrictive Gemma license. Caveat: the direct Qwen3.8-27B vs. Gemma 4 31B comparison is based on sparse, partially non-comparable public evidence (BenchLM explicitly flags most category rows as "not comparable"), so treat the head-to-head numbers as directional rather than definitive. No independent third-party benchmark suite has yet published a fully matched comparison of these two models.