Gemma 4 31B vs Qwen3.8 27B
Compare Gemma 4 31B and Qwen3.8 27B side-by-side.
Compare Gemma 4 31B vs Qwen3.8 27B live
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Models in this comparison
Gemma 4 31B vs Qwen3.8 27B Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Gemma 4 31B | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Aug 2026 |
| Context Window | 256K | 262K |
| Parameters | 31B | 27.78B |
| License | Apache 2.0 | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.450 |
| Output $/1M | $0.340 | $3.20 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 61.2% |
| Avg cost / sample | – | $0.0018 |
| Avg speed / sample | – | 7.33s |
| By task | ||
| Object Detection | – | 54.5% $0.0036 |
| Counting | – | 41.9% $0.0005 |
| Identification | – | 78.1% $0.0005 |
| OCR | – | 81.4% $0.0019 |
| Data Extraction | – | 79.4% $0.0005 |
| Reasoning (low) | – | 31.8% $0.0005 |
| Reasoning (high) | – | 62.3% $0.0087 |
Gemma 4 31B vs Qwen3.8 27B: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.