Gemma 4 31B vs Qwen3.8 27B
Qwen3.8 27B and Gemma 4 31B score 69.5% vs 57.7% averaged across four Vision Evals tasks (#28 and #45 of 69 models), so Qwen3.8 27B leads overall.
- Qwen3.8 27B wins all 4 tasks, with its widest lead on object detection (65.7% vs 47.5%).
- Qwen3.8 27B is about half the cost: $0.0009 vs $0.0015 overall average cost per sample.
- Qwen3.8 27B is faster: 18.0s vs 34.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 31B vs Qwen3.8 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 31B vs Qwen3.8 27B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 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 |
| Commercial use | free for commercial use | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.150 |
| Output $/1M | $0.400 | $1.88 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.7%* | 69.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0009 |
| Avg speed / sample | 34.36s | 17.99s |
| By task | ||
| Object Detection (low) | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | – | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | – | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR | – | – |
| by category | ||
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | – | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 27B performed better. It scores higher on all 4 vision tasks and averages 69.5% (#28 of 69) against 57.7% (#45 of 69) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.8 27B leads with 65.7% against 47.5%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.0015. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.8 27B is faster. Across Roboflow's Vision Evals it averaged 18.0s per inference against 34.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.