Gemma 4 31B vs Qwen3 VL 30B A3B Instruct
Gemma 4 31B: 57.7% average across four Vision Evals tasks (#41 of 65 models). We have not run Qwen3 VL 30B A3B Instruct through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemma 4 31B vs Qwen3 VL 30B A3B Instruct Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Gemma 4 31B | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Oct 2025 |
| Context Window | 256K | 262K |
| Parameters | 31B | 31B |
| 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 | $0.600 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 57.7%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | – |
| Avg speed / sample | 34.36s | – |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | – |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | – |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | – |
Gemma 4 31B vs Qwen3 VL 30B A3B Instruct: 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 VL 30B A3B Instruct is an open-weight multimodal large language model developed by Alibaba as part of the Qwen family, built for instruction-following tasks that unify text generation with visual and video understanding. Released around October 2025 under the Apache-2.0 license, it targets efficient, high-fidelity vision-language reasoning across very long contexts.
The model accepts text and image inputs and produces text outputs, with strong performance in OCR, spatial reasoning, long-video understanding, and agentic or GUI-centric visual tasks. It uses a Mixture-of-Experts (A3B) design with ~31.1B total parameters and ~3B active per token, paired with Qwen3-VL’s unified multimodal stack (including Interleaved-MRoPE and DeepStack fusion) to process text, images, and video in a single architecture. OCR support expands to 32 languages, enhancing document workflows. With a native ~262K token context window (extendable further), it stands out today for its balance of scale, efficiency, long-context support, and open accessibility in multimodal systems.