Gemini 3.8 Flash vs Qwen3 VL 30B A3B Instruct
Gemini 3.8 Flash: 79.8% average across five Vision Evals tasks (#8 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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Gemini 3.8 Flash vs Qwen3 VL 30B A3B Instruct Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | Gemini 3.8 Flash | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Oct 2025 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 31B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.825 | $0.150 |
| Output $/1M | $4.13 | $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, pooled at low effort | ||
| Overall | 79.8% | Not evaluated |
| Avg cost / sample | $0.0045 | – |
| Avg speed / sample | 12.76s | – |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | – |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | – |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | – |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | – |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | – |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 72.8% | – |
| by category |
| |
| OCR (high) | 78.7% | – |
| by category |
| |
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | – |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | – |
Gemini 3.8 Flash vs Qwen3 VL 30B A3B Instruct: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
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.