Gemini 3 Flash vs Qwen3.8 Flash
Compare Gemini 3 Flash and Qwen3.8 Flash side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Qwen3.8 Flash on Vision Evals
Gemini 3 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3 Flash leads 64.9% to 37.8%.
Overall, Gemini 3 Flash averages 75.5% (#11 of 34) against 70.3% (#16 of 34) for Qwen3.8 Flash.
Qwen3.8 Flash is cheaper ($0.0004 vs $0.0021 per sample), while Gemini 3 Flash is faster (4.1s vs 8.2s per sample).
Gemini 3 Flash vs Qwen3.8 Flash Comparison Table
Evals updated August 27, 2026Pricing updated August 27, 2026
| Property | Gemini 3 Flash | Qwen3.8 Flash |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Aug 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 125B total, 6B active (+51B N-gram embeddings) | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.150 |
| Output $/1M | $3.00 | $0.470 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 75.5% | 70.3% |
| Avg cost / sample | $0.0021 | $0.0004 |
| Avg speed / sample | 4.10s | 8.24s |
| By task | ||
| Object Detection | 42.6% $0.0031 | 58.5% $0.0007 |
| Counting | 67.6% $0.0012 | 59.5% $0.0002 |
| Identification | 93.8% $0.0009 | 90.6% $0.0001 |
| OCR | 87.6% $0.0024 | 88.9% $0.0003 |
| Data Extraction | 96.9% $0.0008 | 86.6% $0.0002 |
| Reasoning (low) | 64.9% $0.0020 | 37.8% $0.0002 |
| Reasoning (high) | 74.2% $0.0040 | 68.9% $0.0011 |
Gemini 3 Flash vs Qwen3.8 Flash: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
Qwen3.8-Flash is a multimodal mixture-of-experts model from the Qwen team at Alibaba, and the production counterpart of the open-weight Qwen3.8-Flash-Next preview that introduces the architecture intended for the Qwen4 family. The main model carries 125 billion parameters alongside a separate 51 billion parameter N-gram embedding table, while activating roughly 6 billion parameters per token. It accepts interleaved image and text input and returns text, handling 262,144 tokens of context natively with extension to 1,000,000 tokens using YaRN. The production configuration runs with the 1M context window by default and adds built-in tool support.
Four architectural changes separate it from earlier Qwen releases: hybrid attention that pairs Gated DeltaNet for history compression with Qwen Sparse Attention, which uses a lightweight indexer to select micro-blocks of context; a Gated Residual scheme; N-gram embeddings; and training with the Muon optimizer, refined around orthogonalization accuracy and the division of parameters between Muon and AdamW. Qwen reports training cost around one ninth that of Qwen3.7-Plus, with QSA attention kernels measured up to 7.6 times faster in prefill and 4.9 times faster in decode at 1M-token context. Reported scores include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 84.5 on AndroidWorld and 95.7 on MathVision.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 4 of the six vision tasks and averages 75.5% (#11 of 34) against 70.3% (#16 of 34) for Qwen3.8 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3 Flash leads with 64.9% against 37.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0021. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Qwen3.8 Flash is priced at $0.15 per 1M input tokens and $0.47 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s per inference against 8.2s. 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.