Gemini 3.1 Pro vs Qwen3.8 Flash
Compare Gemini 3.1 Pro and Qwen3.8 Flash side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.1 Pro vs Qwen3.8 Flash on Vision Evals
Gemini 3.1 Pro scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.1 Pro leads 72.2% to 37.8%.
Overall, Gemini 3.1 Pro averages 83.1% (#5 of 34) against 70.3% (#16 of 34) for Qwen3.8 Flash.
Qwen3.8 Flash is cheaper ($0.0004 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 8.2s per sample).
Gemini 3.1 Pro vs Qwen3.8 Flash Comparison Table
Evals updated August 27, 2026Pricing updated August 27, 2026
| Property | Gemini 3.1 Pro | Qwen3.8 Flash |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | 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 | $2.00 | $0.150 |
| Output $/1M | $12.00 | $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 | 83.1% | 70.3% |
| Avg cost / sample | $0.0093 | $0.0004 |
| Avg speed / sample | 7.81s | 8.24s |
| By task | ||
| Object Detection | 67.6% $0.010 | 58.5% $0.0007 |
| Counting | 71.6% $0.0071 | 59.5% $0.0002 |
| Identification | 100.0% $0.0070 | 90.6% $0.0001 |
| OCR | 92.6% $0.0066 | 88.9% $0.0003 |
| Data Extraction | 94.8% $0.0063 | 86.6% $0.0002 |
| Reasoning (low) | 72.2% $0.012 | 37.8% $0.0002 |
| Reasoning (high) | 74.8% $0.021 | 68.9% $0.0011 |
Gemini 3.1 Pro vs Qwen3.8 Flash: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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 3.1 Pro performed better. It scores higher on all six vision tasks and averages 83.1% (#5 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 3.1 Pro leads with 72.2% 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 $0.0093. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 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 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 7.8s 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.