Gemini 3.1 Pro vs Qwen3.8 Max
Compare Gemini 3.1 Pro and Qwen3.8 Max 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 Max on Vision Evals
Qwen3.8 Max scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where Qwen3.8 Max leads 82.4% to 71.6%.
Overall, Gemini 3.1 Pro averages 83.1% (#3 of 24) against 84.0% (#2 of 24) for Qwen3.8 Max.
Qwen3.8 Max is cheaper ($0.0074 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 18.0s per sample).
Gemini 3.1 Pro vs Qwen3.8 Max Comparison Table
Evals updated August 3, 2026Pricing updated August 5, 2026
| Property | Gemini 3.1 Pro | Qwen3.8 Max |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Aug 2026 |
| Context Window | 1.0M | 984K |
| Parameters | 2.4T total, ~95B active | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $12.00 | $6.00 |
| 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% | 84.0% |
| Avg cost / sample | $0.0093 | $0.0074 |
| Avg speed / sample | 7.81s | 18.02s |
| By task | ||
| Object Detection | 67.4% $0.010 | 77.1% $0.013 |
| Counting | 71.6% $0.0071 | 82.4% $0.0046 |
| Identification | 100.0% $0.0070 | 90.6% $0.0027 |
| OCR | 92.6% $0.0066 | 92.8% $0.0056 |
| Data Extraction | 94.8% $0.0063 | 87.6% $0.0029 |
| Reasoning (low) | 72.2% $0.012 | 73.5% $0.0047 |
| Reasoning (high) | 74.8% $0.021 | 80.8% $0.011 |
Gemini 3.1 Pro vs Qwen3.8 Max: 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 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. Published figures include 86.6 on Terminal-Bench 2.1, 67.7 on SWE-bench Pro, 93.0 on PaperBench, 82.8 on IFBench, and 92.6 on GPQA Diamond. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 Max performed better. It scores higher on 4 of the six vision tasks and averages 84.0% (#2 of 24) against 83.1% (#3 of 24) for Gemini 3.1 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark, Qwen3.8 Max leads with 82.4% against 71.6%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 Max is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 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 Max is priced at $2.00 per 1M input tokens and $6.00 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 18.0s. 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.