Gemini 3.1 Pro vs Qwen3.8 Max
Qwen3.8 Max and Gemini 3.1 Pro score 79.3% vs 76.8% averaged across five Vision Evals tasks (#11 and #16 of 68 models), so Qwen3.8 Max leads overall.
- Qwen3.8 Max wins 4 of the 5 tasks, with its widest lead on counting (81.1% vs 71.6%).
- Qwen3.8 Max is cheaper: $0.0080 vs $0.010 overall average cost per sample.
- Gemini 3.1 Pro is faster: 8.4s vs 18.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.1 Pro vs Qwen3.8 Max on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.1 Pro vs Qwen3.8 Max Comparison Table
Evals updated November 10, 2026Pricing updated November 11, 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 | Unknown | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | No published price |
| Output $/1M | $12.00 | No published price |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 79.3% |
| Avg cost / sample | $0.010 | $0.0080 |
| Avg speed / sample | 8.44s | 18.95s |
| By task | ||
| Object Detection (low) | 67.4% | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | – | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 71.6% | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 100.0% | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 72.6% | 74.0% |
| by category |
|
|
| OCR (high) | 73.1% | 71.8% |
| by category |
|
|
| Reasoning (low) | 72.2% | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 74.8% | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
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. 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.