Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct
Gemini 2.5 Pro and Qwen3 VL 235B A22B Instruct score 57.6% vs 55.2% averaged across five Vision Evals tasks (#46 and #54 of 69 models), so Gemini 2.5 Pro leads overall.
- Gemini 2.5 Pro wins 4 of the 5 tasks, but the widest gap is object detection, where Qwen3 VL 235B A22B Instruct leads 52.1% vs 33.7%.
- Qwen3 VL 235B A22B Instruct is about a fifth of the cost: $0.0011 vs $0.0051 overall average cost per sample.
- Gemini 2.5 Pro is faster: 6.8s vs 15.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 2.5 Pro | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | Unknown | 235B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.210 |
| Output $/1M | $10.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.6% | 55.2% |
| Avg cost / sample | $0.0051 | $0.0011 |
| Avg speed / sample | 6.83s | 15.87s |
| By task | ||
| Object Detection | 33.7% | 52.1% |
| Counting | 52.7% | 47.3% |
| Identification | 93.8% | 90.6% |
| OCR (low) | 65.2% | 56.4% |
| by category |
|
|
| OCR (high) | 64.6% | 55.5% |
| by category |
|
|
| Reasoning (low) | 42.4% | 29.8% |
| Reasoning (high) | 62.3% | 33.8% |
Gemini 2.5 Pro vs Qwen3 VL 235B A22B Instruct: Overview
Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.