Gemini 2.5 Pro vs Qwen3.7 Plus
Compare Gemini 2.5 Pro and Qwen3.7 Plus side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Classification, OCR, and Image Captioning.
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Models in this comparison
Gemini 2.5 Pro vs Qwen3.7 Plus on Vision Evals
Gemini 2.5 Pro scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 33.7%.
Overall, Gemini 2.5 Pro averages 66.0% (#22 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.
Qwen3.7 Plus is cheaper ($0.0008 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 7.0s per sample).
Gemini 2.5 Pro vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Gemini 2.5 Pro | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Jun 2025 | — |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.320 |
| Output $/1M | $10.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.0% | 67.4% |
| Avg cost / sample | $0.0050 | $0.0008 |
| Avg speed / sample | 6.11s | 7.01s |
| By task | ||
| Object Detection | 33.7% $0.010 | 60.1% $0.0013 |
| Counting | 52.7% $0.0012 | 50.0% $0.0004 |
| Identification | 93.8% $0.0012 | 84.4% $0.0003 |
| OCR | 88.8% $0.0047 | 86.5% $0.0009 |
| Data Extraction | 84.5% $0.0013 | 83.5% $0.0004 |
| Reasoning (low) | 42.4% $0.0013 | 39.7% $0.0003 |
| Reasoning (high) | 62.3% $0.011 | 68.2% $0.0043 |
Gemini 2.5 Pro vs Qwen3.7 Plus: 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.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 2.5 Pro performed better. It scores higher on 5 of the six vision tasks and averages 66.0% (#22 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark, Qwen3.7 Plus leads with 60.1% against 33.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0050. Gemini 2.5 Pro is priced at $1.25 per 1M input tokens and $10.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 2.5 Pro is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 7.0s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.