Gemini 3.1 Pro vs Qwen3.8 27B
Compare Gemini 3.1 Pro and Qwen3.8 27B 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 27B on Vision Evals
Gemini 3.1 Pro scores higher on all 4 Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 85.4%.
Overall, Gemini 3.1 Pro averages 76.8% (#11 of 61) against 69.5% (#21 of 61) for Qwen3.8 27B.
Qwen3.8 27B is cheaper ($0.0009 vs $0.010 per sample), while Gemini 3.1 Pro is faster (8.4s vs 18.0s per sample).
Gemini 3.1 Pro vs Qwen3.8 27B Comparison Table
Evals updated October 8, 2026Pricing updated October 11, 2026
| Property | Gemini 3.1 Pro | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Aug 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 27.78B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.040 |
| Output $/1M | $12.00 | $2.30 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.8% | 69.5% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.010 | $0.0009 |
| Avg speed / sample | 8.44s | 17.99s |
| By task | ||
| Object Detection (low) | 67.4% | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | – | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 71.6% | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | – | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 100.0% | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 72.6% | – |
| by category |
| |
| OCR (high) | 73.1% | – |
| by category |
| |
| Reasoning (low) | 72.2% | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 74.8% | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Gemini 3.1 Pro vs Qwen3.8 27B: 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.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on all 4 vision tasks and averages 76.8% (#11 of 61) against 69.5% (#21 of 61) for Qwen3.8 27B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Identification benchmark at low effort, Gemini 3.1 Pro leads with 100.0% against 85.4%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.010. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 8.4s 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.