Gemini 2.5 Pro vs Qwen3.8 27B
Compare Gemini 2.5 Pro and Qwen3.8 27B 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 Qwen3.8 27B on Vision Evals
Qwen3.8 27B scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 27B leads 65.7% to 33.9%.
Overall, Gemini 2.5 Pro averages 66.0% (#40 of 61) against 74.7% (#22 of 61) for Qwen3.8 27B.
Qwen3.8 27B is cheaper ($0.0009 vs $0.0050 per sample), while Gemini 2.5 Pro is faster (6.1s vs 18.0s per sample).
Gemini 2.5 Pro vs Qwen3.8 27B Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Gemini 2.5 Pro | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Aug 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 27.78B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $0.025 |
| Output $/1M | $10.00 | $4.35 |
| 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 | 66.0% | 74.7% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0050 | $0.0009 |
| Avg speed / sample | 6.11s | 17.99s |
| By task | ||
| Object Detection (low) | 33.9% | 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) | 52.7% | 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) | 93.8% | 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) | 88.8% | 92.2% ±1.2, Mean of 3 runs, range 91.1 to 93.4 |
| OCR (high) | – | 91.5% ±1.4, Mean of 3 runs, range 90.1 to 92.9 |
| Data Extraction (low) | 84.5% | 78.0% ±1.0, Mean of 3 runs, range 77.3 to 79.4 |
| Data Extraction (high) | – | 80.8% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 42.4% | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 62.3% | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Gemini 2.5 Pro vs Qwen3.8 27B: 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.
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, Qwen3.8 27B performed better. It scores higher on 4 of the six vision tasks and averages 74.7% (#22 of 61) against 66.0% (#40 of 61) for Gemini 2.5 Pro. 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 at low effort, Qwen3.8 27B leads with 65.7% against 33.9%. 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 $0.0050. 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 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.