Gemini 4 Hydrogen vs Qwen3.5-27B
Gemini 4 Hydrogen averages 87.3% across five Vision Evals tasks and Qwen3.5-27B 64.1% across four (#3 and #36 of 69 models), so Gemini 4 Hydrogen leads overall.
- Gemini 4 Hydrogen wins all 4 tasks both ran, with its widest lead on object detection (81.0% vs 50.5%).
- Qwen3.5-27B is about half the cost: $0.0043 vs $0.0094 overall average cost per sample.
- Gemini 4 Hydrogen is faster: 13.2s vs 80.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Hydrogen vs Qwen3.5-27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Hydrogen vs Qwen3.5-27B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 4 Hydrogen | Qwen3.5-27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.260 |
| Output $/1M | No published price | $2.60 |
| Vision Tasks | ||
| Chart Question Answering | Not listed | Supported |
| Document Question Answering | Not listed | Supported |
| Image Tagging | Not listed | Supported |
| Multi-Label Classification | Not listed | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
Both support 6 more tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 87.3% | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0094 | $0.0043 |
| Avg speed / sample | 13.23s | 80.37s |
| By task | ||
| Object Detection (low) | 81.0% ±0.8, Mean of 3 runs, range 80.3 to 81.9 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 83.3% ±0.7, Mean of 3 runs, range 82.4 to 83.8 | – |
| Counting (low) | 83.8% ±2.0, Mean of 3 runs, range 82.4 to 86.5 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 84.2% ±2.7, Mean of 3 runs, range 82.4 to 87.8 | – |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 86.7% | – |
| by category |
| |
| OCR (high) | 87.2% | – |
| by category |
| |
| Reasoning (low) | 88.1% ±0.7, Mean of 3 runs, range 87.4 to 88.7 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 91.8% ±0.3, Mean of 3 runs, range 91.4 to 92.0 | – |
Gemini 4 Hydrogen vs Qwen3.5-27B: Overview
Gemini 4 Hydrogen is a multimodal model from Google, positioned as a successor to Gemini 3.8 Flash with stronger agent and coding capabilities at Flash-class speed.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Hydrogen performed better. It scores higher on all 4 vision tasks and averages 87.3% (#3 of 69) against 64.1% (#36 of 69) for Qwen3.5-27B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, Gemini 4 Hydrogen leads with 81.0% against 50.5%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5-27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0043 per sample against $0.0094. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Hydrogen is faster. Across Roboflow's Vision Evals it averaged 13.2s per inference against 80.4s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.