Gemini 4 Hydrogen vs Gemma 4 26B A4B
Gemini 4 Hydrogen averages 87.3% across five Vision Evals tasks and Gemma 4 26B A4B 54.1% across four (#3 and #56 of 69 models), so Gemini 4 Hydrogen leads overall.
- Gemini 4 Hydrogen wins all 4 tasks both ran, with its widest lead on counting (83.8% vs 43.2%).
- Gemma 4 26B A4B is about a fifth of the cost: $0.0019 vs $0.0094 overall average cost per sample.
- Gemini 4 Hydrogen is faster: 13.2s vs 27.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Hydrogen vs Gemma 4 26B A4B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Hydrogen vs Gemma 4 26B A4B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 4 Hydrogen | Gemma 4 26B A4B |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Apr 2026 |
| Context Window | 1.0M | 256K |
| Parameters | Unknown | 25.2B |
| 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 | $0.090 |
| Output $/1M | No published price | $0.300 |
| 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% | 54.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0094 | $0.0019 |
| Avg speed / sample | 13.23s | 27.84s |
| By task | ||
| Object Detection (low) | 81.0% ±0.8, Mean of 3 runs, range 80.3 to 81.9 | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 |
| 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 | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 |
| 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 | 81.3% ±3.1, Mean of 3 runs, range 78.1 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 | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 |
| Reasoning (high) | 91.8% ±0.3, Mean of 3 runs, range 91.4 to 92.0 | – |
Gemini 4 Hydrogen vs Gemma 4 26B A4B: 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.
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
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 54.1% (#56 of 69) for Gemma 4 26B A4B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Counting benchmark at low effort, Gemini 4 Hydrogen leads with 83.8% against 43.2%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 26B A4B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0019 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 27.8s. 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.