Gemini 4 Hydrogen vs GPT-5.6 Sol
Gemini 4 Hydrogen and GPT-5.6 Sol score 87.3% vs 72.4% averaged across five Vision Evals tasks (#3 and #23 of 69 models), so Gemini 4 Hydrogen leads overall.
- Gemini 4 Hydrogen wins all 5 tasks, with its widest lead on OCR (86.7% vs 63.6%).
- Gemini 4 Hydrogen is about half the cost: $0.0094 vs $0.021 overall average cost per sample.
- GPT-5.6 Sol is faster: 12.2s vs 13.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Hydrogen vs GPT-5.6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Hydrogen vs GPT-5.6 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 4 Hydrogen | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $4.40 |
| Output $/1M | No published price | $22.00 |
| 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% | 72.4% |
| Avg cost / sample | $0.0094 | $0.021 |
| Avg speed / sample | 13.23s | 12.16s |
| By task | ||
| Object Detection (low) | 81.0% ±0.8, Mean of 3 runs, range 80.3 to 81.9 | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 |
| Object Detection (high) | 83.3% ±0.7, Mean of 3 runs, range 82.4 to 83.8 | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 |
| Counting (low) | 83.8% ±2.0, Mean of 3 runs, range 82.4 to 86.5 | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 |
| Counting (high) | 84.2% ±2.7, Mean of 3 runs, range 82.4 to 87.8 | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 86.7% | 63.6% |
| by category |
|
|
| OCR (high) | 87.2% | 65.3% |
| by category |
|
|
| Reasoning (low) | 88.1% ±0.7, Mean of 3 runs, range 87.4 to 88.7 | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 |
| Reasoning (high) | 91.8% ±0.3, Mean of 3 runs, range 91.4 to 92.0 | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 |
Gemini 4 Hydrogen vs GPT-5.6 Sol: 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.
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Hydrogen performed better. It scores higher on all five vision tasks and averages 87.3% (#3 of 69) against 72.4% (#23 of 69) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Gemini 4 Hydrogen leads with 86.7% against 63.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Hydrogen is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0094 per sample against $0.021. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 12.2s per inference against 13.2s. 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.