Gemini 4 Hydrogen vs GPT-6.1 Terra
Gemini 4 Hydrogen and GPT-6.1 Terra score 87.3% vs 85.8% averaged across five Vision Evals tasks (#3 and #5 of 69 models), so Gemini 4 Hydrogen leads overall.
- Gemini 4 Hydrogen wins 3 of the 5 tasks, with its widest lead on OCR (86.7% vs 81.9%).
- GPT-6.1 Terra is about a third of the cost: $0.0033 vs $0.0094 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 13.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 4 Hydrogen vs GPT-6.1 Terra live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Gemini 4 Hydrogen vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Hydrogen vs GPT-6.1 Terra Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 4 Hydrogen | GPT-6.1 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Oct 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $1.00 |
| Output $/1M | No published price | $5.00 |
| 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 |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 87.3% | 85.8% |
| Avg cost / sample | $0.0094 | $0.0033 |
| Avg speed / sample | 13.23s | 10.05s |
| By task | ||
| Object Detection (low) | 81.0% ±0.8, Mean of 3 runs, range 80.3 to 81.9 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 83.3% ±0.7, Mean of 3 runs, range 82.4 to 83.8 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 83.8% ±2.0, Mean of 3 runs, range 82.4 to 86.5 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 84.2% ±2.7, Mean of 3 runs, range 82.4 to 87.8 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 86.7% | 81.9% |
| by category |
|
|
| OCR (high) | 87.2% | 84.4% |
| by category |
|
|
| Reasoning (low) | 88.1% ±0.7, Mean of 3 runs, range 87.4 to 88.7 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 91.8% ±0.3, Mean of 3 runs, range 91.4 to 92.0 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
Gemini 4 Hydrogen vs GPT-6.1 Terra: 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-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.
The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not released.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Hydrogen performed better. It scores higher on 3 of the five vision tasks and averages 87.3% (#3 of 69) against 85.8% (#5 of 69) for GPT-6.1 Terra. 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 81.9%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.0094. Actual costs depend on your image sizes, prompts, and output length.
GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s 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.