Gemini 4 Hydrogen vs MiMo V2.6 Pro
Gemini 4 Hydrogen and MiMo V2.6 Pro score 87.3% vs 50.3% averaged across five Vision Evals tasks (#3 and #60 of 69 models), so Gemini 4 Hydrogen leads overall.
- Gemini 4 Hydrogen wins all 5 tasks, with its widest lead on reasoning (88.1% vs 35.1%).
- MiMo V2.6 Pro is about 1/9 of the cost: $0.0011 vs $0.0094 overall average cost per sample.
- MiMo V2.6 Pro is faster: 9.5s vs 13.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 4 Hydrogen vs MiMo V2.6 Pro 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 MiMo V2.6 Pro on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Hydrogen vs MiMo V2.6 Pro Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 4 Hydrogen | MiMo V2.6 Pro |
|---|---|---|
| Organization | Xiaomi | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 1.02T total, 42B active |
| License | Proprietary | MIT |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.435 |
| Output $/1M | No published price | $0.870 |
| 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% | 50.3% |
| Avg cost / sample | $0.0094 | $0.0011 |
| Avg speed / sample | 13.23s | 9.45s |
| By task | ||
| Object Detection (low) | 81.0% ±0.8, Mean of 3 runs, range 80.3 to 81.9 | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | 83.3% ±0.7, Mean of 3 runs, range 82.4 to 83.8 | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 83.8% ±2.0, Mean of 3 runs, range 82.4 to 86.5 | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | 84.2% ±2.7, Mean of 3 runs, range 82.4 to 87.8 | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 86.7% | 48.6% |
| by category |
|
|
| OCR (high) | 87.2% | 49.9% |
| by category |
|
|
| Reasoning (low) | 88.1% ±0.7, Mean of 3 runs, range 87.4 to 88.7 | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 91.8% ±0.3, Mean of 3 runs, range 91.4 to 92.0 | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
Gemini 4 Hydrogen vs MiMo V2.6 Pro: 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.
MiMo V2.6 Pro is the flagship omni-modal foundation model in Xiaomi's MiMo V2.6 series, released as open weights alongside a Flash variant and a 9B distillation of Qwen3.5. It uses a sparse mixture-of-experts transformer with 1.02 trillion total parameters and roughly 42 billion activated per token, paired with a hybrid attention design that interleaves sliding-window and global attention layers to support a context window of about one million tokens. Dedicated encoders handle non-text inputs, including a vision encoder of roughly 681 million parameters and an audio tokenizer stack, so the model accepts text, images, video, and audio and returns text.
Post-training centers on large-scale reinforcement learning across thousands of interactive environments, combined with agentic grading, self-correction cold start, and a multi-prefix multi-teacher on-policy distillation stage that extends behavior to tasks that are hard to verify automatically.
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 50.3% (#60 of 69) for MiMo V2.6 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 4 Hydrogen leads with 88.1% against 35.1%. This is the widest gap between the two models across the benchmark's tasks.
MiMo V2.6 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.0094. Actual costs depend on your image sizes, prompts, and output length.
MiMo V2.6 Pro is faster. Across Roboflow's Vision Evals it averaged 9.5s 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.