Gemini 2.5 Pro vs MiMo V2.6 Pro
Gemini 2.5 Pro and MiMo V2.6 Pro score 57.6% vs 50.3% averaged across five Vision Evals tasks (#42 and #56 of 65 models), so Gemini 2.5 Pro leads overall.
- Gemini 2.5 Pro wins 4 of the 5 tasks, with its widest lead on OCR (65.2% vs 48.6%).
- MiMo V2.6 Pro is about a fifth of the cost: $0.0011 vs $0.0051 overall average cost per sample.
- Gemini 2.5 Pro is faster: 6.8s vs 9.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 2.5 Pro vs MiMo V2.6 Pro on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 2.5 Pro vs MiMo V2.6 Pro Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | Gemini 2.5 Pro | MiMo V2.6 Pro |
|---|---|---|
| Organization | Xiaomi | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | 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 | $1.25 | $0.435 |
| Output $/1M | $10.00 | $0.870 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.6% | 50.3% |
| Avg cost / sample | $0.0051 | $0.0011 |
| Avg speed / sample | 6.83s | 9.45s |
| By task | ||
| Object Detection (low) | 33.7% | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | – | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 52.7% | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | – | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 93.8% | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | – | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 65.2% | 48.6% |
| by category |
|
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| OCR (high) | 64.6% | 49.9% |
| by category |
|
|
| Reasoning (low) | 42.4% | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 62.3% | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
Gemini 2.5 Pro vs MiMo V2.6 Pro: Overview
Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
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 2.5 Pro performed better. It scores higher on 4 of the five vision tasks and averages 57.6% (#42 of 65) against 50.3% (#56 of 65) 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 Identification benchmark at low effort, Gemini 2.5 Pro leads with 93.8% against 76.0%. 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 $0.0051. Gemini 2.5 Pro is priced at $1.25 per 1M input tokens and $10.00 per 1M output; MiMo V2.6 Pro is priced at $0.43 per 1M input tokens and $0.87 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 2.5 Pro is faster. Across Roboflow's Vision Evals it averaged 6.8s per inference against 9.5s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.