Gemini 3.8 Flash vs MiMo V2.6 Flash
Gemini 3.8 Flash and MiMo V2.6 Flash score 79.8% vs 49.2% averaged across five Vision Evals tasks (#8 and #57 of 65 models), so Gemini 3.8 Flash leads overall.
- Gemini 3.8 Flash wins all 5 tasks, with its widest lead on reasoning (81.2% vs 33.1%).
- MiMo V2.6 Flash is under a tenth of the cost: $0.0003 vs $0.0045 overall average cost per sample.
- Gemini 3.8 Flash is faster: 12.8s vs 16.3s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.8 Flash vs MiMo V2.6 Flash on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.8 Flash vs MiMo V2.6 Flash Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Gemini 3.8 Flash | MiMo V2.6 Flash |
|---|---|---|
| Organization | Xiaomi | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 309B total, 15B active |
| License | Proprietary | MIT |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.825 | $0.140 |
| Output $/1M | $4.13 | $0.280 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 79.8% | 49.2% |
| Avg cost / sample | $0.0045 | $0.0003 |
| Avg speed / sample | 12.76s | 16.34s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 37.8% ±1.1, Mean of 3 runs, range 36.4 to 38.7 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 45.0% ±2.5, Mean of 3 runs, range 42.2 to 47.1 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 49.5% ±8.1, Mean of 3 runs, range 41.9 to 58.1 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 64.9% ±1.4, Mean of 3 runs, range 63.5 to 66.2 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 76.0% ±3.1, Mean of 3 runs, range 71.9 to 78.1 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 82.3% ±4.7, Mean of 3 runs, range 78.1 to 87.5 |
| OCR (low) | 72.8% | 49.7% |
| by category |
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| OCR (high) | 78.7% | 50.4% |
| by category |
|
|
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 33.1% ±2.0, Mean of 3 runs, range 31.1 to 35.1 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 58.5% ±1.3, Mean of 3 runs, range 57.0 to 59.6 |
Gemini 3.8 Flash vs MiMo V2.6 Flash: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
MiMo-V2.6-Flash is the efficiency-oriented checkpoint of Xiaomi's MiMo-V2.6 series, a natively omnimodal foundation model that accepts text, image, video, and audio in a single model and supports a one million token context window. The language backbone is a sparse mixture-of-experts transformer with roughly 309 billion total parameters and 15 billion activated per token, organized as 48 layers with 256 routed experts and top-8 routing. It uses a hybrid attention scheme that interleaves sliding window attention with global attention layers to cut key-value cache cost on long sequences, and pairs the backbone with a vision encoder, an audio encoder, and an audio tokenizer, plus a multi-token prediction module and a draft model for faster decoding.
Training emphasizes large scale reinforcement learning on verifiable, long-horizon tasks, with RL compute, environment diversity, and grader compute scaled together in a single mixed run.