MiMo V2.6 Flash vs Muse Spark 1.3
Muse Spark 1.3 and MiMo V2.6 Flash score 73.9% vs 49.2% averaged across five Vision Evals tasks (#20 and #60 of 68 models), so Muse Spark 1.3 leads overall.
- Muse Spark 1.3 wins all 5 tasks, with its widest lead on reasoning (73.3% vs 33.1%).
- MiMo V2.6 Flash is under a tenth of the cost: $0.0003 vs $0.0084 overall average cost per sample.
- MiMo V2.6 Flash is faster: 16.3s vs 25.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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MiMo V2.6 Flash vs Muse Spark 1.3 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
MiMo V2.6 Flash vs Muse Spark 1.3 Comparison Table
Evals updated November 10, 2026Pricing updated November 11, 2026
| Property | MiMo V2.6 Flash | Muse Spark 1.3 |
|---|---|---|
| Organization | Xiaomi | Meta |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 309B total, 15B active | Unknown |
| License | MIT | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.140 | $1.25 |
| Output $/1M | $0.280 | $4.25 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 49.2% | 73.9% |
| Avg cost / sample | $0.0003 | $0.0084 |
| Avg speed / sample | 16.34s | 25.50s |
| By task | ||
| Object Detection (low) | 37.8% ±1.1, Mean of 3 runs, range 36.4 to 38.7 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 45.0% ±2.5, Mean of 3 runs, range 42.2 to 47.1 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 49.5% ±8.1, Mean of 3 runs, range 41.9 to 58.1 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 64.9% ±1.4, Mean of 3 runs, range 63.5 to 66.2 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 76.0% ±3.1, Mean of 3 runs, range 71.9 to 78.1 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 82.3% ±4.7, Mean of 3 runs, range 78.1 to 87.5 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 49.7% | 70.4% |
| by category |
|
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| OCR (high) | 50.4% | 69.8% |
| by category |
|
|
| Reasoning (low) | 33.1% ±2.0, Mean of 3 runs, range 31.1 to 35.1 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 58.5% ±1.3, Mean of 3 runs, range 57.0 to 59.6 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
MiMo V2.6 Flash vs Muse Spark 1.3: Overview
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.
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on all five vision tasks and averages 73.9% (#20 of 68) against 49.2% (#60 of 68) for MiMo V2.6 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Muse Spark 1.3 leads with 73.3% against 33.1%. This is the widest gap between the two models across the benchmark's tasks.
MiMo V2.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0003 per sample against $0.0084. MiMo V2.6 Flash is priced at $0.14 per 1M input tokens and $0.28 per 1M output; Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
MiMo V2.6 Flash is faster. Across Roboflow's Vision Evals it averaged 16.3s per inference against 25.5s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.