Roboflow

MiMo V2.6 Pro vs Muse Spark Open

Muse Spark Open and MiMo V2.6 Pro score 69.4% vs 50.3% averaged across five Vision Evals tasks (#25 and #56 of 65 models), so Muse Spark Open leads overall.

  • Muse Spark Open wins all 5 tasks, with its widest lead on reasoning (63.8% vs 35.1%).
  • MiMo V2.6 Pro is about a fifth of the cost: $0.0011 vs $0.0059 overall average cost per sample.
  • Muse Spark Open is faster: 7.4s vs 9.5s per sample.

Compare MiMo V2.6 Pro vs Muse Spark Open live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

MiMo V2.6 Pro vs Muse Spark Open on Vision Evals

Score by taskMiMo V2.6 ProMuse Spark Open
Reasoning
35.1
63.8
Counting
50.0
75.7
OCR
48.6
67.6
Object Detection
42.0
57.8
Identification
76.0
82.3

Roboflow Vision Evals. Full breakdown below.

MiMo V2.6 Pro vs Muse Spark Open Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyMiMo V2.6 ProMuse Spark Open
OrganizationXiaomiMeta
Categoryopenopen
Modalitymultimodalmultimodal
Release DateSep 2026Oct 2026
Context Window1.0M1.0M
Parameters1.02T total, 42B active816B total / 42B active
LicenseMITCustom
Commercial usefree for commercial useSee license terms
Pricing per 1M tokens
Input $/1M$0.435$1.25
Output $/1M$0.870$4.25
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
50.3%
69.4%
Avg cost / sample$0.0011$0.0059
Avg speed / sample9.45s7.37s
By task
Object Detection (low)
42.0%
±1.1, Mean of 3 runs, range 40.9 to 43.1
$0.0014
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)
46.7%
±0.8, Mean of 3 runs, range 45.7 to 47.3
$0.0030
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)
50.0%
±2.0, Mean of 3 runs, range 48.6 to 52.7
$0.0005
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)
59.0%
±5.4, Mean of 3 runs, range 52.7 to 63.5
$0.0016
75.2%
±4.1, Mean of 3 runs, range 71.6 to 79.7
$0.0073
Identification (low)
76.0%
±1.6, Mean of 3 runs, range 75.0 to 78.1
$0.0004
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)
78.1%
±4.7, Mean of 3 runs, range 71.9 to 81.3
$0.0012
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)
48.6%
$0.0012
67.6%
$0.0061
by category
Single value
37.0%
Transcription
68.2%
Structured JSON
67.7%
Text localization
18.7%
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)
49.9%
$0.0034
68.6%
$0.014
by category
Single value
40.4%
Transcription
59.9%
Structured JSON
70.2%
Text localization
16.8%
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)
35.1%
±2.6, Mean of 3 runs, range 32.5 to 37.8
$0.0005
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)
55.9%
±2.3, Mean of 3 runs, range 54.3 to 58.9
$0.0026
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

MiMo V2.6 Pro vs Muse Spark Open: Overview

MiMo V2.6 Pro

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.

Muse Spark Open

Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.

The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Spark Open performed better. It scores higher on all five vision tasks and averages 69.4% (#25 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.

No. On the Vision Evals Reasoning benchmark at low effort, Muse Spark Open leads with 63.8% 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 $0.0059. MiMo V2.6 Pro is priced at $0.43 per 1M input tokens and $0.87 per 1M output; Muse Spark Open 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.

Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 9.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.