Gemini 3.6 Flash vs Muse Spark 1.3
Compare Gemini 3.6 Flash and Muse Spark 1.3 side-by-side. See how these vision models stack up in Open Prompt, Classification, Image Captioning, OCR, and Object Detection.
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Models in this comparison
Gemini 3.6 Flash vs Muse Spark 1.3 on Vision Evals
Gemini 3.6 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Data Extraction, where Gemini 3.6 Flash leads 95.9% to 88.7%.
Overall, Gemini 3.6 Flash averages 83.0% (#6 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Gemini 3.6 Flash is both cheaper ($0.0032 vs $0.0075 per sample) and faster (14.7s vs 23.1s per sample).
Gemini 3.6 Flash vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemini 3.6 Flash | Muse Spark 1.3 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $1.25 |
| Output $/1M | $3.75 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.0% | 79.8% |
| Avg cost / sample | $0.0032 | $0.0075 |
| Avg speed / sample | 14.66s | 23.14s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Gemini 3.6 Flash vs Muse Spark 1.3: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
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. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. 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, Gemini 3.6 Flash performed better. It scores higher on 4 of the six vision tasks and averages 83.0% (#6 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Data Extraction benchmark at low effort, Gemini 3.6 Flash leads with 95.9% against 88.7%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 per sample against $0.0075. Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 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.
Gemini 3.6 Flash is faster. Across Roboflow's Vision Evals it averaged 14.7s per inference against 23.1s. 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 open prompts and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.