Gemini 3.5 Flash-Lite vs Muse Spark 1.1
Compare Gemini 3.5 Flash-Lite and Muse Spark 1.1 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Gemini 3.5 Flash-Lite vs Muse Spark 1.1 on Vision Evals
Muse Spark 1.1 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.1 leads 74.2% to 48.3%.
Overall, Gemini 3.5 Flash-Lite averages 70.3% (#23 of 52) against 80.5% (#8 of 52) for Muse Spark 1.1.
Gemini 3.5 Flash-Lite is both cheaper ($0.0014 vs $0.0067 per sample) and faster (2.7s vs 7.1s per sample).
Gemini 3.5 Flash-Lite vs Muse Spark 1.1 Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Gemini 3.5 Flash-Lite | Muse Spark 1.1 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $1.25 |
| Output $/1M | $2.50 | $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 | 70.3% | 80.5% |
| Avg cost / sample | $0.0014 | $0.0067 |
| Avg speed / sample | 2.70s | 7.07s |
| By task | ||
| Object Detection (low) | 57.5% | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 |
| Object Detection (high) | – | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 |
| Counting (low) | 52.7% | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Counting (high) | – | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 |
| Identification (low) | 84.4% | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 87.4% | 92.6% ±0.5, Mean of 3 runs, range 92.1 to 93.1 |
| OCR (high) | – | 92.8% ±0.3, Mean of 3 runs, range 92.6 to 93.2 |
| Data Extraction (low) | 91.8% | 87.6% ±1.0, Mean of 3 runs, range 86.6 to 88.7 |
| Data Extraction (high) | – | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Reasoning (low) | 48.3% | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 |
| Reasoning (high) | 68.9% | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 |
Gemini 3.5 Flash-Lite vs Muse Spark 1.1: Overview
Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.
On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.
Muse Spark 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.
Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.1 performed better. It scores higher on 5 of the six vision tasks and averages 80.5% (#8 of 52) against 70.3% (#23 of 52) for Gemini 3.5 Flash-Lite. 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 1.1 leads with 74.2% against 48.3%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.5 Flash-Lite is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0014 per sample against $0.0067. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 per 1M output; Muse Spark 1.1 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 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 2.7s per inference against 7.1s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.