Gemini 2.5 Flash-Lite vs Muse Spark 1.1
Compare Gemini 2.5 Flash-Lite and Muse Spark 1.1 side-by-side. See how these vision models stack up in Image Captioning, Object Detection, OCR, Open Prompt, and Classification.
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Gemini 2.5 Flash-Lite vs Muse Spark 1.1 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemini 2.5 Flash-Lite | Muse Spark 1.1 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $1.25 |
| Output $/1M | $0.400 | $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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 80.5% |
| Avg cost / sample | – | $0.0067 |
| Avg speed / sample | – | 7.07s |
| By task | ||
| Object Detection (low) | – | 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) | – | 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) | – | 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) | – | 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) | – | 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) | – | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 |
| Reasoning (high) | – | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 |
Gemini 2.5 Flash-Lite vs Muse Spark 1.1: Overview
Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.
Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.
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
Gemini 2.5 Flash-Lite has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.