GPT-5.4 Mini vs Muse Spark Open
Muse Spark Open and GPT-5.4 Mini score 69.4% vs 53.2% averaged across five Vision Evals tasks (#25 and #53 of 65 models), so Muse Spark Open leads overall.
- Muse Spark Open wins 4 of the 5 tasks, with its widest lead on object detection (57.8% vs 15.8%).
- GPT-5.4 Mini is about half the cost: $0.0034 vs $0.0059 overall average cost per sample.
- GPT-5.4 Mini is faster: 5.5s vs 7.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-5.4 Mini 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.
GPT-5.4 Mini vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.4 Mini vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-5.4 Mini | Muse Spark Open |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Oct 2026 |
| Context Window | 400K | 1.0M |
| Parameters | Unknown | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $1.25 |
| Output $/1M | $4.50 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 53.2% | 69.4% |
| Avg cost / sample | $0.0034 | $0.0059 |
| Avg speed / sample | 5.51s | 7.37s |
| By task | ||
| Object Detection (low) | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 51.1% | 67.6% |
| by category |
|
|
| OCR (high) | 55.8% | 68.6% |
| by category |
|
|
| Reasoning (low) | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
GPT-5.4 Mini vs Muse Spark Open: Overview
GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.
Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
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 4 of the five vision tasks and averages 69.4% (#25 of 65) against 53.2% (#53 of 65) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Muse Spark Open leads with 57.8% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0034 per sample against $0.0059. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 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.
GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.5s per inference against 7.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.