Claude Opus 5 vs Muse Spark Open
Claude Opus 5 and Muse Spark Open score 70.9% vs 69.4% averaged across five Vision Evals tasks (#21 and #25 of 65 models), so Claude Opus 5 leads overall.
- Claude Opus 5 wins 3 of the 5 tasks, with its widest lead on reasoning (71.5% vs 63.8%).
- Muse Spark Open is about a third of the cost: $0.0059 vs $0.020 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 8.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Opus 5 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.
Claude Opus 5 vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5 vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Claude Opus 5 | Muse Spark Open |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Oct 2026 |
| Context Window | 1.0M | 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 | $5.00 | $1.25 |
| Output $/1M | $25.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Image Tagging | Not listed | Supported |
Both support 4 more tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 70.9% | 69.4% |
| Avg cost / sample | $0.020 | $0.0059 |
| Avg speed / sample | 8.21s | 7.37s |
| By task | ||
| Object Detection (low) | 54.4% | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | – | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 70.3% | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | – | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 90.6% | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 68.0% | 67.6% |
| by category |
|
|
| OCR (high) | 65.8% | 68.6% |
| by category |
|
|
| Reasoning (low) | 71.5% | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 74.2% | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Claude Opus 5 vs Muse Spark Open: Overview
Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at a lower cost. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.
Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.
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, Claude Opus 5 performed better. It scores higher on 3 of the five vision tasks and averages 70.9% (#21 of 65) against 69.4% (#25 of 65) for Muse Spark Open. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Identification benchmark at low effort, Claude Opus 5 leads with 90.6% against 82.3%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark Open is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0059 per sample against $0.020. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 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 8.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.