Claude Opus 5 vs Muse Ember
Muse Ember and Claude Opus 5 score 76.6% vs 70.9% averaged across five Vision Evals tasks (#16 and #23 of 67 models), so Muse Ember leads overall.
- Muse Ember wins all 5 tasks, with its widest lead on counting (79.7% vs 70.3%).
- Muse Ember is under a tenth of the cost: $0.0009 vs $0.020 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 8.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Opus 5 vs Muse Ember 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 Ember on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5 vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Opus 5 | Muse Ember |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Nov 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.100 |
| Output $/1M | $25.00 | $0.500 |
| 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% | 76.6% |
| Avg cost / sample | $0.020 | $0.0009 |
| Avg speed / sample | 8.21s | 6.08s |
| By task | ||
| Object Detection (low) | 54.4% | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 |
| Object Detection (high) | – | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 |
| Counting (low) | 70.3% | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | – | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Identification (low) | 90.6% | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| OCR (low) | 68.0% | 69.5% |
| by category |
|
|
| OCR (high) | 65.8% | 70.5% |
| by category |
|
|
| Reasoning (low) | 71.5% | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | 74.2% | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
Claude Opus 5 vs Muse Ember: 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 Ember is a proprietary multimodal reasoning model from Meta, released on November 9, 2026. It accepts text, images, video, audio, and PDF documents and returns text. The Meta Model API serves it as muse-ember-1.0, with a 1,048,576-token context window and up to 524,288 output tokens. Reasoning effort is adjustable from minimal to max.
Meta positions Ember for repeated tasks within larger agent workflows: tool calls, routing, coding subtasks, and processing collections of images and documents. It can caption or sort photos and extract fields from records while a larger Muse model handles planning. Meta recommends Spark or Wonder for harder single-shot coding and knowledge-heavy problems. Ember is available as a hosted model; its weights are not publicly released.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Ember performed better. It scores higher on all five vision tasks and averages 76.6% (#16 of 67) against 70.9% (#23 of 67) for Claude Opus 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark at low effort, Muse Ember leads with 79.7% against 70.3%. This is the widest gap between the two models across the benchmark's tasks.
Muse Ember is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.020. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Muse Ember is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Muse Ember is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 8.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.