Claude Sonnet 5 vs Muse Ember
Muse Ember and Claude Sonnet 5 score 76.6% vs 55.3% averaged across five Vision Evals tasks (#16 and #51 of 67 models), so Muse Ember leads overall.
- Muse Ember wins all 5 tasks, with its widest lead on reasoning (78.2% vs 43.0%).
- Muse Ember is about 1/8 of the cost: $0.0009 vs $0.0074 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Sonnet 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 Sonnet 5 vs Muse Ember on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Sonnet 5 vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Sonnet 5 | Muse Ember |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 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 | $2.00 | $0.100 |
| Output $/1M | $10.00 | $0.500 |
| 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 | 55.3% | 76.6% |
| Avg cost / sample | $0.0074 | $0.0009 |
| Avg speed / sample | 5.86s | 6.08s |
| By task | ||
| Object Detection (low) | 36.1% | 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) | 56.8% | 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) | 81.3% | 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) | 59.4% | 69.5% |
| by category |
|
|
| OCR (high) | 59.5% | 70.5% |
| by category |
|
|
| Reasoning (low) | 43.0% | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | 43.0% | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
Claude Sonnet 5 vs Muse Ember: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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 55.3% (#51 of 67) for Claude Sonnet 5. 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 Ember leads with 78.2% against 43.0%. 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 $0.0074. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 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.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 5.9s per inference against 6.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.