Claude Sonnet 5 vs Muse Spark 1.3
Compare Claude Sonnet 5 and Muse Spark 1.3 side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.3 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.3 leads 73.3% to 43.0%.
Overall, Claude Sonnet 5 averages 66.4% (#31 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Claude Sonnet 5 is both cheaper ($0.0064 vs $0.0075 per sample) and faster (4.8s vs 23.1s per sample).
Claude Sonnet 5 vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Claude Sonnet 5 | Muse Spark 1.3 |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $10.00 | $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 | 66.4% | 79.8% |
| Avg cost / sample | $0.0064 | $0.0075 |
| Avg speed / sample | 4.84s | 23.14s |
| By task | ||
| Object Detection (low) | 36.1% | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | – | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 56.8% | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | – | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 81.3% | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 91.7% | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | – | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 89.7% | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | – | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 43.0% | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 43.0% | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Claude Sonnet 5 vs Muse Spark 1.3: 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. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
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 Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on 4 of the six vision tasks and averages 79.8% (#10 of 52) against 66.4% (#31 of 52) 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 Spark 1.3 leads with 73.3% against 43.0%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0064 per sample against $0.0075. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Muse Spark 1.3 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.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 4.8s per inference against 23.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.