Claude Haiku 5.5 vs Muse Spark Open
Claude Haiku 5.5 and Muse Spark Open score 69.6% vs 69.4% averaged across five Vision Evals tasks (#23 and #25 of 65 models), so they are effectively tied.
- Claude Haiku 5.5 wins 3 of the 5 tasks, with its widest lead on object detection (65.8% vs 57.8%).
- Claude Haiku 5.5 is under a tenth of the cost: $0.0006 vs $0.0059 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 11.0s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Haiku 5.5 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 Haiku 5.5 vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Haiku 5.5 vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Claude Haiku 5.5 | Muse Spark Open |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | undisclosed | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $1.25 |
| Output $/1M | $0.500 | $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 | 69.6% | 69.4% |
| Avg cost / sample | $0.0006 | $0.0059 |
| Avg speed / sample | 11.01s | 7.37s |
| By task | ||
| Object Detection (low) | 65.8% ±0.6, Mean of 3 runs, range 65.1 to 66.2 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 68.2% ±1.0, Mean of 3 runs, range 67.2 to 69.2 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 68.9% ±4.7, Mean of 3 runs, range 64.9 to 74.3 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 73.0% ±1.3, Mean of 3 runs, range 71.6 to 74.3 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 83.3% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 61.2% | 67.6% |
| by category |
|
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| OCR (high) | 65.3% | 68.6% |
| by category |
|
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| Reasoning (low) | 68.9% ±2.0, Mean of 3 runs, range 66.9 to 70.9 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 74.8% ±2.6, Mean of 3 runs, range 72.2 to 77.5 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
Claude Haiku 5.5 vs Muse Spark Open: Overview
Claude Haiku 5.5 is a proprietary multimodal language model from Anthropic and the smallest member of the Claude 5.5 family, released on October 7, 2026 after Claude Opus 5.5 and Claude Sonnet 5.5. It accepts text and image input and returns text, with a 1M token context window and up to 128K output tokens per request. It is the first Haiku-class model with an adjustable effort parameter: adaptive thinking is on by default and the model decides how much to reason, steered by effort levels from low to max with medium as the default. Its training data cutoff is June 2026. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, which Anthropic reports is about 90% lower than Claude Haiku 4.5 for requests in that range.
Anthropic positions Haiku 5.5 for high-volume, latency-sensitive work such as classification, extraction, routing, summarization, and subagent tasks, and describes it as its fastest model to date at standard speed. It uses the same tokenizer as Claude Opus 4.7 and later models, so the same text counts as roughly 30% more tokens than on Haiku 4.5.
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 Haiku 5.5 performed better. It scores higher on 3 of the five vision tasks and averages 69.6% (#23 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 Object Detection benchmark at low effort, Claude Haiku 5.5 leads with 65.8% against 57.8%. This is the widest gap between the two models across the benchmark's tasks.
Claude Haiku 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0006 per sample against $0.0059. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 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.
Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 11.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.