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Claude Haiku 5.5 vs Moondream 2

Compare Claude Haiku 5.5 and Moondream 2 side-by-side.

Compare Claude Haiku 5.5 vs Moondream 2 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.

Models in this comparison

Claude Haiku 5.5 vs Moondream 2 Comparison Table

Evals updated October 13, 2026Pricing updated October 20, 2026

PropertyClaude Haiku 5.5Moondream 2
OrganizationAnthropicMoondream
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2026Jan 2024
Context Window1.0M—
Parametersundisclosed~2B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$0.100No published price
Output $/1M$0.500No published price
Vision Tasks
CaptioningDemoSupported
Object DetectionDemoSupported
Vision LanguageSupportedSupported
Visual Question AnsweringDemoSupported
Chart Question AnsweringSupportedNot listed
ClassificationDemoNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
OCRDemoNot listed
Model Features
Multimodal VisionSupportedSupported
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
69.6%
Not evaluated
Avg cost / sample$0.0006–
Avg speed / sample11.01s–
By task
Object Detection (low)
65.8%
±0.6, Mean of 3 runs, range 65.1 to 66.2
$0.0006
–
Object Detection (high)
68.2%
±1.0, Mean of 3 runs, range 67.2 to 69.2
$0.0010
–
Counting (low)
68.9%
±4.7, Mean of 3 runs, range 64.9 to 74.3
$0.0003
–
Counting (high)
73.0%
±1.3, Mean of 3 runs, range 71.6 to 74.3
$0.0004
–
Identification (low)
83.3%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0002
–
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0003
–
OCR (low)
61.2%
$0.0006
–
by category
Single value
44.8%
Transcription
93.0%
Structured JSON
82.2%
Text localization
32.0%
OCR (high)
65.3%
$0.0010
–
by category
Single value
46.5%
Transcription
94.7%
Structured JSON
85.3%
Text localization
45.8%
Reasoning (low)
68.9%
±2.0, Mean of 3 runs, range 66.9 to 70.9
$0.0004
–
Reasoning (high)
74.8%
±2.6, Mean of 3 runs, range 72.2 to 77.5
$0.0006
–

Claude Haiku 5.5 vs Moondream 2: Overview

Claude Haiku 5.5

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. On visual and agentic evaluations reported at launch, it scores 46.4% on Chartography, a chart reading benchmark, compared with 6.4% for Haiku 4.5 and 61.6% for Sonnet 5.5, and 72.4% on the offline subset of OSWorld 2.1, a screenshot driven computer use benchmark, compared with 15.7% for Haiku 4.5. 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.

Moondream 2

Moondream 2 is a small open-source vision-language model from Moondream, the company founded by Vikhyat Korrapati. It was first released in early 2024 and updated through mid-2025. At approximately 1.9 billion parameters, it is designed to run efficiently on consumer hardware such as laptops and edge devices while supporting a practical range of multimodal tasks. Moondream 2 combines a vision encoder based on SigLIP with a compact language backbone, trained for image understanding tasks rather than as a general chat model.

The model accepts an image paired with a natural language prompt and produces text responses, supporting visual question answering, image captioning, and image-conditioned dialogue. Later Moondream 2 releases added object localization through a point API that returns coordinates for queried objects, along with improvements to OCR, counting, and document understanding. Moondream 2 is distributed under the Apache 2.0 license and is available through Hugging Face and the maintainer's distribution. Because the model is updated frequently, production deployments should pin to a specific revision rather than tracking the latest release. A successor model, Moondream 3 (Preview), was released in September 2025 with a 9B mixture-of-experts architecture and 2B active parameters, offering substantially stronger visual reasoning than Moondream 2 while retaining the efficiency-focused design. A referring expression segmentation extension to Moondream 3 was released in March 2026.