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Claude Haiku 5.5 vs Qwen3.7 Plus

Compare Claude Haiku 5.5 and Qwen3.7 Plus side-by-side.

Compare Claude Haiku 5.5 vs Qwen3.7 Plus 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 Qwen3.7 Plus on Vision Evals

Claude Haiku 5.5 scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Claude Haiku 5.5 leads 68.9% to 39.7%.

Overall, Claude Haiku 5.5 averages 77.2% (#20 of 61) against 67.4% (#37 of 61) for Qwen3.7 Plus.

Claude Haiku 5.5 is cheaper ($0.0005 vs $0.0008 per sample), while Qwen3.7 Plus is faster (7.0s vs 13.2s per sample).

Claude Haiku 5.5Qwen3.7 Plus

Claude Haiku 5.5 vs Qwen3.7 Plus Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyClaude Haiku 5.5Qwen3.7 Plus
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodal—
Release DateOct 2026Jun 2026
Context Window1.0M—
ParametersundisclosedUnknown
LicenseProprietaryUnknown
Pricing per 1M tokens
Input $/1M$0.100$0.320
Output $/1M$0.500$1.28
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
77.2%
67.4%
Avg cost / sample$0.0005$0.0008
Avg speed / sample13.23s7.01s
By task
Object Detection (low)
65.8%
±0.6, Mean of 3 runs, range 65.1 to 66.2
$0.0006
60.1%
$0.0013
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
50.0%
$0.0004
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
84.4%
$0.0003
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0003
–
OCR (low)
90.1%
±1.1, Mean of 3 runs, range 88.8 to 91.0
$0.0006
86.5%
$0.0009
OCR (high)
88.0%
±1.3, Mean of 3 runs, range 87.0 to 89.6
$0.0009
–
Data Extraction (low)
85.9%
±1.5, Mean of 3 runs, range 84.5 to 87.6
$0.0002
83.5%
$0.0004
Data Extraction (high)
87.3%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0002
–
Reasoning (low)
68.9%
±2.0, Mean of 3 runs, range 66.9 to 70.9
$0.0004
39.7%
$0.0003
Reasoning (high)
74.8%
±2.6, Mean of 3 runs, range 72.2 to 77.5
$0.0006
68.2%
$0.0043

Claude Haiku 5.5 vs Qwen3.7 Plus: 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.

Qwen3.7 Plus
No description available

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Haiku 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 77.2% (#20 of 61) against 67.4% (#37 of 61) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Haiku 5.5 leads with 68.9% against 39.7%. 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 $0.0005 per sample against $0.0008. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.0s per inference against 13.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.