Claude Haiku 4.5 vs Qwen3.5 397B A17B

Compare Claude Haiku 4.5 and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

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AnthropicClaude Haiku 4.5
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QwenQwen3.5 397B A17B
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Claude Haiku 4.5 vs Qwen3.5 397B A17B: Overview

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.

Qwen3.5 397B A17B

Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.

As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.

Claude Haiku 4.5 vs Qwen3.5 397B A17B Comparison Table

PropertyClaude Haiku 4.5Qwen3.5 397B A17B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2025Feb 2026
Context Window200K262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.00$0.385
Output $/1M$5.00$2.45
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
58.21%
Avg Response Time56.61s
Median input tokensincl. image tokens1.1K
Median output tokens54
Est. cost / taskon this benchmark$0.0006
Defect Detection
66.7%(10/15)
Document Understanding
77.8%(7/9)
Object Counting
20%(2/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
OCR
Overall Score
68.56%
Avg Response Time7.45s
Median input tokensincl. image tokens122
Median output tokens20
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
57.6%(57/99)
Handwritten Math
80%(8/10)
License Plate Recognition
100%(30/30)
Text Recognition
70%(21/30)
VQA & Extraction
68.3%(41/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology