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Claude 3.5 Haiku vs Qwen3 VL 8B Instruct

Compare Claude 3.5 Haiku and Qwen3 VL 8B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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AnthropicClaude 3.5 Haiku

Claude 3.5 Haiku is deprecated and can no longer be run. Details and evals are still available on its model page.

QwenQwen3 VL 8B Instruct
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Claude 3.5 Haiku vs Qwen3 VL 8B Instruct Comparison Table

Evals updated August 6, 2026Pricing updated August 7, 2026

PropertyClaude 3.5 HaikuQwen3 VL 8B Instruct
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2024Oct 2025
Context Window200K256K
Parameters8.8B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.117
Output $/1M$0.455
Vision Tasks
CaptioningDemo
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Multi-Label Classification
Object Detection
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Claude 3.5 Haiku vs Qwen3 VL 8B Instruct: Overview

Claude 3.5 Haiku

Claude 3.5 Haiku, released by Anthropic in October 2024, is the fastest member of the Claude 3.5 family, optimized for low-latency, high-throughput applications. It is a multimodal model that handles both text and image inputs and supports a large ~200,000-token context window. Haiku is designed to balance efficiency with intelligence, outperforming even Claude 3 Opus on several reasoning benchmarks while maintaining its hallmark speed.

Typical applications include real-time chatbots, code completion, large-scale data extraction, and content moderation—scenarios where rapid response and scalability are essential.

Qwen3 VL 8B Instruct

Qwen3 VL 8B Instruct is an open-weight multimodal vision-language model developed by Qwen / Alibaba Cloud as part of the Qwen3-VL series, designed for instruction-following tasks that combine text with visual inputs such as images and video. Released around October 2025 under the Apache-2.0 license, it targets developers who need capable multimodal reasoning without the scale or cost of very large models.

The model contains roughly 8.8 billion dense parameters and supports text, image, and video understanding with strong spatial perception, visual reasoning, and emerging visual agent abilities such as GUI interaction. A standout feature is its native ~256K token context window, extendable to around 1M tokens, enabling long-document reading and extended video comprehension. In today’s landscape, it balances openness, long-context capacity, and solid multimodal performance against heavier proprietary models. Typical applications include multimodal assistants, document and video analysis, visual question answering, and research or product prototyping where transparency and deployability matter.