Roboflow

Claude Opus 4.8 vs Qwen3.5 35B A3B

Compare Claude Opus 4.8 and Qwen3.5 35B A3B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

Compare Claude Opus 4.8 vs Qwen3.5 35B A3B live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
AnthropicClaude Opus 4.8
Run to compare this model.
QwenQwen3.5 35B A3B
Run to compare this model.

Models in this comparison

Claude Opus 4.8 vs Qwen3.5 35B A3B on Vision Evals

Claude Opus 4.8 scores higher on 3 of the six Vision Evals tasks.

The widest gap is Object Detection, where Qwen3.5 35B A3B leads 55.9% to 38.6%.

Overall, Claude Opus 4.8 averages 68.7% (#27 of 52) against 70.3% (#24 of 52) for Qwen3.5 35B A3B.

Qwen3.5 35B A3B is cheaper ($0.0015 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 29.3s per sample).

Claude Opus 4.8Qwen3.5 35B A3B

Claude Opus 4.8 vs Qwen3.5 35B A3B Comparison Table

Evals updated September 3, 2026Pricing updated September 4, 2026

PropertyClaude Opus 4.8Qwen3.5 35B A3B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMay 2026Feb 2026
Context Window1.0M262K
Parameters35B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00$0.250
Output $/1M$25.00$1.25
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
68.7%
70.3%
Quantizationsself-hosted
FP869.0%GPTQ-INT470.3%hardware →
Avg cost / sample$0.016$0.0015
Avg speed / sample5.20s29.34s
By task
Object Detection
38.6%
$0.026
55.9%
$0
Counting
54.0%
$0.0076
62.2%
$0
Identification
84.4%
$0.0067
81.3%
$0
OCR
93.8%
$0.020
83.2%
$0
Data Extraction
88.7%
$0.0076
84.7%
$0
Reasoning (low)
53.0%
$0.0078
54.3%
$0
Reasoning (high)
52.3%
$0.0078

Claude Opus 4.8 vs Qwen3.5 35B A3B: Overview

Claude Opus 4.8

Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.

Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.

Qwen3.5 35B A3B

The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.

Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.

Frequently Asked Questions

On Roboflow's Vision Evals, Qwen3.5 35B A3B performed slightly better overall. The two split the six vision tasks 3 to 3, but Qwen3.5 35B A3B averages 70.3% (#24 of 52) against 68.7% (#27 of 52) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.5 35B A3B leads with 55.9% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.

Qwen3.5 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.016. Actual costs depend on your image sizes, prompts, and output length.

Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s per inference against 29.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.