Claude Opus 4.8 vs Qwen3 VL 235B A22B Instruct
Compare Claude Opus 4.8 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs Qwen3 VL 235B A22B Instruct on Vision Evals
Claude Opus 4.8 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3 VL 235B A22B Instruct leads 42.3% to 18.6%.
Overall, Claude Opus 4.8 averages 64.8% (#15 of 16) against 66.4% (#10 of 16) for Qwen3 VL 235B A22B Instruct.
Qwen3 VL 235B A22B Instruct is cheaper ($0.0007 vs $0.013 per sample), while Claude Opus 4.8 is faster (4.2s vs 8.2s per sample).
Claude Opus 4.8 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 20, 2026
| Property | Claude Opus 4.8 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 235B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.210 |
| Output $/1M | $25.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 64.8% | 66.4% |
| Object Detection | 18.6% | 42.3% |
| Counting | 52.7% | 47.3% |
| Identification | 75.0% | 90.6% |
| OCR | 93.8% | 88.1% |
| Data Extraction | 87.6% | 86.6% |
| Reasoning | 60.9% | 43.5% |
| Avg cost / sample | $0.013 | $0.0007 |
| Avg speed / sample | 4.2s | 8.2s |
Claude Opus 4.8 vs Qwen3 VL 235B A22B Instruct: Overview
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 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.