Claude Opus 4.8 vs Qwen3.5 9b
Compare Claude Opus 4.8 and Qwen3.5 9b 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 Qwen3.5 9b on Vision Evals
Claude Opus 4.8 scores higher on 3 of the six Vision Evals tasks.
The widest gap is OCR, where Claude Opus 4.8 leads 93.8% to 79.1%.
Overall, Claude Opus 4.8 averages 68.7% (#27 of 52) against 66.1% (#33 of 52) for Qwen3.5 9b.
Qwen3.5 9b is cheaper ($0.0016 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 31.3s per sample).
Claude Opus 4.8 vs Qwen3.5 9b Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Claude Opus 4.8 | Qwen3.5 9b |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Mar 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 9B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.100 |
| Output $/1M | $25.00 | $0.150 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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% | 66.1% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.016 | $0.0016 |
| Avg speed / sample | 5.20s | 31.33s |
| By task | ||
| Object Detection | 38.6% | 45.8% |
| Counting | 54.0% | 54.0% |
| Identification | 84.4% | 84.4% |
| OCR | 93.8% | 79.1% |
| Data Extraction | 88.7% | 82.7% |
| Reasoning (low) | 53.0% | 50.3% |
| Reasoning (high) | 52.3% | – |
Claude Opus 4.8 vs Qwen3.5 9b: 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Opus 4.8 performed better. It scores higher on 3 of the six vision tasks and averages 68.7% (#27 of 52) against 66.1% (#33 of 52) for Qwen3.5 9b. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Claude Opus 4.8 leads with 93.8% against 79.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 9b is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 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 31.3s. 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.