Claude Opus 4.8 vs Qwen3.8 Max
Compare Claude Opus 4.8 and Qwen3.8 Max side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs Qwen3.8 Max on Vision Evals
Qwen3.8 Max scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 Max leads 76.7% to 38.6%.
Overall, Claude Opus 4.8 averages 68.7% (#28 of 53) against 83.9% (#5 of 53) for Qwen3.8 Max.
Qwen3.8 Max is cheaper ($0.0074 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 17.3s per sample).
Claude Opus 4.8 vs Qwen3.8 Max Comparison Table
Evals updated September 5, 2026Pricing updated September 19, 2026
| Property | Claude Opus 4.8 | Qwen3.8 Max |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Aug 2026 |
| Context Window | 1.0M | 984K |
| Parameters | 2.4T total, ~95B active | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | 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% | 83.9% |
| Avg cost / sample | $0.016 | $0.0074 |
| Avg speed / sample | 5.20s | 17.25s |
| By task | ||
| Object Detection (low) | 38.6% | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | – | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 54.0% | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 84.4% | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 93.8% | 93.3% ±0.5, Mean of 3 runs, range 92.8 to 93.9 |
| OCR (high) | – | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.7 |
| Data Extraction (low) | 88.7% | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Data Extraction (high) | – | 89.3% ±1.0, Mean of 3 runs, range 88.7 to 90.7 |
| Reasoning (low) | 53.0% | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 52.3% | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Claude Opus 4.8 vs Qwen3.8 Max: 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.
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. Published figures include 86.6 on Terminal-Bench 2.1, 67.7 on SWE-bench Pro, 93.0 on PaperBench, 82.8 on IFBench, and 92.6 on GPQA Diamond. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 Max performed better. It scores higher on 4 of the six vision tasks and averages 83.9% (#5 of 53) against 68.7% (#28 of 53) 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, Qwen3.8 Max leads with 76.7% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 Max is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 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 17.3s. 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 image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.