Claude Opus 5.5 vs Qwen3.8 Max
Claude Opus 5.5 and Qwen3.8 Max score 82.0% vs 79.3% averaged across five Vision Evals tasks (#4 and #9 of 65 models), so Claude Opus 5.5 leads overall.
- Claude Opus 5.5 wins 3 of the 5 tasks, with its widest lead on reasoning (83.0% vs 75.9%).
- Qwen3.8 Max is about half the cost: $0.0080 vs $0.015 overall average cost per sample.
- Claude Opus 5.5 is faster: 7.6s vs 18.9s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Opus 5.5 vs Qwen3.8 Max live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Claude Opus 5.5 vs Qwen3.8 Max on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5.5 vs Qwen3.8 Max Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Claude Opus 5.5 | Qwen3.8 Max |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.0M | 984K |
| Parameters | Unknown | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | No published price |
| Output $/1M | $20.00 | No published price |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 82.0% | 79.3% |
| Avg cost / sample | $0.015 | $0.0080 |
| Avg speed / sample | 7.63s | 18.95s |
| By task | ||
| Object Detection (low) | 74.5% ±0.5, Mean of 3 runs, range 74.0 to 75.0 | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | 76.0% ±1.1, Mean of 3 runs, range 74.7 to 77.0 | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 78.3% | 74.0% |
| by category |
|
|
| OCR (high) | 79.9% | 71.8% |
| by category |
|
|
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Claude Opus 5.5 vs Qwen3.8 Max: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
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. 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.