Claude Fable 5.1 vs Qwen3.8 Max
Compare Claude Fable 5.1 and Qwen3.8 Max side-by-side. See how these vision models stack up in Object Detection, OCR, Image Captioning, Open Prompt, and Classification.
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Models in this comparison
Claude Fable 5.1 vs Qwen3.8 Max on Vision Evals
Claude Fable 5.1 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 Max leads 77.1% to 61.4%.
Overall, Claude Fable 5.1 averages 81.3% (#7 of 36) against 84.0% (#4 of 36) for Qwen3.8 Max.
Qwen3.8 Max is cheaper ($0.0074 vs $0.035 per sample), while Claude Fable 5.1 is faster (8.3s vs 18.0s per sample).
Claude Fable 5.1 vs Qwen3.8 Max Comparison Table
Evals updated September 2, 2026Pricing updated September 2, 2026
| Property | Claude Fable 5.1 | Qwen3.8 Max |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 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 | $10.00 | $2.00 |
| Output $/1M | $50.00 | $6.00 |
| 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 | 81.3% | 84.0% |
| Avg cost / sample | $0.035 | $0.0074 |
| Avg speed / sample | 8.28s | 18.02s |
| By task | ||
| Object Detection (low) | 61.4% ±0.5, Mean of 3 runs, range 61.0 to 62.0 | 77.1% |
| Object Detection (high) | 65.0% ±0.4, Mean of 3 runs, range 64.6 to 65.3 | – |
| Counting (low) | 69.4% ±2.7, Mean of 3 runs, range 66.2 to 71.6 | 82.4% |
| Counting (high) | 73.0% ±4.7, Mean of 3 runs, range 67.6 to 77.0 | – |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 90.6% |
| Identification (high) | 96.9% ±3.1, Mean of 3 runs, range 93.8 to 100.0 | – |
| OCR (low) | 94.0% ±0.4, Mean of 3 runs, range 93.6 to 94.4 | 92.8% |
| OCR (high) | 93.6% ±0.2, Mean of 3 runs, range 93.5 to 93.9 | – |
| Data Extraction (low) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 87.6% |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| Reasoning (low) | 72.0% ±1.3, Mean of 3 runs, range 70.9 to 73.5 | 73.5% |
| Reasoning (high) | 73.1% ±2.0, Mean of 3 runs, range 71.5 to 75.5 | 80.8% |
Claude Fable 5.1 vs Qwen3.8 Max: Overview
Claude Fable 5.1 is a proprietary multimodal model from Anthropic in the Mythos-class tier of the Claude 5 family, positioned above Claude Opus for demanding reasoning and long-horizon agentic work. It accepts text and images as input and returns text, with a one million token context window and a maximum output of 128 thousand tokens. Adaptive thinking is always on, and an effort parameter controls how much reasoning the model applies to a given request. Anthropic reports a reliable knowledge and training data cutoff of June 2026. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model; the difference between them is the set of safety classifiers applied to dual-use cybersecurity and biology requests.
On the vision side, Anthropic documents improvements in reading dense charts, financial filings, and tables nested inside PDF documents, which extends the model toward document understanding, chart question answering, and spreadsheet and slide work. Reported evaluations cover agentic scientific research on Terminal-Bench-Science 0.1, agentic coding on Terminal-Bench 4.0, computer use on OSWorld 2.0, and multidisciplinary reasoning on Humanity's Last Exam. Model weights are not published.
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 slightly better overall. The two split the six vision tasks 3 to 3, but Qwen3.8 Max averages 84.0% (#4 of 36) against 81.3% (#7 of 36) for Claude Fable 5.1. 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 77.1% against 61.4%. 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 $0.035. Claude Fable 5.1 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Qwen3.8 Max is priced at $2.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Fable 5.1 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 18.0s. 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 object detection and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.