Claude Fable 5 vs Qwen-VL
Compare Claude Fable 5 and Qwen-VL side-by-side.
Compare Claude Fable 5 vs Qwen-VL live
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Models in this comparison
Claude Fable 5 vs Qwen-VL: Overview
Claude Fable 5 is Anthropic's first generally available Mythos-class large language model, released on June 9, 2026. It is built for long-horizon, asynchronous, and agentic tasks that prior Claude generations could not sustain, including multi-day autonomous coding sessions, complex knowledge work, and document-heavy analysis. The model supports a 1 million token context window with up to 128,000 output tokens per request and uses adaptive thinking as its sole reasoning mode, where the effort level is adjustable but raw chain-of-thought is never returned. Vision capabilities allow the model to parse diagrams, charts, and tables embedded in files and PDFs, and to use visual feedback to evaluate its own coding outputs against design goals. On benchmarks such as SWE-Bench Pro, the model scores 80.3% compared to 69.2% for Claude Opus 4.8, and it leads on CursorBench 3.1 for autonomous coding workflows.
Claude Fable 5 shares the same underlying model weights as Claude Mythos 5, but is deployed with safety classifiers that automatically reroute queries in high-risk domains — including cybersecurity, biology, and chemistry — to Claude Opus 4.8. These classifiers trigger in fewer than 5% of sessions on average. As a designated Covered Model, all traffic is subject to mandatory 30-day data retention to support safety monitoring. The model is available via the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry. Anthropic has not publicly disclosed parameter count, architecture details, or training data composition for this model.
Qwen-VL is a large vision-language model released in August 2023 by the Qwen team at Alibaba Cloud. Built on the 7-billion-parameter Qwen language model with an added visual receptor based on Openclip ViT-bigG, the model accepts images, text, and bounding box coordinates as inputs, and can produce both text and bounding boxes as outputs. Qwen-VL processes images at 448×448 resolution, higher than the 224×224 input used by many contemporaneous vision-language models, which supports finer-grained visual recognition and text-heavy tasks such as OCR. This design supports a range of multimodal tasks in a single model, including image captioning, visual question answering, visual grounding, text recognition, and image-conditioned dialogue, with native support for English, Chinese, and multilingual conversation.
At release, Qwen-VL achieved competitive results against contemporaneous vision-language models across zero-shot captioning, general VQA, text-oriented VQA, and referring expression comprehension benchmarks. A chat-tuned variant, Qwen-VL-Chat, is optimized for interactive use with instruction-following and multi-turn conversation. The model is distributed under the Tongyi Qianwen License, a custom license from Alibaba Cloud with specific terms that should be reviewed prior to commercial use. Qwen-VL is the first generation of Alibaba's open multimodal series and precedes the later Qwen2-VL and Qwen2.5-VL releases.
Claude Fable 5 vs Qwen-VL Comparison Table
| Property | Claude Fable 5 | Qwen-VL |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Aug 2023 |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | |
| Output $/1M | $50.00 | |
| Vision Tasks | ||
| Captioning | ||
| Vision Language | ||
| Visual Question Answering | ||
| Chart Question Answering | ||
| classification | ||
| Document Question Answering | ||
| Object Detection | ||
| OCR | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 74.63% | |
| Avg Response Time | 16.44s | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 30%(3/10) | |
| Object Understanding | 100%(14/14) | |
| Spatial Understanding | 78.9%(15/19) | |