Claude Fable 5.1 vs Qwen3.5 9b
Compare Claude Fable 5.1 and Qwen3.5 9b side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
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Models in this comparison
Claude Fable 5.1 vs Qwen3.5 9b on Vision Evals
Claude Fable 5.1 scores higher on all 4 Vision Evals tasks.
The widest gap is Reasoning, where Claude Fable 5.1 leads 72.0% to 45.9%.
Overall, Claude Fable 5.1 averages 74.7% (#14 of 63) against 56.0% (#42 of 63) for Qwen3.5 9b.
Qwen3.5 9b is cheaper ($0.0021 vs $0.039 per sample), while Claude Fable 5.1 is faster (8.3s vs 41.4s per sample).
Claude Fable 5.1 vs Qwen3.5 9b Comparison Table
Evals updated October 13, 2026Pricing updated October 18, 2026
| Property | Claude Fable 5.1 | Qwen3.5 9b |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 9B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $0.100 |
| Output $/1M | $50.00 | $0.150 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Supported |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 74.7% | 56.0% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.039 | $0.0021 |
| Avg speed / sample | 8.35s | 41.36s |
| By task | ||
| Object Detection (low) | 61.4% ±0.5, Mean of 3 runs, range 61.0 to 62.0 | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| 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 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| 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 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±3.1, Mean of 3 runs, range 93.8 to 100.0 | – |
| OCR (low) | 72.9% | – |
| by category |
| |
| OCR (high) | 75.3% | – |
| by category |
| |
| Reasoning (low) | 72.0% ±1.3, Mean of 3 runs, range 70.9 to 73.5 | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 73.1% ±2.0, Mean of 3 runs, range 71.5 to 75.5 | – |
Claude Fable 5.1 vs Qwen3.5 9b: 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.
Qwen3.5-9B 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 Fable 5.1 performed better. It scores higher on all 4 vision tasks and averages 74.7% (#14 of 63) against 56.0% (#42 of 63) 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 Reasoning benchmark at low effort, Claude Fable 5.1 leads with 72.0% against 45.9%. 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 $0.0021 per sample against $0.039. 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 41.4s. 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 OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.