Claude Fable 5.2 vs Qwen3.5 9b
Claude Fable 5.2 averages 86.5% across five Vision Evals tasks and Qwen3.5 9b 56.0% across four (#2 and #46 of 67 models), so Claude Fable 5.2 leads overall.
- Claude Fable 5.2 wins all 4 tasks both ran, with its widest lead on object detection (81.6% vs 38.1%).
- Qwen3.5 9b is under a tenth of the cost: $0.0021 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 41.4s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 vs Qwen3.5 9b live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Claude Fable 5.2 vs Qwen3.5 9b on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs Qwen3.5 9b Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | Qwen3.5 9b |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 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 | $8.00 | $0.170 |
| Output $/1M | $40.00 | $0.250 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 86.5% | 56.0%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.031 | $0.0021 |
| Avg speed / sample | 9.33s | 41.36s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| Object Detection (high) | 83.3% ±0.5, Mean of 3 runs, range 82.8 to 83.7 | – |
| Counting (low) | 86.5% ±0.0, Mean of 3 runs, range 86.5 to 86.5 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Counting (high) | 92.8% ±1.4, Mean of 3 runs, range 91.9 to 94.6 | – |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 94.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | – |
| OCR (low) | 79.5% | – |
| by category |
| |
| OCR (high) | 84.8% | – |
| by category |
| |
| Reasoning (low) | 88.1% ±1.3, Mean of 3 runs, range 86.8 to 89.4 | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | – |
Claude Fable 5.2 vs Qwen3.5 9b: Overview
Claude Fable 5.2 is a proprietary multimodal model from Anthropic, released on November 9, 2026. It accepts text and images and produces text, with a one million token context window and up to 128,000 output tokens. Adaptive thinking is always enabled, with an effort setting that controls reasoning. Anthropic positions Fable for demanding reasoning and long-running technical work, including coding and analysis of visual inputs.
Fable 5.2 succeeds Fable 5.1. Anthropic reports improvements in coding, reasoning, and vision, with output generation up to 30% faster than its predecessor. Fable and Mythos 5.2 share an underlying model, with additional cybersecurity and biology safeguards applied to Fable. Model weights are not publicly released.
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.
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 5.2 performed better. It scores higher on all 4 vision tasks and averages 86.5% (#2 of 67) against 56.0% (#46 of 67) 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 Object Detection benchmark at low effort, Claude Fable 5.2 leads with 81.6% against 38.1%. 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 $0.031. Actual costs depend on your image sizes, prompts, and output length.
Claude Fable 5.2 is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 41.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.