Claude Fable 5.2 vs Qwen3.5 35B A3B
Claude Fable 5.2 averages 86.5% across five Vision Evals tasks and Qwen3.5 35B A3B 62.5% across four (#2 and #36 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 reasoning (88.1% vs 54.1%).
- Qwen3.5 35B A3B is under a tenth of the cost: $0.0016 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 31.9s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 vs Qwen3.5 35B A3B 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 35B A3B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs Qwen3.5 35B A3B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | Qwen3.5 35B A3B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 35B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $8.00 | $0.140 |
| Output $/1M | $40.00 | $1.00 |
| 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% | 62.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.031 | $0.0016 |
| Avg speed / sample | 9.33s | 31.88s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 |
| 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 | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| 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 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| 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 | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | – |
Claude Fable 5.2 vs Qwen3.5 35B A3B: 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.
The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.
Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.
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 62.5% (#36 of 67) for Qwen3.5 35B A3B. 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 5.2 leads with 88.1% against 54.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 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 31.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.