Claude Opus 5.5 vs Qwen3.5 35B A3B
Compare Claude Opus 5.5 and Qwen3.5 35B A3B side-by-side.
Compare Claude Opus 5.5 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.
Models in this comparison
Claude Opus 5.5 vs Qwen3.5 35B A3B on Vision Evals
Claude Opus 5.5 scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 54.1%.
Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 69.4% (#28 of 57) for Qwen3.5 35B A3B.
Qwen3.5 35B A3B is cheaper ($0.0016 vs $0.014 per sample), while Claude Opus 5.5 is faster (12.8s vs 31.9s per sample).
Claude Opus 5.5 vs Qwen3.5 35B A3B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 5.5 | Qwen3.5 35B A3B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 35B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $0.313 |
| Output $/1M | $20.00 | $1.25 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 85.5% | 69.4% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.014 | $0.0016 |
| Avg speed / sample | 12.76s | 31.88s |
| By task | ||
| Object Detection (low) | 74.4% ±0.5, Mean of 3 runs, range 73.9 to 74.8 | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 |
| Object Detection (high) | 76.8% ±1.2, Mean of 3 runs, range 75.4 to 77.8 | – |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | – |
| OCR (low) | 87.8% ±0.6, Mean of 3 runs, range 87.0 to 88.2 | 83.0% ±0.4, Mean of 3 runs, range 82.7 to 83.5 |
| OCR (high) | 87.2% ±0.6, Mean of 3 runs, range 86.5 to 87.8 | – |
| Data Extraction (low) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 83.5% ±2.6, Mean of 3 runs, range 80.4 to 85.6 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | – |
Claude Opus 5.5 vs Qwen3.5 35B A3B: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
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 Opus 5.5 performed better. It scores higher on all six vision tasks and averages 85.5% (#3 of 57) against 69.4% (#28 of 57) 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 Opus 5.5 leads with 83.0% 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 $0.014. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 12.8s per inference against 31.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.