Claude Opus 4 vs Qwen3.8 27B
Compare Claude Opus 4 and Qwen3.8 27B side-by-side.
Compare Claude Opus 4 vs Qwen3.8 27B live
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Models in this comparison
Claude Opus 4 vs Qwen3.8 27B Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Aug 2026 |
| Context Window | 200K | 262K |
| Parameters | 27.78B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.450 |
| Output $/1M | $75.00 | $3.20 |
| Vision Tasks | ||
| Captioning | ||
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | ||
| Vision Language | ||
| Visual Question Answering | ||
| Object Detection | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Deprecated | 61.2% |
| Avg cost / sample | – | $0.0018 |
| Avg speed / sample | – | 7.33s |
| By task | ||
| Object Detection | – | 54.5% $0.0036 |
| Counting | – | 41.9% $0.0005 |
| Identification | – | 78.1% $0.0005 |
| OCR | – | 81.4% $0.0019 |
| Data Extraction | – | 79.4% $0.0005 |
| Reasoning (low) | – | 31.8% $0.0005 |
| Reasoning (high) | – | 62.3% $0.0087 |
Claude Opus 4 vs Qwen3.8 27B: Overview
Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.
Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.
Frequently Asked Questions
Claude Opus 4 has been deprecated by its provider and can no longer be run, so it is not part of Roboflow's current Vision Evals. This page compares the models on specs, licensing, and pricing instead.
Claude Opus 4 is released under Proprietary, while Qwen3.8 27B uses Apache 2.0. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.