Claude Opus 4.8 vs Qwen3.8 27B
Qwen3.8 27B averages 69.5% across four Vision Evals tasks and Claude Opus 4.8 58.3% across five (#28 and #44 of 69 models), so Qwen3.8 27B leads overall.
- Qwen3.8 27B wins all 4 tasks both ran, with its widest lead on object detection (65.7% vs 38.6%).
- Qwen3.8 27B is under a tenth of the cost: $0.0009 vs $0.021 overall average cost per sample.
- Claude Opus 4.8 is faster: 7.7s vs 18.0s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Opus 4.8 vs Qwen3.8 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 4.8 vs Qwen3.8 27B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Claude Opus 4.8 | Qwen3.8 27B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Aug 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 27.78B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.150 |
| Output $/1M | $25.00 | $1.88 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 58.3% | 69.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.021 | $0.0009 |
| Avg speed / sample | 7.66s | 17.99s |
| By task | ||
| Object Detection (low) | 38.6% | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | – | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 54.0% | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | – | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 84.4% | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 61.5% | – |
| by category |
| |
| OCR (high) | 62.1% | – |
| by category |
| |
| Reasoning (low) | 53.0% | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 52.3% | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Claude Opus 4.8 vs Qwen3.8 27B: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 27B performed better. It scores higher on all 4 vision tasks and averages 69.5% (#28 of 69) against 58.3% (#44 of 69) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.8 27B leads with 65.7% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.021. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 7.7s per inference against 18.0s. 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 image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.