Claude Sonnet 5 vs Qwen3.8 27B
Qwen3.8 27B averages 69.5% across four Vision Evals tasks and Claude Sonnet 5 55.3% across five (#28 and #53 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 36.1%).
- Qwen3.8 27B is about 1/8 of the cost: $0.0009 vs $0.0074 overall average cost per sample.
- Claude Sonnet 5 is faster: 5.9s vs 18.0s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Sonnet 5 vs Qwen3.8 27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Sonnet 5 vs Qwen3.8 27B Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Claude Sonnet 5 | Qwen3.8 27B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 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 | $2.00 | $0.150 |
| Output $/1M | $10.00 | $1.88 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.3% | 69.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0074 | $0.0009 |
| Avg speed / sample | 5.86s | 17.99s |
| By task | ||
| Object Detection (low) | 36.1% | 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) | 56.8% | 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) | 81.3% | 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) | 59.4% | – |
| by category |
| |
| OCR (high) | 59.5% | – |
| by category |
| |
| Reasoning (low) | 43.0% | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 43.0% | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
Claude Sonnet 5 vs Qwen3.8 27B: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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 55.3% (#53 of 69) for Claude Sonnet 5. 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 36.1%. 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 $0.0074. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 5.9s 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.