GPT-5.6 Luna vs Qwen3.5 27B
Compare GPT-5.6 Luna and Qwen3.5 27B side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Object Detection, and Open Prompt.
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Models in this comparison
GPT-5.6 Luna vs Qwen3.5 27B on Vision Evals
GPT-5.6 Luna scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-5.6 Luna leads 55.0% to 31.8%.
Overall, GPT-5.6 Luna averages 71.5% (#14 of 31) against 64.3% (#26 of 31) for Qwen3.5 27B.
Qwen3.5 27B is cheaper ($0.0007 vs $0.0010 per sample), while GPT-5.6 Luna is faster (6.5s vs 7.4s per sample).
GPT-5.6 Luna vs Qwen3.5 27B Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | GPT-5.6 Luna | Qwen3.5 27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Feb 2026 |
| Context Window | 1.5M | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.195 |
| Output $/1M | $1.20 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 71.5% | 64.3% |
| Avg cost / sample | $0.0010 | $0.0007 |
| Avg speed / sample | 6.55s | 7.38s |
| By task | ||
| Object Detection | 59.9% $0.0015 | 58.8% $0.0013 |
| Counting | 66.2% $0.0007 | 54.0% $0.0002 |
| Identification | 78.1% $0.0004 | 78.1% $0.0002 |
| OCR | 88.4% $0.0012 | 84.5% $0.0009 |
| Data Extraction | 81.4% $0.0004 | 78.3% $0.0002 |
| Reasoning (low) | 55.0% $0.0006 | 31.8% $0.0002 |
| Reasoning (high) | 60.9% $0.0015 | 61.6% $0.0065 |
GPT-5.6 Luna vs Qwen3.5 27B: Overview
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-5.6 Luna performed better. It scores higher on 5 of the six vision tasks and averages 71.5% (#14 of 31) against 64.3% (#26 of 31) for Qwen3.5 27B. 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, GPT-5.6 Luna leads with 55.0% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0007 per sample against $0.0010. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output; Qwen3.5 27B is priced at $0.20 per 1M input tokens and $1.56 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 6.5s per inference against 7.4s. 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 classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.