GPT-5.6 Luna vs Qwen3.8 27B
Compare GPT-5.6 Luna and Qwen3.8 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 Qwen3.8 27B on Vision Evals
Qwen3.8 27B scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 27B leads 65.7% to 61.0%.
Overall, GPT-5.6 Luna averages 73.8% (#23 of 61) against 74.7% (#22 of 61) for Qwen3.8 27B.
Qwen3.8 27B is cheaper ($0.0009 vs $0.0010 per sample), while GPT-5.6 Luna is faster (7.4s vs 18.0s per sample).
GPT-5.6 Luna vs Qwen3.8 27B Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | GPT-5.6 Luna | Qwen3.8 27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.5M | 262K |
| Parameters | 27.78B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.025 |
| Output $/1M | $1.20 | $4.35 |
| 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 | 73.8% | 74.7% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0010 | $0.0009 |
| Avg speed / sample | 7.38s | 17.99s |
| By task | ||
| Object Detection (low) | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 90.7% ±1.8, Mean of 3 runs, range 88.4 to 92.0 | 92.2% ±1.2, Mean of 3 runs, range 91.1 to 93.4 |
| OCR (high) | 91.5% ±0.3, Mean of 3 runs, range 91.2 to 91.7 | 91.5% ±1.4, Mean of 3 runs, range 90.1 to 92.9 |
| Data Extraction (low) | 80.4% ±2.1, Mean of 3 runs, range 78.3 to 82.5 | 78.0% ±1.0, Mean of 3 runs, range 77.3 to 79.4 |
| Data Extraction (high) | 81.8% ±0.5, Mean of 3 runs, range 81.4 to 82.5 | 80.8% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
GPT-5.6 Luna vs Qwen3.8 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.
Qwen3.8-27B 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
On Roboflow's Vision Evals, Qwen3.8 27B performed better. It scores higher on 4 of the six vision tasks and averages 74.7% (#22 of 61) against 73.8% (#23 of 61) for GPT-5.6 Luna. 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 61.0%. 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 $0.0010. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 7.4s 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 classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.