GPT-6 Luna vs Qwen3.5-27B
GPT-6 Luna averages 69.7% across five Vision Evals tasks and Qwen3.5-27B 64.1% across four (#22 and #32 of 65 models), so GPT-6 Luna leads overall.
- GPT-6 Luna wins all 4 tasks both ran, with its widest lead on object detection (65.5% vs 50.5%).
- GPT-6 Luna is about 1/10 of the cost: $0.0004 vs $0.0043 overall average cost per sample.
- GPT-6 Luna is faster: 10.2s vs 80.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6 Luna vs Qwen3.5-27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Luna vs Qwen3.5-27B Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | GPT-6 Luna | Qwen3.5-27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.1M | 262K |
| Parameters | Unknown | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.260 |
| Output $/1M | $0.500 | $2.60 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 69.7% | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0004 | $0.0043 |
| Avg speed / sample | 10.21s | 80.37s |
| By task | ||
| Object Detection (low) | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 | – |
| Counting (low) | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| Identification (high) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | – |
| OCR (low) | 59.8% | – |
| by category |
| |
| OCR (high) | 62.5% | – |
| by category |
| |
| Reasoning (low) | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 | – |
GPT-6 Luna vs Qwen3.5-27B: Overview
GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.
The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.
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. 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. 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-6 Luna performed better. It scores higher on all 4 vision tasks and averages 69.7% (#22 of 65) against 64.1% (#32 of 65) 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 Object Detection benchmark at low effort, GPT-6 Luna leads with 65.5% against 50.5%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0043. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Luna is faster. Across Roboflow's Vision Evals it averaged 10.2s per inference against 80.4s. 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 captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.