GPT-6 Sol vs Qwen3.5-27B
GPT-6 Sol averages 76.9% across five Vision Evals tasks and Qwen3.5-27B 64.1% across four (#13 and #32 of 65 models), so GPT-6 Sol leads overall.
- GPT-6 Sol wins all 4 tasks both ran, with its widest lead on object detection (74.7% vs 50.5%).
- Qwen3.5-27B is about half the cost: $0.0043 vs $0.0074 overall average cost per sample.
- GPT-6 Sol is faster: 10.2s vs 80.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6 Sol vs Qwen3.5-27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Sol vs Qwen3.5-27B Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | GPT-6 Sol | Qwen3.5-27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.1M | 262K |
| Parameters | undisclosed | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.260 |
| Output $/1M | $10.00 | $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 | 76.9% | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0074 | $0.0043 |
| Avg speed / sample | 10.20s | 80.37s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | – |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 66.7% | – |
| by category |
| |
| OCR (high) | 68.8% | – |
| by category |
| |
| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | – |
GPT-6 Sol vs Qwen3.5-27B: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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 Sol performed better. It scores higher on all 4 vision tasks and averages 76.9% (#13 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 Sol leads with 74.7% against 50.5%. 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 $0.0043 per sample against $0.0074. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol 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.