GPT-6 Sol vs Qwen3.5-27B
Compare GPT-6 Sol and Qwen3.5-27B side-by-side.
Compare GPT-6 Sol vs Qwen3.5-27B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
GPT-6 Sol vs Qwen3.5-27B on Vision Evals
GPT-6 Sol scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Sol leads 73.6% to 50.5%.
Overall, GPT-6 Sol averages 80.7% (#10 of 57) against 70.8% (#25 of 57) for Qwen3.5-27B.
Qwen3.5-27B is cheaper ($0.0043 vs $0.0065 per sample), while GPT-6 Sol is faster (8.1s vs 80.4s per sample).
GPT-6 Sol vs Qwen3.5-27B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 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 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.195 |
| Output $/1M | $10.00 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.7% | 70.8% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0065 | $0.0043 |
| Avg speed / sample | 8.15s | 80.37s |
| By task | ||
| Object Detection (low) | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 | – |
| Counting (low) | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 | – |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 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) | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 | 84.7% ±3.3, Mean of 3 runs, range 80.8 to 87.3 |
| OCR (high) | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 | – |
| Data Extraction (low) | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 | 83.8% ±1.5, Mean of 3 runs, range 82.5 to 85.6 |
| Data Extraction (high) | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 | – |
| Reasoning (low) | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 | – |
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. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. 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. 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-6 Sol performed better. It scores higher on 5 of the six vision tasks and averages 80.7% (#10 of 57) against 70.8% (#25 of 57) 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 73.6% 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 $0.0065. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 8.1s per inference against 80.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.