GPT-6.1 Sol vs Qwen3.8 Max
GPT-6.1 Sol and Qwen3.8 Max score 81.7% vs 79.3% averaged across five Vision Evals tasks (#9 and #12 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins 4 of the 5 tasks, with its widest lead on reasoning (83.7% vs 75.9%).
- GPT-6.1 Sol is cheaper: $0.0068 vs $0.0080 overall average cost per sample.
- GPT-6.1 Sol is faster: 12.8s vs 18.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6.1 Sol vs Qwen3.8 Max on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Sol vs Qwen3.8 Max Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | GPT-6.1 Sol | Qwen3.8 Max |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.1M | 984K |
| Parameters | undisclosed | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | No published price |
| Output $/1M | $10.00 | No published price |
| 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 | 81.7% | 79.3% |
| Avg cost / sample | $0.0068 | $0.0080 |
| Avg speed / sample | 12.82s | 18.95s |
| By task | ||
| Object Detection (low) | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 75.8% | 74.0% |
| by category |
|
|
| OCR (high) | 75.8% | 71.8% |
| by category |
|
|
| Reasoning (low) | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
GPT-6.1 Sol vs Qwen3.8 Max: Overview
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.