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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.

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OpenAIGPT-6.1 Sol
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GPT-6.1 Sol vs Qwen3.8 Max on Vision Evals

Score by taskGPT-6.1 SolQwen3.8 Max
Reasoning
83.7
75.9
Object Detection
80.8
76.7
Counting
78.8
81.1
OCR
75.8
74.0
Identification
89.6
88.5

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

PropertyGPT-6.1 SolQwen3.8 Max
OrganizationOpenAIQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Aug 2026
Context Window1.1M984K
Parametersundisclosed2.4T total, ~95B active
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$2.00No published price
Output $/1M$10.00No published price
Vision Tasks
Promptable Concept SegmentationDemoNot listed
Both support 10 more tasks
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 / sample12.82s18.95s
By task
Object Detection (low)
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
76.7%
±0.3, Mean of 3 runs, range 76.5 to 77.1
$0.012
Object Detection (high)
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
78.4%
±0.4, Mean of 3 runs, range 78.1 to 78.9
$0.030
Counting (low)
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
81.1%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0046
Counting (high)
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
81.1%
±0.0, Mean of 3 runs, range 81.1 to 81.1
$0.0091
Identification (low)
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0027
Identification (high)
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0037
OCR (low)
75.8%
$0.0069
74.0%
$0.0077
by category
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
Single value
63.9%
Transcription
95.2%
Structured JSON
85.4%
Text localization
59.0%
OCR (high)
75.8%
$0.016
71.8%
$0.028
by category
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Single value
61.7%
Transcription
94.8%
Structured JSON
81.6%
Text localization
59.6%
Reasoning (low)
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
75.9%
±2.0, Mean of 3 runs, range 73.5 to 77.5
$0.0048
Reasoning (high)
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042
80.3%
±2.0, Mean of 3 runs, range 78.2 to 82.1
$0.011

GPT-6.1 Sol vs Qwen3.8 Max: Overview

GPT-6.1 Sol

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

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.