Roboflow

GPT-6 Sol vs MiMo V2.6 Pro

GPT-6 Sol and MiMo V2.6 Pro score 76.9% vs 50.3% averaged across five Vision Evals tasks (#13 and #56 of 65 models), so GPT-6 Sol leads overall.

  • GPT-6 Sol wins all 5 tasks, with its widest lead on reasoning (72.6% vs 35.1%).
  • MiMo V2.6 Pro is about 1/7 of the cost: $0.0011 vs $0.0074 overall average cost per sample.
  • MiMo V2.6 Pro is faster: 9.5s vs 10.2s per sample.
OpenAI

Compare GPT-6 Sol vs MiMo V2.6 Pro live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
OpenAIGPT-6 Sol
Run to compare this model.
MiMo V2.6 Pro
Run to compare this model.

GPT-6 Sol vs MiMo V2.6 Pro on Vision Evals

Score by taskGPT-6 SolMiMo V2.6 Pro
Reasoning
72.6
35.1
Object Detection
74.7
42.0
Counting
76.6
50.0
OCR
66.7
48.6
Identification
93.8
76.0

Roboflow Vision Evals. Full breakdown below.

GPT-6 Sol vs MiMo V2.6 Pro Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyGPT-6 SolMiMo V2.6 Pro
OrganizationOpenAIXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Sep 2026
Context Window1.1M1.0M
Parametersundisclosed1.02T total, 42B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$2.00$0.435
Output $/1M$10.00$0.870
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
76.9%
50.3%
Avg cost / sample$0.0074$0.0011
Avg speed / sample10.20s9.45s
By task
Object Detection (low)
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
$0.011
42.0%
±1.1, Mean of 3 runs, range 40.9 to 43.1
$0.0014
Object Detection (high)
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
$0.022
46.7%
±0.8, Mean of 3 runs, range 45.7 to 47.3
$0.0030
Counting (low)
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0039
50.0%
±2.0, Mean of 3 runs, range 48.6 to 52.7
$0.0005
Counting (high)
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0070
59.0%
±5.4, Mean of 3 runs, range 52.7 to 63.5
$0.0016
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0027
76.0%
±1.6, Mean of 3 runs, range 75.0 to 78.1
$0.0004
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
78.1%
±4.7, Mean of 3 runs, range 71.9 to 81.3
$0.0012
OCR (low)
66.7%
$0.0077
48.6%
$0.0012
by category
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
Single value
37.0%
Transcription
68.2%
Structured JSON
67.7%
Text localization
18.7%
OCR (high)
68.8%
$0.018
49.9%
$0.0034
by category
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Single value
40.4%
Transcription
59.9%
Structured JSON
70.2%
Text localization
16.8%
Reasoning (low)
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
$0.0037
35.1%
±2.6, Mean of 3 runs, range 32.5 to 37.8
$0.0005
Reasoning (high)
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
$0.0061
55.9%
±2.3, Mean of 3 runs, range 54.3 to 58.9
$0.0026

GPT-6 Sol vs MiMo V2.6 Pro: Overview

GPT-6 Sol

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.

MiMo V2.6 Pro

MiMo V2.6 Pro is the flagship omni-modal foundation model in Xiaomi's MiMo V2.6 series, released as open weights alongside a Flash variant and a 9B distillation of Qwen3.5. It uses a sparse mixture-of-experts transformer with 1.02 trillion total parameters and roughly 42 billion activated per token, paired with a hybrid attention design that interleaves sliding-window and global attention layers to support a context window of about one million tokens. Dedicated encoders handle non-text inputs, including a vision encoder of roughly 681 million parameters and an audio tokenizer stack, so the model accepts text, images, video, and audio and returns text.

Post-training centers on large-scale reinforcement learning across thousands of interactive environments, combined with agentic grading, self-correction cold start, and a multi-prefix multi-teacher on-policy distillation stage that extends behavior to tasks that are hard to verify automatically.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#13 of 65) against 50.3% (#56 of 65) for MiMo V2.6 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Reasoning benchmark at low effort, GPT-6 Sol leads with 72.6% against 35.1%. This is the widest gap between the two models across the benchmark's tasks.

MiMo V2.6 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.0074. GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; MiMo V2.6 Pro is priced at $0.43 per 1M input tokens and $0.87 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

MiMo V2.6 Pro is faster. Across Roboflow's Vision Evals it averaged 9.5s per inference against 10.2s. 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.