Roboflow

OpenAIOpenAIReleased Sep 2026Proprietary

GPT-6 Sol

GPT-6 Sol scores 76.9% overall on Roboflow's Vision Evals (October 26, 2026 snapshot), #13 of 65 models (GPT-6 Astra ranks higher at 83.4%).

Its best task is object detection (74.7%, #6 of 65) and its weakest is OCR (66.7%, #20 of 49). It is a proprietary model from OpenAI, so commercial use follows OpenAI's terms.

Vision Evals76.9%Field median 63.5% · #13 of 65
Cost$0.0074/ sampleField median $0.0033 · #46 of 65
Speed10.2s/ sampleField median 10.4s · #31 of 65

GPT-6 Sol Interactive Demo

Model settings

Thinking level

Max output tokens

Default 65,536 · max 65,536

Sign in to adjust thinking and output length per run.

Results appear here. Add an image or pick an example to run GPT-6 Sol.

GPT-6 Sol Vision Evals

Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across five vision tasks, and answers are scored against ground truth.

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

Strengths and weaknesses

Best showing

TaskScoreMedianRank
Object Detection74.7%56.4%#6 of 65

Weak spot

TaskScoreMedianRank
OCR66.7%63.6%#20 of 49

Above the field median, but its lowest rank.

Score by taskScoreRank
Object Detection
74.7%#6
Identification
93.8%#9
Counting
76.6%#12
Reasoning
72.6%#16
OCR
66.7%#20

GPT-6 SolField median

Ranks out of 65 models (49 for OCR)

Results by task

TaskScoreField (0 to 100)RankCost / sampleSpeed
Object Detection (low)
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
#6 of 65$0.01115.60s
Object Detection (high)
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
#7 of 33$0.02223.63s
Counting (low)
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
#12 of 65$0.00395.72s
Counting (high)
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
#13 of 33$0.007011.03s
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
#9 of 65$0.00274.16s
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
#9 of 33$0.00364.13s
OCR (low)
66.7%
#20 of 49$0.00779.27s
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
OCR (high)
68.8%
#18 of 49$0.01826.43s
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Reasoning (low)
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
#16 of 65$0.00378.44s
Reasoning (high)
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
#13 of 52$0.00617.37s
  • Thinking longer helps: 1.8 points higher on object detection at high effort for 2x the cost and 1.5x the latency.
  • Thinking longer helps: 0.9 points higher on counting at high effort for 1.8x the cost and 1.9x the latency.
  • Thinking longer does not help: 1 points lower on identification at high effort for 1.4x the cost and 1x the latency.
  • Thinking longer helps: 2.1 points higher on ocr at high effort for 2.4x the cost and 2.9x the latency.
  • Thinking longer helps: 5.1 points higher on reasoning at high effort for 1.6x the cost and 0.9x the latency.

Price vs. performance

Score vs. cost

Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.

64 of 65 models plotted (the rest have no measured figure for this axis) · scores and efficiency pooled across all five tasks at low effort · GPT-6 Sol highlighted

GPT-6 Sol scores are the mean of 3 runs per task at both low and high effort; OCR is a single preliminary run · Methodology

View all Vision Evals →

GPT-6 Sol 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.

GPT-6 Sol Pricing

GPT-6 Sol costs $2.00 per 1M input tokens and $10.00 per 1M output tokens.

Input$2.00 / 1M tokens
Output$10.00 / 1M tokens
Cached input$0.200 / 1M tokens

Pricing updated Nov 7, 2026

Deploy GPT-6 Sol with an API

GPT-6 Sol runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "GPT-6 Sol" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-6-sol-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "gpt-6-sol-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="gpt-6-sol-captioning",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-6-sol-captioning', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"}
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-6-sol-captioning' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"}
  }
}'

GPT-6 Sol License

Proprietary

GPT-6 Sol is proprietary: the weights are not distributed, and the GPT-6 Sol license is the vendor's commercial terms of service that you accept when you call the API.

Commercial use
Permitted under the vendor terms, typically metered per token or per request, with the vendor usage policy applying to your inputs and outputs.
Modification
Not available. GPT-6 Sol weights are closed, so you can configure prompts and use vendor-hosted fine-tuning where it is offered, but you cannot modify the model itself.
Redistribution
Not permitted. You cannot self-host or resell the model; you build on the hosted API instead.

Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.

Do I need a commercial license for GPT-6 Sol?

Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy an open model instead of GPT-6 Sol, Roboflow's licensing page lists the models whose commercial license is included when you deploy on Roboflow.

Do not hesitate to reach out with questions for your commercial project. Our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

License information is provided as a guide and is not legal advice.

GPT-6 Sol Details

Details

Categoryclosed
ModalityMultimodal
Context Window1.1M
Parametersundisclosed
LicenseProprietary
Commercial usecommercial use per vendor terms
Release DateSep 2026

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRVision LanguageVisual Question AnsweringObject Detection

Usage

Past 30 Days

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Other OpenAI GPT models

Other versions in the same family as GPT-6 Sol.

OpenAIGPT-6.1 SolOpenAIGPT-6 AstraOpenAIGPT-5.6 SolOpenAIGPT-5.5OpenAIGPT-5.4OpenAIGPT-5.2OpenAIGPT-5.1OpenAIGPT-5

Frequently Asked Questions About GPT-6 Sol Vision

Yes. GPT-6 Sol accepts image input and handles OCR, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Object Detection at 74.7% (#6 of 65 at low effort). You can test it on your own image in the demo above.

Yes. it scores 66.7% (#20 of 49 at low effort) on Vision Evals OCR, which covers single readings, full transcriptions, structured JSON, and locating text with boxes. Within that, full transcriptions score 88.3%, and returning the fields of a label or form as JSON scores 83.4%.

Yes. On Vision Evals, GPT-6 Sol scores 74.7% mAP@50 on object detection (#6 of 65 at low effort) and 76.6% judge-graded accuracy on object counting.

On our benchmark's task mix, GPT-6 Sol averages $0.0074 per sample at $2.00 per 1M input and $10.00 per 1M output tokens (#46 of 65 on cost), with an average speed of 10.2s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, GPT-6 Sol sits #13 of 65 at 76.9%, just behind Gemini 3.6 Flash (77.1%) and just ahead of Gemini 3.1 Pro (76.8%). See the full side-by-side: GPT-6 Sol vs Gemini 3.6 Flash.