OpenAI
GPT 5.6 Sol
gpt-5.6-sol is a high-reasoning model built for agentic search and complex research tasks. It features a 256k context window and enhanced tool-calling logic to minimize loop fatigue in autonomous workflows.

Modalities

Input: TextInput: ImageInput: Document
Output: Text

Context

API Usage Examples
curl --request POST "https://gptproto.com/v1/chat/completions" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "gpt-5.6-sol",
    "messages": [
      {
        "role": "user",
        "content": "Hello"
      }
    ]
  }'
GPT 5.6 Sol pricing

Estimate a request with real work scenarios. GPTProto token pricing is 20% below official rates.

Cost calculator

Multi-turn agent with cached context.
$4 / 1M$0.006
$24 / 1M$0.0192
$5 / 1M$0.015
$0.4 / 1M$0.01
Cost per request$0.0502

Top up

GPTProto vs official pricing.
Requests
You pay
20% off
$100
You receive$100.00

Save$25.00 (20%)vs OpenAI official

Related Models
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ModelInput → Output
GPT 5.6 SolCurrent
1.05M$4.00 / $24.00 per 1M$5.00 / $0.40 per 1M
Input: TextInput: ImageInput: Document
Output: Text
GLM 5.3
1.05M$1.26 / $3.96 per 1M— / $0.23 per 1M
Input: TextInput: ImageInput: Document
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Gemini 3.7 Flash
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Qwen3.8 Max
1M$1.80 / $5.40 per 1M$2.25 / $0.23 per 1M
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Claude Opus 5
1M$4.00 / $20.00 per 1M$5.00 / $0.40 per 1M
Input: TextInput: ImageInput: Document
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Gemini 3.6 Flash
1.05M$0.45 / $2.25 per 1M— / $0.04 per 1M
Input: TextInput: ImageInput: Document
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Gemini 3.5 Flash Lite
1.05M$0.18 / $1.50 per 1M$0.02 / $0.02 per 1M
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Kimi K3
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Input: TextInput: ImageInput: Document
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GPT 5.6 Luna
1.05M$0.16 / $0.96 per 1M$0.20 / $0.02 per 1M
Input: TextInput: ImageInput: Document
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GPT 5.6 Terra
1.05M$1.60 / $9.60 per 1M$2.00 / $0.16 per 1M
Input: TextInput: ImageInput: Document
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Grok 4.5
500K$1.20 / $3.60 per 1M$0.30 / $0.30 per 1M
Input: TextInput: Image
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Claude Sonnet 5
1M$1.60 / $8.00 per 1M$2.00 / $0.16 per 1M
Input: TextInput: Document
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MiniMax M3
1.05M$0.48 / $0.96 per 1M$0.10 / $0.10 per 1M
Input: TextInput: ImageInput: Document
Output: Text
GLM 5.2
1.05M$1.26 / $3.96 per 1M$0.23 / $0.23 per 1M
Input: TextInput: ImageInput: Document
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GPT 5.1 Chat Latest
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Qwen3.7 Max
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DeepSeek v4 Flash
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DeepSeek v4 Pro
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Input: TextInput: Image
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1.05M$24.00 / $144.00 per 1M—
Input: TextInput: ImageInput: Document
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GPT 5.5 Pro
1.05M$24.00 / $144.00 per 1M—
Input: TextInput: ImageInput: Document
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GPT 5.5
1.05M$4.00 / $24.00 per 1M$0.40 / $0.40 per 1M
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Kimi K2.6
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GLM 5.1
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DeepSeek v3.2
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MiniMax M2.5
205K$0.24 / $0.96 per 1M$0.30 / $0.02 per 1M
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Kimi K2.5
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Qwen Turbo
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Doubao Seed 1.6 Thinking (Build 250715)
262K$0.10 / $0.97 per 1M—
Input: TextInput: Image
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Doubao Seed 1.6 Thinking (Build 250615)
262K$0.10 / $0.97 per 1M—
Input: TextInput: Image
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Doubao Seed 1.6 Flash (Build 250615)
262K$0.02 / $0.18 per 1M—
Input: TextInput: Image
Output: Text

Core gpt-5.6-sol Reasoning Capabilities

Explore how gpt-5.6-sol reasoning and agentic search features enable deep research and structured data extraction from 256k contexts.

Agentic Efficiency Sol-Variant

gpt-5.6-sol minimizes tool-call fatigue, ensuring autonomous agents terminate loops immediately upon task completion.

256k Expanded Context Window

Process massive datasets and technical documentation with gpt-5.6-sol while maintaining perfect recall and reasoning depth.

Agentic Web Search Integration

Enable gpt-5.6-sol to actively browse, filter domains, and refine search queries for real-time, grounded intelligence.

Parallel Tool Use Reasoning

Execute multiple functions simultaneously with gpt-5.6-sol, optimizing complex workflows and reducing total latency.

gpt-5.6-sol Reasoning & Agentic Search FAQ

Answers regarding gpt-5.6-sol reasoning, agentic search integration, and how the model handles complex research workflows for enterprise-grade autonomous agents.

How does gpt-5.6-sol reasoning differ from GPT-4o?

gpt-5.6-sol reasoning is specifically optimized to reduce tool-call fatigue. While GPT-4o is excellent for multimodal speed, gpt-5.6-sol provides deeper symbolic logic and symbolic calculus performance. It is better at terminating multi-step agentic loops without becoming repetitive, making it the preferred choice for autonomous engineering tasks.

What is agentic search in the gpt-5.6-sol model?

Agentic search allows gpt-5.6-sol to actively manage its own web browsing process. Instead of a single query, gpt-5.6-sol uses its chain-of-thought to decide whether to search more, open specific pages, or find data within a page. This flexibility makes gpt-5.6-sol significantly more accurate for complex workflows than standard search models.

Does gpt-5.6-sol support deep research via API?

Yes, gpt-5.6-sol supports a specialized deep research method. By setting the reasoning effort to high or xhigh in the Responses API, gpt-5.6-sol can run extended investigations for several minutes. It uses an agent-driven approach to consult hundreds of sources and provides comprehensive reports with verified inline citations.

What is the context window for gpt-5.6-sol research?

The gpt-5.6-sol model supports a massive 256,000-token context window. However, when using the web search tool, the search context size is typically limited to 128,000 tokens to maintain high performance and accuracy. This ensures that gpt-5.6-sol can synthesize vast amounts of external data while keeping focus on your primary input instructions.

Can gpt-5.6-sol handle autonomous agent workflows?

Absolutely. gpt-5.6-sol was built with agentic efficiency in mind. It supports parallel tool use and a strict JSON mode for structured outputs. The 'Sol' variant's logic is specifically tuned to navigate complex terminal commands and large directories, which is ideal for AI developers building autonomous coding agents like Devin.

How are gpt-5.6-sol research token budgets managed?

When performing high-effort research, you can set the 'return_token_budget' to unlimited. This allows gpt-5.6-sol to inspect a high volume of web content without hitting standard caps. Note that using unlimited budgets can increase latency and cost, so it is best used in background mode for long-running multi-step reporting tasks.