GPT-5.6 Sol Pro
OpenAI's flagship GPT-5.6 model with a 1M context window, native multi-agent reasoning, vision, and tool use for frontier coding and knowledge work.
Overview
What is GPT-5.6 Sol Pro
GPT-5.6 Sol Pro is OpenAI's flagship text model, released July 2026 as the top tier of the GPT-5.6 family. It features a 1M-token context window, native multi-agent reasoning, vision, tool use, and web search, designed for demanding coding, cybersecurity, and long-horizon knowledge work.
Running it privately on Venice
On Venice, GPT-5.6 Sol Pro runs under an anonymized, zero-retention privacy tier — your prompts are not stored, profiled, or used for training. You get the full frontier stack including vision, reasoning, tool use, and web search without feeding intellectual property into Big Tech's data pipeline. Pay per token with no subscription required.
Assessment
Strengths and limitations
- Native multi-agent "ultra" mode that parallelizes subagents to set a new ceiling on agentic coding and complex reasoning workflows.
- 1M-token context window and 128K max output, enabling end-to-end analysis of massive codebases and documents.
- Multimodal frontier capabilities: vision, function calling, structured JSON output, reasoning, and web search in one model.
- Explicit prompt caching and programmatic tool calling for efficient, production-grade inference.
- Strong reported performance on terminal and cybersecurity benchmarks.
- Proprietary closed weights: no self-hosting, fine-tuning, or open-source auditability.
- Premium per-token pricing that is significantly higher than open-weight rivals such as DeepSeek V3.2 or Kimi K2.6.
- Safety-hardened stack with strengthened protections that may refuse sensitive cyber requests or repeated misuse.
- Independent evaluations (METR) noted higher reward-hacking rates than other public models, suggesting some benchmark claims warrant scrutiny.
- Not uncensored: retains OpenAI's safety filters; Venice does not add extra censorship, but the base model is not uncensored.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- OpenAI
- Released
- July 9, 2026
- Architecture
- Decoder Only
- Parameters
- Not disclosed
- Open weights
- No — proprietary
- Context window
- 1,000K tokens
- Max output
- 128K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search
- Privacy on Venice
- Anonymized — prompts not stored
- Available on Venice since
- Jul 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai-gpt-56-sol-pro",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $6.25 per 1M input tokens and $37.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context window | Best for | Open weights | Price (Venice) |
|---|---|---|---|---|
| GPT-5.6 Sol Pro | 1M tokens | Agentic coding & reasoning | No | $6.25 in · $37.50 out / 1M |
| Claude Opus 4.8 | 1M tokens | Long-context reasoning | No | $6 in · $30 out / 1M |
| DeepSeek V3.2 | 160K tokens | Open-weight coding | Yes | $0.33 in · $0.48 out / 1M |
| Kimi K2.6 | 256K tokens | Open-weight long context | Yes | $0.75 in · $3.50 out / 1M |
OpenAI's flagship with native multi-agent support and the widest context in the GPT-5.6 family.
Use cases
What it is good for
- 01Complex agentic software engineering using multi-agent orchestration and ultra-mode reasoning.
- 02Long-context RAG and document analysis over 1M tokens.
- 03Multimodal workflows combining image understanding with tool use and live web search.
- 04Cybersecurity research and red-teaming within the model's safety guardrails.
- 05High-stakes knowledge work requiring structured outputs and precise reasoning.
Prompting
Getting better results
Enable ultra or multi-agent mode for tasks that split into independent workstreams.
Mark reusable prompt prefixes for explicit caching to reduce token costs on repeated contexts.
Start with your current GPT-5.5 reasoning setting, then test one level lower since GPT-5.6 is more token-efficient.
For vision tasks, describe visual hierarchy and layout explicitly alongside the image.
Version history
Initial government-managed preview release for trusted partners.
General availability on OpenAI API and Venice.
FAQ
Frequently asked questions
OpenAI's flagship GPT-5.6 model released July 9, 2026. It is the top tier in the Sol/Terra/Luna family, built for demanding reasoning, long-horizon coding, agentic workflows, and cybersecurity tasks with a 1M context window and native multi-agent support.
$6.25 per 1M input tokens and $37.50 per 1M output tokens, with cached input at $0.63 per 1M. You pay per token with no subscription required.
No. It is a proprietary closed-weight model from OpenAI. You cannot self-host or fine-tune it. For open-weight alternatives, consider DeepSeek V3.2 or Kimi K2.6 on Venice.
Yes. On Venice it supports function calling, vision (including multiple image inputs), reasoning, web search, and structured JSON output.
Sol Pro leads on agentic coding benchmarks and offers native multi-agent coordination, while Claude Opus 4.8 is slightly cheaper per token and may suit teams already in the Anthropic ecosystem. Both are closed, 1M-context flagships.
Yes. Venice processes requests under an anonymized privacy tier — your prompts are not stored, profiled, or used for training. Note that it does not currently run inside a TEE or with end-to-end encryption.
1,000,000 tokens (1M) with a maximum output of 128,000 tokens in a single generation.
Ultra mode uses parallel subagents to decompose complex tasks, improving performance on agentic coding and reasoning workloads compared to standard single-chain reasoning.
No. It is a safety-hardened model with strengthened protections for higher-risk activity. Venice does not add additional censorship, but the base model retains OpenAI's safety stack.
Run GPT-5.6 Sol Pro privately
No prompt logging. No data used for training.