GPT-5.6 Terra
OpenAI's balanced mid-tier frontier model — 1M context, multimodal reasoning, and native tool use for production workloads.
Overview
What is GPT-5.6 Terra
GPT-5.6 Terra is OpenAI's balanced mid-tier model in the GPT-5.6 family, released in July 2026. It offers a 1,000K-token context window, vision, reasoning, tool use, and web search, delivering strong performance for production workloads at a lower cost than the flagship Sol tier.
Running it privately on Venice
On Venice, GPT-5.6 Terra runs under an anonymized privacy tier — your prompts are not stored, profiled, or used for training. You get the same balanced OpenAI model with 1M context, vision, reasoning, and tool use, but without the surveillance baggage. Note that this deployment does not currently use a TEE or end-to-end encryption.
Assessment
Strengths and limitations
- 1M-token context window supports long-document analysis, large-codebase RAG, and extended agent sessions.
- Native multimodal support: accepts multiple image inputs alongside text for vision-heavy workflows.
- Built-in reasoning, web search, and function calling make it a strong agent backbone.
- Structured output (JSON schema) enables reliable integration into pipelines.
- Competitive performance per dollar within the OpenAI tier, undercutting flagship Sol while retaining most capabilities.
- Proprietary and closed-weight: no self-hosting, fine-tuning, or sovereignty over the weights.
- Not uncensored; safety filters and refusals remain part of the model behavior.
- Output token pricing ($18.75/1M) is steep versus open-weight rivals such as DeepSeek V3.2 and Kimi K2.6.
- On Venice, the deployment is anonymized but does not run in a TEE or with end-to-end encryption.
- 1M context is matched by several rivals; the value proposition depends on OpenAI's specific reasoning and tool quality.
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
- Params
- 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-terra",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $3.13 per 1M input tokens and $18.75 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 Terra | 1M tokens | Balanced reasoning & agents | No | $3.13 in · $18.75 out / 1M |
| Claude Sonnet 4.6 | 1M tokens | General reasoning | No | $3.60 in · $18 out / 1M |
| Grok 4.3 | 1M tokens | Real-time & web search | No | $1.42 in · $2.83 out / 1M |
| DeepSeek V3.2 | 160K tokens | Open-weight coding | Yes | $0.33 in · $0.48 out / 1M |
OpenAI's balanced tier with native vision, tool use, and 1M context — a workhorse for agentic pipelines.
Use cases
What it is good for
- 01Enterprise knowledge work: analyzing and summarizing long contracts, research papers, or logs within a 1M context.
- 02Agentic automation: chaining tool calls, web searches, and reasoning steps into multi-turn workflows.
- 03Multimodal assistants: processing screenshots, diagrams, and documents with mixed text and image inputs.
- 04Structured data extraction: converting unstructured documents into JSON via schema-constrained generation.
- 05Customer support and internal tooling at scale where balanced latency and quality matter.
Prompting
Getting better results
Mark reusable prompt prefixes for caching to take advantage of the $0.31/1M cached input rate.
Request structured output (JSON schema) when you need deterministic, parseable responses.
For long documents, place key instructions at both the start and end of the context to improve recall across the 1M window.
Use medium reasoning as a starting point, then tune down for token efficiency on simpler tasks.
Version history
Predecessor generation.
Efficient, high-volume tier.
Current balanced mid-tier.
Flagship capability tier.
FAQ
Frequently asked questions
OpenAI's balanced mid-tier model in the GPT-5.6 family, released July 2026. It delivers a 1M-token context, vision, reasoning, tool use, and web search for production workloads.
On Venice, $3.13 per 1M input tokens and $18.75 per 1M output tokens. Cached input costs $0.31 per 1M tokens. You pay per use with no subscription.
No. It is proprietary and closed-weight. For open-weight alternatives, try DeepSeek V3.2 or Kimi K2.6 on Venice.
New Venice accounts include free credits to test it, but continued use is billed per token. There is no separate subscription fee.
Both offer 1M context and strong reasoning. Terra adds native web search and programmatic tool calling. Sonnet 4.6 is slightly cheaper on output. Choose Terra for agentic stacks; Sonnet for Anthropic's safety tuning.
Yes. It supports function calling, structured JSON output, and programmatic tool use. It can also process multiple image inputs in one turn.
Yes. It accepts image inputs and can reason over multiple images alongside text.
1,000K (1 million) tokens, with a maximum output of 128K tokens per generation.
Venice processes it under an anonymized tier — prompts are not stored or used for training. It does not currently run inside a TEE or with end-to-end encryption.
Run GPT-5.6 Terra privately
No prompt logging. No data used for training.