OpenAI's balanced GPT-5.6 tier for everyday coding, reasoning, and agents at roughly half the cost of Sol.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where GPT-5.6 Terra Earns Its Place in the Lineup
Benchmarks
GPT-5.6 Terra on Independent Benchmarks
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
GPT-5.6 Terra vs GPT-5.6 Sol: When to Escalate
Terra and Sol are siblings in the same GPT-5.6 family, share the naming system and reasoning-effort controls, and both target coding and agentic work. The practical difference is capability ceiling versus cost. Sol is the flagship with the highest reasoning ceiling, built for complex reasoning over large codebases and demanding long-running agentic tasks. Terra is the balanced default that OpenAI positions as competitive with the previous GPT-5.5 generation while costing meaningfully less. For most teams the real decision is not Terra or Sol exclusively, but which tier handles the bulk of traffic and which handles escalations.
Choose GPT-5.6 Terra when you need a cost-efficient everyday default for interactive and agentic coding, general reasoning, and high-volume tool-driven traffic where capability close to the prior generation is sufficient. Choose GPT-5.6 Sol when a task demands the highest reasoning ceiling — complex reasoning across large codebases or long-running multi-agent workstreams — and the added cost is justified by outcome quality. A common architecture runs Terra by default and escalates specific hard cases to Sol.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Everyday agentic and interactive coding
OpenAI and GitHub Copilot both position Terra as the balanced default for everyday interactive and agentic coding. Its reasoning-effort control lets you dial capability per task, and its cost tier makes it viable for the high request volumes coding agents generate. Use Terra for code generation, refactoring, and multi-step agent loops, escalating only the hardest reasoning-heavy tasks to Sol.
High-volume reasoning at scale
Terra scores 42 on Artificial Analysis's Intelligence Index — well above class median — while sitting in a lower cost tier than Sol. That combination makes it a strong choice for reasoning workloads you run at volume: classification with justification, structured extraction, and analytical assistant turns. Tune reasoning.effort to balance quality against token spend, keeping effort low for latency-sensitive traffic.
Tool-driven and function-calling agents
Terra supports function calling via tools and tool_choice plus structured outputs through JSON schema, and independent benchmarks show strong search-and-function-calling scores. This makes it well suited to agents that orchestrate external tools, APIs, and retrieval steps. The large context window helps the agent retain long tool-call histories, though watch the 272K long-context cost threshold on token-heavy sessions.
Large-document and codebase analysis
The ~1.05M-token context window lets Terra ingest large repositories, technical documentation, logs, or contracts in a single request. Combined with image input for diagrams and screenshots, it fits document-understanding and code-review pipelines. Note the cost cliff above 272K input tokens: for routinely oversized prompts, retrieval trimming or chunking keeps per-request economics predictable.