GPT‑6.1 Terra vs. Sol, Astra and Opus 5.5: which model for agents and development?
GPT‑6.1 Terra is a candidate for much of the everyday work in an AI agent without paying frontier-model rates for every step. It does not automatically replace Sol, Astra or Claude Opus. It makes a more useful comparison possible: the cost of a verified result, not just token pricing or a leaderboard score.
OpenAI released it on October 26, 2026. The model documentation emphasizes visual reasoning, computer use and writing; its selection guide also recommends trying it for scoped code fixes. Our Astra and Sol article covers their API and migration. Here the focus is Terra as a working layer.
A practical starting point
| Model | Where I would start testing | When to escalate or add review |
|---|---|---|
| GPT‑6 Luna | high-volume classification and simple routing | ambiguity, difficult documents or missing context |
| GPT‑6.1 Terra | screens and images, scoped code changes, extraction and routine agent tasks | broad refactors, conflicting sources or sensitive writes |
| GPT‑6.1 Sol | complex development, multi-step analysis and harder agent work | critical incidents or repeatedly failing work |
| GPT‑6 Astra | ambiguous, demanding analysis and difficult coding | still needs evidence, boundaries and human approval |
| Claude Opus 5.5 | long coding sessions, agentic review and large codebases | compare on the same task and account for its distinct API |
These are proposed starting roles, not a universal ranking or my own test results. OpenAI positions Sol close to Astra for complex work; Anthropic targets long-running coding and knowledge work with Opus. Each vendor is describing its own products.
Agent control: a capable worker is not an orchestrator
In the Responses API, Terra supports function calling, structured outputs, web and file tools, shell, computer use and MCP. Its reasoning effort ranges from none to max; according to the GPT‑6 guide, Sol and Astra do not support none. Terra tool calling requires Responses API, which matters when changing an existing integration.
Tool support does not establish that a model will plan ten steps reliably or recover from a CRM error. Permissions, isolation and stopping a run belong to the application or agent environment.
For lead triage, I would use an inexpensive first step in n8n to choose a queue. Terra could assemble an evidence-based summary and propose an action; conflicting evidence could go to Sol, then to a person where the impact or uncertainty requires it. An email or CRM change needs approval of the exact content and recipient. This is a proposed design, not an integration tested for this article.
A long run needs a job ID, tool-call records, time and cost limits, duplicate handling and a blocked state. The model operates within its granted tools; the orchestrator enforces approval rules. Test Terra both as a single worker and under a stronger coordinator: another agent also adds context, tokens and opportunities for error.
Coding: same agent, different model
Artificial Analysis evaluates model variants inside Codex. At the time of review, its Terminal‑Bench 4.0 results at max were:
| Model in Codex | Terminal‑Bench 4.0 | Average cost per task across the full Coding Agent Index |
|---|---|---|
| GPT‑6.1 Terra | 48% | $1.24 |
| GPT‑6.1 Sol | 53% | $1.50 |
| GPT‑6 Astra | 56% | $7.04 |
The cost is averaged over the full index, not Terminal‑Bench alone or only successful tasks. This is the evaluator's agent and task set, not our benchmark. A few points do not guarantee the same order on your repository; the same model in another harness may behave differently.
I have not put Opus in that table because the cited set does not show a comparable Opus run in the same Codex harness. Anthropic's own evaluations show how Opus cost and success change with effort, but cannot be merged with a different test into one ranking.
I would try Terra on a defined bug, form change, tests for an existing feature or a bounded refactor. Compare Sol and Opus on a change crossing several modules, and add Astra for difficult diagnosis or architecture. Give each the same brief, starting commit and documentation access. Tests and review must not be weakened by the agent to make its own change pass.
Measure accepted pull requests without regressions, review corrections, engineer time and the total bill. Lines changed and a green test written by the agent are not sufficient.
Data analysis: three missed questions may matter
Plotly's DataAnalyticsBench compares models in the same agent and tools against a synthetic data warehouse. On its 43-question set, Terra scored 40/43 for $1.31, Sol 43/43 for $2.61, Astra 43/43 for $19.97, and Opus 41/43 for $13.31 for the full run. Those are the authors' test costs, not a price for a typical enterprise task.
That is more informative than simply calling Terra cheap. A few misses might be acceptable for an internal dashboard but not a financial report. The set has 43 English questions, one domain and model-based grading against an answer key. The authors also describe single runs, so small differences can be noise. It does not prove Terra generally beats Opus or that Sol never makes mistakes.
On your own data, test incomplete exports, duplicate invoices, missing currency, an ambiguous revenue definition and questions the data cannot answer. Sometimes the correct result is “I don't know” or a request for clarification.
API prices versus the real bill
Standard text-token prices per million as checked October 27, 2026, from OpenAI and Anthropic:
| Model | Input | Cache read | Output |
|---|---|---|---|
| GPT‑6 Luna | $0.10 | $0.01 | $0.50 |
| GPT‑6.1 Terra | $1 | $0.05 | $5 |
| GPT‑6.1 Sol | $2 | $0.10 | $10 |
| Claude Opus 5.5 | $4 | $0.20 | $20 |
| GPT‑6 Astra | $10 | $1 | $50 |
At these rates Terra is half Sol, one quarter Opus and one tenth Astra. Their APIs charge for cache writes too, with different retention rules. Terra's 30-minute write costs $1.25 and its 12-hour write $2.50 per million tokens. For prompts over 272,000 input tokens, its entire request has double input/cache and 1.5× output rates. Batch/Flex and Fast differ, and regional processing can add 10%.
For 100,000 ordinary input and 20,000 output tokens, with no cache, tools or other fees, Terra costs $0.20, Sol $0.40, Opus $0.80 and Astra $2. That is not the cost of a full agent session. Tool calls, cache writes, retries and human review change the result. A Codex or Claude Code subscription is not the API bill.
Screens, documents and boundaries
Terra accepts text and images and returns text; the model page does not list native audio or video input. For receipts and screenshots, retain the source file, proposed fields and a way to correct them. An agent using a supplier portal should record what it saw and saved, and the application should verify the destination record. A screenshot of a Save button is not evidence that a record was saved.
For EU data, OpenAI says Fast mode for Terra and Sol supports EU data residency, while Astra Fast does not. Check account eligibility and contract settings. Minimize data before model calls and restrict tool access.
How I would choose as a team
Build 30–50 anonymized real tasks, keeping tuning examples separate from the final test. Include routine, exceptional and risky cases. Give every model the same inputs, permissions and predefined checks; record the agent version and reasoning effort.
For code, measure accepted changes and regressions. For invoices, measure field accuracy, correct rejection of missing data and review time. For agents, include completion without unauthorized writes, retries and tool costs. Begin in shadow mode and retain a rollback to the existing workflow.
My starting choice would be Terra for scoped work with a verifiable result, Sol or Opus for harder context, and Astra for the toughest exceptions. Your data and the cost of an error should set the actual boundaries. A cheaper model becomes a sound default only when it completes the right work reliably.
Facts and prices checked October 27, 2026. Workflow examples are proposed pilots; benchmark results belong to their respective authors. Cover: AI-generated illustrative photograph.