· reAPI Team
GPT-6.1 Terra vs 6.1 Sol: Price, Reasoning and Which to Use
GPT-6.1 Terra vs 6.1 Sol from OpenAI's docs: positioning, reasoning levels, context, knowledge cutoff and price, plus a pick-by-task table and where Luna fits.
GPT-6.1 Terra vs 6.1 Sol is the first choice anyone building on OpenAI's GPT-6.1 line has to make. Terra arrived on October 26, 2026, and on paper the two models are close: the same 1,050,000-token context window, the same 128,000-token output ceiling, text and image input[1][2][3]. GPT-6.1 Terra costs half as much on every token type, and OpenAI aims the two at different work.
This comparison uses only OpenAI's documentation, retrieved October 27, 2026: the two model pages, the GPT-6 guide and the pricing page. It covers what each model is for, how reasoning effort differs, what the price gap means per request, and where GPT-6 Luna and the older GPT-5.6 Terra fit. It does not use benchmark scores; no independent evaluation was reviewed for this article.
TL;DR
- Positioning: Terra is "a fast, efficient model for visual reasoning, precise computer use, and clear, natural writing"; Sol offers "near-Astra performance for complex work at a lower cost"[1][2].
- Price: Terra is $1.00 input and $5.00 output per 1M tokens, exactly half of Sol's $2.00 and $10.00[4].
- Reasoning: Terra supports
nonethroughmax; Sol starts atlow[5]. - Limits: identical context, input and output limits; Terra's knowledge cutoff is June 1, 2026, Sol's April 30, 2026[1][2].
- Tools: both support Chat Completions, but tool calling requires the Responses API for both[5].
- Rule of thumb (our judgment): start with Terra for image-heavy and writing work, move to Sol for complex coding and long professional tasks.
GPT-6.1 Terra vs 6.1 Sol in one table
| GPT-6.1 Terra | GPT-6.1 Sol | |
|---|---|---|
| Model ID | gpt-6.1-terra | gpt-6.1-sol |
| OpenAI's description | Fast, efficient: visual reasoning, computer use, natural writing | Near-Astra performance for complex work at a lower cost |
| Reasoning effort | none, low, medium (default), high, xhigh, max | low, medium (default), high, xhigh, max |
| Context / max input / max output | 1,050,000 / 922,000 / 128,000 | 1,050,000 / 922,000 / 128,000 |
| Knowledge cutoff | June 1, 2026 | April 30, 2026 |
| Standard input / output per 1M | $1.00 / $5.00 | $2.00 / $10.00 |
| Cached input per 1M | $0.05 | $0.10 |
| Ultrafast tier | Not listed | 6x Standard |
| Tool calling | Responses API | Responses API |
Sources: OpenAI model pages, GPT-6 guide and pricing page[1][2][4][5].
The table makes one thing plain. The difference between GPT-6.1 Terra and 6.1 Sol is not capacity, since the limits match. It is the job each model is tuned for, the price, and the lowest reasoning setting.
What OpenAI built each model for
OpenAI's examples for Terra are concrete: "match product photos to catalog entries, interpret technical drawings, act on application screens, or draft and revise text"[1]. The GPT-6 guide adds a migration note for teams coming from GPT-5.6 Terra: "compare both models on representative images and workflows using the same reasoning effort"[5].
For Sol, the model page says "Use GPT-6.1 Sol for complex coding, computer use, and professional work"[2]. Computer use appears on both lists. Coding appears only on Sol's.
Read together, the split looks like this. GPT-6.1 Terra is the model for work where images and text go in and a short, well-written answer comes out: catalog matching, reading drawings and screenshots, rewriting copy. GPT-6.1 Sol is for long, multi-step work where depth matters more than cost per call. That is OpenAI's positioning, not a measured quality ranking.
Reasoning effort: the none setting and what it costs
GPT-6.1 Terra supports six effort levels and GPT-6.1 Sol five. The GPT-6 guide states it directly: "GPT-6 Astra and GPT-6.1 Sol do not support the none reasoning effort; GPT-6.1 Terra, GPT-6 Sol, and GPT-6 Luna do"[5]. For Terra it also advises: "Start with medium and increase it if the improvement in quality is worth the added time and tokens"[5].
none is useful for simple rewrites and tagging where no reasoning pass is needed, but it has limits. In reAPI's tests on October 27, 2026, a short multi-step arithmetic question got a wrong answer from Terra at none; at xhigh it used 80 reasoning tokens and at max 199, and both answers were correct[6]. One prompt is not a benchmark, but it shows why none belongs on easy tasks only.
Reasoning tokens bill as output[7], which matters for the common question of Terra at a higher effort against Sol at a lower one. Because Terra's output rate is half of Sol's, Terra at high costs less than Sol at medium as long as Terra generates fewer than twice as many output tokens for the same request. That is arithmetic, not a quality claim; whether the answers match is something to test on your own prompts.
What the price gap means per request
The rates are exactly half across the board[4]. For the same token counts:
| Request (Standard rates) | GPT-6.1 Terra | GPT-6.1 Sol |
|---|---|---|
| 2,000 in, 500 out | $0.0045 | $0.009 |
| 20,000 in (18,000 cached), 800 out | $0.0069 | $0.0138 |
| 1,000 in, 2,300 out incl. reasoning | $0.0125 | $0.025 |
| 300,000 in, 4,000 out (long context) | $0.63 | $1.26 |
Our arithmetic from OpenAI's Standard and long-context rates[4]. Token counts are illustrative.
Two pricing details change the comparison. First, GPT-6.1 Terra at Standard costs the same as GPT-6.1 Sol at Batch, $1.00 and $5.00[4], so work that can wait for a batch window narrows the gap to zero. Second, Sol has an Ultrafast tier at six times Standard; the pricing page lists no Ultrafast rate for Terra[2][4]. Full Terra rates, cache costs and five worked examples are in GPT-6.1 Terra pricing.
Where GPT-6 Luna and GPT-5.6 Terra fit
| Model | OpenAI's description | Input / output per 1M | Lowest effort | Knowledge cutoff |
|---|---|---|---|---|
| GPT-6 Luna | "Most efficient model for focused, high-volume tasks" | $0.10 / $0.50 | none | May 18, 2026 |
| GPT-6.1 Terra | Fast, efficient: visual reasoning, computer use, writing | $1.00 / $5.00 | none | June 1, 2026 |
| GPT-6.1 Sol | Near-Astra performance for complex work | $2.00 / $10.00 | low | April 30, 2026 |
| GPT-5.6 Terra | Balances intelligence and cost | $2.00 / $12.00 | none | February 16, 2026 |
| GPT-6 Astra | "Most capable model for the most demanding work" | $10.00 / $50.00 | low | April 30, 2026 |
Sources: OpenAI model pages and pricing page[1][2][4][8][9][10].
Luna sits one step below Terra at a tenth of the input and output price. GPT-5.6 Terra, the model GPT-6.1 Terra replaces in name, cost twice as much for input and 2.4 times as much for output, with a knowledge cutoff three and a half months earlier[4][9].
Picking between GPT-6.1 Terra and 6.1 Sol by task
This table is editorial judgment built on OpenAI's positioning above, not on measured scores.
| Task | Start with | Why |
|---|---|---|
| Match product photos to catalog entries | GPT-6.1 Terra | Named in OpenAI's Terra examples; half the price per call |
| Read technical drawings, forms and screenshots | GPT-6.1 Terra | Named in OpenAI's Terra examples |
| Draft, rewrite and tighten text | GPT-6.1 Terra | "Clear, natural writing" is in Terra's description |
| Simple tagging where latency matters | GPT-6.1 Terra at none, or GPT-6 Luna | Sol has no none level; Luna is cheaper still |
| Complex coding and long agent tasks | GPT-6.1 Sol | "Complex coding" is in Sol's description, not Terra's |
| Hardest reasoning, cost secondary | GPT-6 Astra | OpenAI's most capable model |
Calling GPT-6.1 Terra on reAPI
reAPI serves GPT-6.1 Terra on its Chat Completions endpoint with the model id gpt-6.1-terra, billed per token from one credit balance; on October 27, 2026 the rates were $0.75 input and $3.75 output per 1M tokens[6][11]. GPT-6.1 Sol was added on October 28, 2026 with the model id gpt-6.1-sol, through the same endpoint and key; in reAPI's checks that day it accepted low to xhigh, and max returned an error. The GPT-6.1 Sol model page has its current rates. The GPT-6.1 Terra model page has the current rates and a playground.
FAQ
is gpt terra better than sol
Not across the board, by OpenAI's own description. GPT-6.1 Sol is positioned for "complex coding, computer use, and professional work", GPT-6.1 Terra for visual reasoning, computer use and writing at half the price[1][2]. For image and writing tasks, test Terra first; for complex coding, start with Sol.
gpt terra high vs sol medium
Terra at high costs less than Sol at medium whenever it produces fewer than twice the output tokens, because Terra's output rate is half of Sol's and reasoning tokens bill as output[4][7]. Whether the answers are as good depends on the task; compare both on a labelled sample.
is gpt terra faster than sol
OpenAI describes GPT-6.1 Terra as "fast" and Sol as near-Astra in performance, but neither model page publishes a latency figure[1][2]. Sol has an Ultrafast tier at six times Standard pricing; Terra does not[4]. Terra's none effort removes the reasoning pass entirely, which Sol cannot do[5].
is gpt terra good for coding
OpenAI names "complex coding" for GPT-6.1 Sol and does not list coding among Terra's uses[1][2]. Terra can still handle code, but for complex coding work Sol is the documented choice.
gpt terra vs astra
GPT-6 Astra is OpenAI's "most capable model for the most demanding work" and costs ten times GPT-6.1 Terra: $10.00 vs $1.00 input and $50.00 vs $5.00 output per 1M tokens[4][10]. Astra has no none effort; Terra does[5].
gpt 6 terra vs gpt 5.6 terra
There is no GPT-6 Terra; the new Terra is GPT-6.1 Terra[3]. Against GPT-5.6 Terra it costs half for input ($1.00 vs $2.00) and less than half for output ($5.00 vs $12.00), with a June 1, 2026 knowledge cutoff instead of February 16, 2026[4][9]. OpenAI advises comparing the two on representative images and workflows at the same effort before switching[5].
GPT-6.1 Terra or 6.1 Sol in practice
The GPT-6.1 Terra vs 6.1 Sol decision comes down to the work, not the limits, which are identical. Put image-heavy tasks, catalog matching and writing on Terra at medium, check a sample, and lower the effort where accuracy holds. Keep Sol for complex coding and long professional tasks, or use Sol's Batch rates when the job can wait. If a task is simple enough for none, compare GPT-6.1 Terra with GPT-6 Luna before settling on either.
References
- OpenAI. GPT-6.1 Terra model page. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-6.1-terra
- OpenAI. GPT-6.1 Sol model page. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-6.1-sol
- OpenAI. API changelog, October 26, 2026. Retrieved October 2026 from developers.openai.com/api/docs/changelog
- OpenAI. API pricing. Retrieved October 2026 from developers.openai.com/api/docs/pricing
- OpenAI. GPT-6 guide. Retrieved October 2026 from developers.openai.com/api/docs/guides/latest-model
- reAPI. GPT-6.1 Terra API docs. Retrieved October 2026 from reapi.ai/docs/gpt-6-1-terra
- OpenAI. Reasoning models. Retrieved October 2026 from developers.openai.com/api/docs/guides/reasoning
- OpenAI. GPT-6 Luna model page. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-6-luna
- OpenAI. GPT-5.6 Terra model page. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-5.6-terra
- OpenAI. GPT-6 Astra model page. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-6-astra
- reAPI. GPT-6.1 Terra model page. Retrieved October 2026 from reapi.ai/models/gpt-6-1-terra