GPT-6.1 Terra is the mid-tier model of OpenAI's GPT-6.1 series, released on October 26, 2026 and priced below GPT-6.1 Sol.
- Input
- text image $1/M
- Output
- text $5/M
- Cache read
- $0.05/M
- Context
- 1.1M
- vs GPT-4o
- ~80% cheaper
- Knowledge cutoff
- 2026-06
Prompts over 272K tokens: the whole request bills at $2/M input · $7.5/M output
Price in context
Where the price sits among 70 comparable models
The bar shows how this model’s price compares with every other model of the same kind on Synthorai. The cheapest and the most expensive are named at each end. These are base rates; batch, region and cache-write discounts are on the pricing page.
Specs & limits
Tokens
| Context window (vendor spec) | 1,050,000 |
|---|---|
| Max output (vendor spec) | 128,000 |
| Knowledge cutoff | 2026-06 |
Prompt caching
| How it caches | automatic |
|---|---|
| Min prefix | 1,024 |
| Lifetime | 5-10m, up to 1h |
Thinking
| Vendor control | reasoning.effort |
|---|---|
| Accepted values | none · low · medium · high · xhigh · max |
| Default | medium applied when the request sets nothing |
| Can be turned off | Yes |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface; the vendor mapping above applies |
Model
| Modalities | text + image → text |
|---|
- Mid-tier GPT-6.1 model at half GPT-6.1 Sol's price, for visual reasoning, precise computer use and clear, natural writing. 1.05M context, 128K max output. Cached input is 0.05x the input rate. Prompts over 272K input tokens move the whole request onto the long-context rate (2x input and cache, 1.5x output). Reasoning effort runs from none to max
- minimal is not supported. Tool calling requires the Responses API
- Chat Completions is supported without tools
Use GPT-6.1 Terra in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="gpt-6.1-terra",
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "gpt-6.1-terra",
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6.1-terra",
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "gpt-6.1-terra",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("gpt-6.1-terra")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About GPT-6.1 Terra
- OpenAI describes it as a fast, efficient model for visual reasoning, precise computer use, and clear, natural writing, such as matching product photos to catalog entries, interpreting technical drawings, acting on application screens, or drafting and revising text.
- It has a 1,050,000-token context window with 128K max output tokens, text and image input, structured outputs, streaming, function calling, prompt caching, and a June 2026 knowledge cutoff.
- Input and output list prices are half of GPT-6.1 Sol's, and cached input costs 0.05x the input rate.
- Prompts above 272K input tokens move the whole request onto the long-context rate at twice the input and cache rates and 1.5x the output price.
- Reasoning effort runs from none to max with medium as the default, so reasoning can be turned off; OpenAI does not support minimal on this model, so Synthorai runs requests that ask for it at none instead of rejecting them.
- OpenAI requires the Responses API for function calling; through Synthorai, a Chat Completions request with tools still works, because the gateway sends it upstream in the form that supports function calling.
- Synthorai serves GPT-6.1 Terra through the same OpenAI-compatible API as the rest of the fleet.
FAQ
Is the GPT-6.1 Terra API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $1/M input tokens, that credit alone covers roughly 125 requests of ~8K tokens against GPT-6.1 Terra.
What is GPT-6.1 Terra best at?
Mid-tier GPT-6.1 model for visual reasoning, computer use and natural writing; 1.05M context, 128K output, reasoning effort none to max; half GPT-6.1 Sol's price, long-context pricing above 272K input tokens. See the About section for the full picture from the vendor's own release notes.
How much does GPT-6.1 Terra cost?
GPT-6.1 Terra costs $1 per million input tokens and $5 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.05/M.
What is GPT-6.1 Terra's context window?
GPT-6.1 Terra has a context window of 1.1M tokens on Synthorai. A single response can be up to 128,000 tokens long.
Does GPT-6.1 Terra support prompt caching?
Yes, automatically: OpenAI-served prompts cache with no code changes. Cached input tokens bill at $0.05/M vs $1/M uncached; prompts need a 1,024-token stable prefix to cache (TTL 5-10m, up to 1h). Prompt caching guide →
How do I get access to GPT-6.1 Terra?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gpt-6.1-terra", and you're done. One API key covers every model on the gateway.
What is GPT-6.1 Terra's knowledge cutoff?
GPT-6.1 Terra's knowledge cutoff is 2026-06, per the vendor's official documentation (as of 2026-10-27).
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Every value on this page is transcribed from the vendor's own documentation, linked above, and carries the date it was checked. Prices are compared across the catalogue; specification values that vendors define differently are shown with the difference stated rather than charted. Nothing here is measured by us, and nothing is scored.