GPT-5.6 Terra is the balanced mid-tier of OpenAI's GPT-5.6 family, described on its model page as the GPT-5.6 model that balances intelligence and cost; OpenAI positions it for high-volume business tasks such as customer support, internal tools, and document analysis, positioning it below the flagship on price.
- Input
- text image $2.5/M
- Output
- text $15/M
- Cache read
- $0.25/M
- Context
- 1.1M
- vs GPT-4o
- ~50% cheaper
- Knowledge cutoff
- 2026-02
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Price in context
Where the price sits among 60 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 | 2026-02 |
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 |
| Thinking behaviour | The Responses API also takes reasoning.mode, either standard (the default) or pro; mode and effort are independent dials. |
| 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 for workloads that balance intelligence and cost
- OpenAI positions it for high-volume business tasks at a lower price point than the flagship
- 1.05M context / 128k max output
- prompts over 272k input tokens billed at 2x input / 1.5x output
One prompt, measured through the gateway
GPT-5.6 Terra passed · 3 sentences
Enabling reasoning makes the model generate additional internal analysis tokens and perform more computation before producing the final answer, increasing usage per request. Those extra tokens are billed like other model output or are reflected in higher-priced reasoning-model rates, so even unchanged user traffic can cost substantially more. The bill doubled because average cost per request rose, likely from longer reasoning traces, more complex model routing, or both.
out 120 tok (+31 thinking) latency 2.4 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-5.6 Terra passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` and `best` were counting adjacent *matches* rather than the number of items in a run: a run of one item has zero matching adjacent pairs, and a run of length `n` has only `n - 1` such pairs. Initializing both counters to `1` for a nonempty list and resetting `cur` to `1` when the value changes makes them represent actual run lengths. Updating `best` on every iteration also ensures a run that continues through the final element is included.
out 196 tok latency 2.6 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-5.6 Terra passed · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":null,"guidance":"withheld pending the Q4 close"}
out 149 tok (+103 thinking) latency 2.3 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-5.6 Terra passed · 120 words, 0 banned, 1 question
Today, our API gateway introduces cross-provider prompt caching, reducing repeated token processing while preserving your existing model routing. Teams can store reusable system prompts, policy blocks, and long context prefixes once, then serve them to supported providers with consistent cache keys and observability. The feature tracks hit rates, latency savings, and estimated cost reductions in the dashboard you already use for requests. Why pay to recompute identical context on every call? Configure cache policies by route, tenant, model, or TTL, and fall back automatically when a provider lacks compatible caching. Built-in controls help protect sensitive data through encryption, regional settings, and explicit expiration. Start with a single endpoint, compare results across providers, and scale prompt reuse without rewriting application logic.
out 631 tok (+475 thinking) latency 6.0 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use GPT-5.6 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-5.6-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-5.6-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-5.6-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-5.6-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-5.6-terra")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About GPT-5.6 Terra
- OpenAI's own tier mapping says Terra roughly corresponds to the mini model tier of earlier GPT-5 families, and its selection guide recommends it when you want strong performance at a lower price than the flagship.
- It shares the family's 1,050,000-token context window (vendor spec), 128K max output tokens, image input, reasoning-token support, and hosted tool suite from web search to computer use and MCP, with a February 2026 knowledge cutoff.
- Reasoning effort defaults to medium when omitted and reaches the generation's new max level; the Responses-API pro mode and the beta multi-agent subagent orchestration are available here as they are across GPT-5.6.
- Cached input costs a tenth of list input with cache writes at 1.25x, and prompts above 272K input tokens are billed at 2x input and 1.5x output for the entire request.
- For teams that adopted GPT-5.5 as their default, Terra is the direct lower-cost replacement.
- On Synthorai, GPT-5.6 Terra sits behind the same OpenAI-compatible API as the rest of the fleet.
FAQ
Is the GPT-5.6 Terra API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $2.5/M input tokens, that credit alone covers roughly 49 requests of ~8K tokens against GPT-5.6 Terra.
What is GPT-5.6 Terra best at?
GPT-5.5-competitive performance at 2x lower cost; built for high-volume business workloads; full 1,050,000-token context and hosted tools. See the About section for the full picture from the vendor's own release notes.
How much does GPT-5.6 Terra cost?
GPT-5.6 Terra costs $2.5 per million input tokens and $15 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.25/M.
Does GPT-5.6 Terra support prompt caching?
Yes, automatically: OpenAI-served prompts cache with no code changes. Cached input tokens bill at $0.25/M vs $2.5/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-5.6 Terra?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gpt-5.6-terra", and you're done. One API key covers every model on the gateway.
What is GPT-5.6 Terra's knowledge cutoff?
GPT-5.6 Terra's knowledge cutoff is 2026-02, per the vendor's official documentation (as of 2026-07-10).
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Compare
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.