GPT-5.6 vs GPT-5.6 Terra
Which one, when — curated verdict, not a benchmark table
Terra is half-price GPT-5.6 ($2.5/$15 vs $5/$30) on the same 1.05M context. If your traffic does not need the full model’s ceiling, Terra is the same family with the same window at half the spend — the definition of a right-sizing move.
Pricing
| GPT-5.6 | GPT-5.6 Terra | Δ | |
|---|---|---|---|
| Input / 1M tokens | $5 | $2.5 | 2× |
| Output / 1M tokens | $30 | $15 | 2× |
| Cache read / 1M tokens | $0.5 | $0.25 | 2× |
| Cache write | no separate charge | no separate charge | — |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| GPT-5.6 | GPT-5.6 Terra | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | 5–10m, up to 1h | 5–10m, up to 1h |
| Minimum cached prefix | 1024 tokens | 1024 tokens |
Specs
| GPT-5.6 | GPT-5.6 Terra | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-07-09 | 2026-07-09 |
| Knowledge cutoff | 2026-02 | 2026-02 |
| Context window | 1.1M | 1.1M |
| Max output | 128K | 128K |
| Thinking parameter | reasoning.effort | reasoning.effort |
| Accepted values | reasoning.effort
| reasoning.effort
|
| Default | medium | medium |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GPT-5.6 · GPT-5.6 Terra
One prompt, both models — measured through the gateway
GPT-5.6 passed · 3 sentences
Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.
out 126 tok (+30 thinking) latency 3.0 s
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 passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: best = max(best, cur) cur = 1 return max(best, cur) ``` The original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.
out 277 tok (+93 thinking) latency 4.2 s
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 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 179 tok (+118 thinking) latency 3.7 s
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 passed · 120 words, 0 banned, 1 question
Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.
out 628 tok (+473 thinking) latency 7.3 s
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.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
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",
# model="gpt-5.6-terra", # uncomment this line, comment the one above
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",
// model: "gpt-5.6-terra", // uncomment this line, comment the one above
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",
# "model": "gpt-5.6-terra", # uncomment this line, comment the one above
"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",
// Model: "gpt-5.6-terra", // uncomment this line, comment the one above
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")
// .model("gpt-5.6-terra") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, GPT-5.6 or GPT-5.6 Terra?
GPT-5.6 Terra is cheaper on input / 1m tokens ($2.5 vs $5, 2.0× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test GPT-5.6 against GPT-5.6 Terra without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do GPT-5.6 and GPT-5.6 Terra support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.