GPT-5.6 is not a separate model.
- Input
- text image $5/M
- Output
- text $30/M
- Cache read
- $0.5/M
- Context
- 1.1M
- 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 |
|---|
- Not a separate model: the bare gpt-5.6 alias routes to GPT-5.6 Sol, the family's frontier tier. Chat Completions, Responses and Batch (no Realtime, audio, embeddings or fine-tuning)
- prompts over 272k input tokens are billed at 2x input / 1.5x output for the whole request
One prompt, 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
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
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
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
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use GPT-5.6 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",
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",
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",
"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",
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")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About GPT-5.6
- OpenAI documents the bare gpt-5.6 name as an alias that routes requests to GPT-5.6 Sol, the frontier tier of the GPT-5.6 family, described on its page as the frontier model for complex professional work and as roughly corresponding to the unsuffixed model tier used in earlier GPT-5 families.
- Calling it therefore gets you Sol's specifications: a 1,050,000-token context window with a 922,000-token maximum input, 128,000 max output tokens, text and image input with text output, reasoning-token support, and a February 2026 knowledge cutoff.
- Chat Completions, Responses, and Batch are supported; Realtime, audio, embeddings, and fine-tuning are not.
- OpenAI positions the generation as a new quality and efficiency baseline for complex production workflows, singling out token efficiency and stronger frontend layout and design judgment, and its migration advice is to keep the reasoning setting you used on GPT-5.5 or GPT-5.4 as the baseline and then test one level lower, because this generation often holds quality with fewer tokens.
- Reasoning effort spans none through the new max level and resolves to medium when omitted, in standard and pro mode alike.
- The release also adds programmatic tool calling, a beta multi-agent mode that coordinates subagents in parallel, explicit prompt caching alongside the implicit kind, and persisted reasoning across turns through reasoning.context.
- Two billing details matter: prompts above 272K input tokens are charged at 2x input and 1.5x output for the whole request, and cache writes cost 1.25x the uncached input rate.
- Synthorai serves GPT-5.6 on the same OpenAI-compatible chat completions endpoint as the rest of the family.
FAQ
Is the GPT-5.6 API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $5/M input tokens, that credit alone covers roughly 24 requests of ~8K tokens against GPT-5.6.
What is GPT-5.6 best at?
An alias that routes to GPT-5.6 Sol; the frontier tier for complex professional work; prompts above 272K input billed at 2x input and 1.5x output. See the About section for the full picture from the vendor's own release notes.
How much does GPT-5.6 cost?
GPT-5.6 costs $5 per million input tokens and $30 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.5/M.
Does GPT-5.6 support prompt caching?
Yes, automatically: OpenAI-served prompts cache with no code changes. Cached input tokens bill at $0.5/M vs $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?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gpt-5.6", and you're done. One API key covers every model on the gateway.
What is GPT-5.6's knowledge cutoff?
GPT-5.6's knowledge cutoff is 2026-02, per the vendor's official documentation (as of 2026-07-28).
Related models
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.