GPT-5.6 Sol is the flagship of OpenAI's GPT-5.6 family, described on its page as the frontier model for complex professional work; OpenAI's guidance is to start here for complex reasoning and coding.
- 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 |
|---|
- Family flagship for frontier reasoning and long-horizon agentic work
- introduces the max reasoning effort, a pro reasoning mode in the Responses API, and a beta multi-agent capability that coordinates subagents (3 concurrent by default)
- the bare gpt-5.6 alias routes to Sol
- prompts over 272k input tokens billed at 2x input / 1.5x output
One prompt, measured through the gateway
GPT-5.6 Sol passed · 3 sentences
Enabling reasoning makes the model generate additional internal tokens before producing an answer, so each request consumes more billable compute. Reasoning may also increase response length, latency, tool calls, and retries, further raising per-request cost. In short, usage volume may be unchanged, but the cost per task increased enough to double the bill.
out 111 tok (+34 thinking) latency 2.3 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 Sol 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: cur = 1 best = max(best, cur) return best ``` The original function counted equal *adjacencies* rather than items, so a run of length one produced `0` and every longer run was undercounted by one. Initializing `cur` and `best` to `1` for nonempty input makes them represent item counts, resetting `cur` to `1` starts a new run at the current item, and updating `best` on every iteration ensures a run reaching the final index is included.
out 316 tok (+135 thinking) latency 5.2 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-5.6 Sol passed · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":"11+","guidance":"withheld pending the Q4 close"}
out 194 tok (+147 thinking) latency 3.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-5.6 Sol passed · 120 words, 0 banned, 1 question
Today, we’re introducing Provider Prompt Cache, a new API gateway feature that reuses prompt prefixes across supported AI providers, reducing latency, token costs, and duplicated processing. Teams can define cache policies once, route requests dynamically, and preserve provider flexibility without rewriting application logic. Switching models during testing or failover? The gateway identifies eligible prompt segments, applies provider-specific caching controls, and reports hits, misses, savings, and expiration details through unified logs and metrics. Configurable TTLs, tenant isolation, encryption, and cache-bypass options help teams balance performance, privacy, and freshness for every workload. Provider Prompt Cache is available today in beta through the dashboard and API, with SDK examples and migration guidance included. […]
out 733 tok (+564 thinking) latency 7.7 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use GPT-5.6 Sol 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-sol",
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-sol",
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-sol",
"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-sol",
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-sol")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About GPT-5.6 Sol
- It combines a 1,050,000-token context window (vendor spec) and 128K max output tokens with image input and the full hosted tool suite (web search, file search, code interpreter, hosted shell, computer use, MCP), with a February 2026 knowledge cutoff, and the bare gpt-5.6 alias routes to Sol as the family default.
- The generation introduces a new max reasoning-effort level above xhigh for the most complex tasks, and OpenAI suggests evaluating whether max beats xhigh on your own workload rather than assuming it.
- It also adds a pro reasoning mode in the Responses API for difficult tasks that can tolerate higher latency and token usage, alongside a beta multi-agent capability that lets the model spin up and coordinate subagents in parallel, defaulting to three concurrent subagents.
- Migration advice from OpenAI is to start at the reasoning setting you used on GPT-5.5 or GPT-5.4, then test one level lower, because this generation often holds quality with fewer tokens.
- Prompts above 272K input tokens bill at 2x input and 1.5x output for the full request.
- Synthorai serves GPT-5.6 Sol natively on its OpenAI-compatible API.
FAQ
Is the GPT-5.6 Sol 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 Sol.
What is GPT-5.6 Sol best at?
Frontier reasoning and long-horizon agentic work; new max reasoning effort, pro mode, and multi-agent subagents; the bare gpt-5.6 alias routes here by default. See the About section for the full picture from the vendor's own release notes.
How much does GPT-5.6 Sol cost?
GPT-5.6 Sol 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 Sol 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 Sol?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gpt-5.6-sol", and you're done. One API key covers every model on the gateway.
What is GPT-5.6 Sol's knowledge cutoff?
GPT-5.6 Sol's knowledge cutoff is 2026-02, per the vendor's official documentation (as of 2026-07-10).
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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.