Published: · Updated:
Exact ID: gpt-6.1-sol. Check access for your key using GET /v1/models; a published model does not guarantee access for every account.
Prices and billing units
| API429: published rates | Manufacturer: base Standard |
|---|---|
$0.750 / 1,000,000 cache-write input tokens · Context up to 272,000 tokens inclusive $1.50 / 1,000,000 cache-write input tokens · Context over 272,000 tokens $0.030 / 1,000,000 cached input tokens · Context up to 272,000 tokens inclusive $0.060 / 1,000,000 cached input tokens · Context over 272,000 tokens $0.60 / 1,000,000 input tokens · Context up to 272,000 tokens inclusive $1.20 / 1,000,000 input tokens · Context over 272,000 tokens $3.00 / 1,000,000 output tokens · Context up to 272,000 tokens inclusive $4.50 / 1,000,000 output tokens · Context over 272,000 tokens | $2 / $10 per 1M ordinary input / output tokens Standard; input ≤272,000 tokens Manufacturer sourceChecked: Oct 24, 2026. Cache, tools and special modes are excluded from this rate pair. |
API429 catalog generated . This dates the catalog data, not the model’s addition.
Workload budget: input and output
Ordinary input and output are counted, excluding cache, tools, fees and repeated calls. This estimates spending; it does not validate whether a request is accepted. RUB results use only your entered rate; they are neither a bank quote nor a top-up amount.
Specifications and compatibility boundaries
The manufacturer specifies a context window of 1,050,000 and a maximum output of 128,000 tokens for its own API (specification: OpenAI, checked Oct 24, 2026). These are not verified effective API429 limits: the current public catalog does not publish them. Confirm the required volume and mode before migrating your workload.
API429 capabilities published for this exact ID: chat function_calling reasoning . A missing property does not mean a prohibition, but it does not establish support. Check input format, tools, JSON/schema, reasoning and streaming separately for the selected method.
Code review: compare a completed fix
When moving from GPT-6 Sol, prepare a small task with a known success criterion: identify an empty-list handling bug, explain the cause and suggest a minimal fix. Then add a longer example with several files and restrictions on permitted changes. OpenAI describes GPT-6.1 Sol as a model for complex code, computer use and professional work, but this is not a quality measurement on your project. Compare both versions against the same requirements. Record missed cases, unnecessary changes, repeated requests and actual usage. If the task includes documents, require the passage supporting each conclusion. The demonstration text below checks only that an answer can be obtained, rather than the accuracy of a full repository analysis.
Moving from GPT-6 Sol changes the supported modes
The manufacturer’s GPT-6.1 Sol does not support none or minimal reasoning effort, although GPT-6 Sol allows none. Replacing the name in an existing integration may therefore require revised settings. In the native OpenAI API, tools for GPT-6.1 Sol require Responses; Chat Completions is described for requests without tools. These rules cannot be transferred automatically to the API429 contract. This page’s example uses only model, messages, max_tokens and stream from the public gateway specification. Do not add reasoning parameters or internal fields from another SDK without checking support. Preserve the original configuration before switching a production queue, and check response parsing and error handling. Compare the final result on the same task separately, since the old none mode has no direct equivalent in the new model.
Budgeting: identical base rates do not mean identical usage
GPT-6.1 Sol and GPT-6 Sol have the same verified OpenAI Standard base rates for ordinary input and output, but their published cache-read rates differ. This is a reason to separate token types in your estimate, rather than declare fixed savings on every task. By default, the calculator estimates ordinary input and output for independent requests, excluding cache and tools. Add conversation history, retries and billable operations to your own budget. Take the current customer price of API429 from its current catalog: the manufacturer’s price does not determine it. OpenAI has a separate threshold for long input at 272,000 tokens. Above it, rates change for the entire request, including output, rather than only text beyond the threshold.
From the first response to a robust integration
Start with a short synthetic example without files or external actions. Check that gpt-6.1-sol appears in your key’s model list, then set a small max_tokens and obtain a regular non-streaming response. Next, test stream and completion of reading in your client separately. A response limit in a trial request restricts its volume but does not establish maximum context through the gateway. OpenAI specifications are manufacturer information in the sources; the model name does not establish an effective API429 limit. When transferring a document or long history, increase input gradually and record actual usage. The public mean latency cannot replace measuring completion time for your own task. After a timeout, establish status and usage before automatically repeating a paid request.
Public model observations
Over the last 24 hours: mean successful response time — 82 s; service error rate — 0.4%. Data as of .
Source: public 24-hour statistics. The mean is neither a median nor a response time guarantee; the error rate and observation window refer to the API429 service. Model quality is not measured here.
First request: curl, Python, JavaScript
Create a key, check your balance and set API429_API_KEY on the server. The generation example incurs a charge. It does not retry automatically; a timeout does not prove the operation was not charged. Inspect the content and actual usage after receiving a response. Python and JavaScript first check the available model list.
curl
Before generation, check the exact ID in the response from GET https://gateway.api429.com/v1/models using the same Bearer key.
curl --fail-with-body https://gateway.api429.com/v1/chat/completions \
-H "Authorization: Bearer $API429_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-6.1-sol","messages":[{"role":"user","content":"Find two edge cases in def total(xs): return xs[0] + xs[-1]. Do not execute code."}],"max_tokens":512,"stream":false}'Python 3
import json
import os
import urllib.request
key = os.environ["API429_API_KEY"]
base = "https://gateway.api429.com/v1"
headers = {"Authorization": "Bearer " + key, "Content-Type": "application/json"}
models_request = urllib.request.Request(base + "/models", headers=headers)
with urllib.request.urlopen(models_request, timeout=30) as response:
available = {item["id"] for item in json.load(response)["data"]}
model = "gpt-6.1-sol"
if model not in available:
raise ValueError("Model is unavailable to this key")
payload = json.loads("{\"model\":\"gpt-6.1-sol\",\"messages\":[{\"role\":\"user\",\"content\":\"Find two edge cases in def total(xs): return xs[0] + xs[-1]. Do not execute code.\"}],\"max_tokens\":512,\"stream\":false}")
request = urllib.request.Request(base + "/chat/completions",
data=json.dumps(payload).encode("utf-8"), headers=headers, method="POST")
with urllib.request.urlopen(request, timeout=60) as response:
result = json.load(response)
choice = result["choices"][0]
if choice.get("finish_reason") != "stop" or not choice.get("message", {}).get("content"):
raise ValueError("Incomplete or empty response")
print(choice["message"]["content"])
print(result.get("usage"))JavaScript: Node.js 18+
const key = process.env.API429_API_KEY;
if (!key) throw new Error("Set API429_API_KEY");
const base = "https://gateway.api429.com/v1";
const headers = { Authorization: "Bearer " + key, "Content-Type": "application/json" };
const model = "gpt-6.1-sol";
const available = await fetch(base + "/models", { headers, signal: AbortSignal.timeout(30000) });
if (!available.ok) throw new Error("Models HTTP " + available.status);
if (!(await available.json()).data.some(item => item.id === model)) throw new Error("Model unavailable to this key");
const response = await fetch(base + "/chat/completions", {
method: "POST", headers, signal: AbortSignal.timeout(60000),
body: JSON.stringify({"model":"gpt-6.1-sol","messages":[{"role":"user","content":"Find two edge cases in def total(xs): return xs[0] + xs[-1]. Do not execute code."}],"max_tokens":512,"stream":false})
});
if (!response.ok) throw new Error("Generation HTTP " + response.status);
const result = await response.json();
const choice = result.choices?.[0];
if (choice?.finish_reason !== "stop" || !choice?.message?.content) throw new Error("Incomplete or empty response");
console.log(choice.message.content);
console.log(result.usage);The examples use basic fields from the published API contract. They are checked offline; no successful paid execution or output accuracy is claimed.
n8n: minimal HTTP Request
- Create an HTTP Request with method POST and URL
https://gateway.api429.com/v1/chat/completions. - Store the Bearer key in credentials; add
Content-Type: application/json. Do not put the key in an exported workflow example. - Send the JSON from curl: the exact model
gpt-6.1-sol, messages, max_tokens and stream=false. - After the HTTP node, check
choices,finish_reasonand the response content before writing the result to your production system.
HTTP Request documentation. This recipe describes HTTP Request. Compatibility with specific Agent nodes, SDKs and other API methods needs separate verification.
Three models for your own comparison
These are options for a controlled comparison on the same task, rather than a quality ranking. Base rates apply to a short text request; see the catalog for full pricing.
| Model | API429: input / output per 1M tokens |
|---|---|
GPT 6 Astragpt-6-astra | $3 / $15 |
GPT 6 Solgpt-6-sol | $0.6 / $3 |
Deepseek V4 Prodeepseek-v4-pro | $0.396 / $1.188 |
Version and release date
Manufacturer API release date, OpenAI: . Previous generation: GPT-6 Sol; this does not automatically replace the ID in your integration. The date of addition to API429 is unknown. Current availability is determined by the catalog and key permissions, rather than the manufacturer’s historical release date.
Frequently asked questions
What changed compared with GPT-6 Sol?
Official documentation calls GPT-6.1 Sol a new Sol version. Practical differences include the absence of none and minimal, native API requirements for tools and a different cache-read rate. Validate migration on your own task rather than matching base prices.
Can I use an old request with tools?
In the direct OpenAI API, tool calling for this model requires Responses. This page shows a small text Chat Completions request. Confirm whether your tools can transfer through API429 against the client contract and with a separate check.
How do I account for input above 272,000 tokens?
At OpenAI, exceeding this boundary changes the price of the entire request: input and cache rates double, while output costs 1.5 times its base rate. This manufacturer pricing condition does not establish the technical capacity of an API429 request.
Why should the first test be short?
It lets you check the ID, authorization, messages format and response parsing separately. You can then increase complexity and volume. One successful short answer does not establish access to every mode or suitability for applying changes automatically.
Sources and review
- developers.openai.com/api/docs/models/gpt-6.1-sol
- developers.openai.com/api/docs/guides/latest-model
- developers.openai.com/api/docs/changelog
- gateway.api429.com/api/public/openapi.json
Editorial review: . Pricing and general calculator · Getting started · Compare the family: OpenAI