API429 / gpt-6-sol

GPT-6 Sol API: pricing, agent tasks and moving to 6.1

GPT-6 Sol remains a separate exact ID: do not replace it with GPT-6.1 Sol in examples or estimates. The manufacturer describes it for complex code and agent workflows and lists the newer Sol separately. Start with a control task and decide whether to migrate based on your evaluation.

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Published: · Updated:

Exact ID: gpt-6-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 ratesManufacturer: 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
$0.060 / 1,000,000 cached input tokens · Context up to 272,000 tokens inclusive
$0.120 / 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

API429$2.7
Manufacturer$9
Difference from manufacturer price70%API429 costs less for this workload

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.

Existing coding sequences: preserve the baseline result

For an application already using GPT-6 Sol, first capture the working configuration and control responses. Choose several tasks: explaining a failure, finding an edge case, suggesting a small change and checking its consequences. For each, record the input, expected result and permitted scope of edits. The manufacturer positions Sol for complex programming and agent workflows; this statement of purpose does not establish the quality of a specific patch. If the process includes several requests, evaluate the whole sequence, including error recovery. This page’s example asks only for textual function analysis without executing code. That limited start helps identify format or access issues before adding long history, files and extra operations that affect cost and results.

GPT-6 Sol and GPT-6.1 Sol have different contracts

The official GPT-6 Sol page explicitly identifies GPT-6.1 Sol as a newer model. However, the older Sol’s none mode makes migration a substantive change: the newer version does not support it. In the OpenAI API, GPT-6 Sol allows function calling in Chat Completions only with reasoning_effort none, while reasoning with tools requires Responses. These details concern the manufacturer’s native contract. They do not establish support for the same fields in your API429 call. First send basic messages without tools and implement correct response parsing. When comparing versions, preserve the original request and separately note parameters without direct equivalents. Successfully substituting a new ID does not prove an equivalent execution mode or identical usage.

Input, output and repeated history: count the whole process

OpenAI Standard base rates for GPT-6 Sol’s ordinary input and output match GPT-6.1 Sol, but cache-read rates differ. Separate new input, repeated history and output from every attempt in your budget. The calculator shows ordinary input and output token costs for independent requests by default, excluding cache and tools. It does not automatically reconstruct an agent loop or know how often your application will retry a failed task. For manufacturer input above 272,000 tokens, higher rates cover the whole request rather than just the excess. Check API429 customer rates separately in the current catalog. To decide on migration, compare accepted-fix cost, retries and manual rework on the same material.

Images and reproducible integration checks

The official September 25, 2026 changelog records an image encoding fix for GPT-6 Sol and GPT-6 Luna. OpenAI recommends rerunning evaluations involving images. If an old control set contains interface screenshots or diagrams, account for its evaluation date: previous results may not describe current manufacturer behavior. This does not mean your API429 request supports every image format. Check the transmission method against gateway documentation and start with a small file. This page’s text example needs no images. Test long input, streaming completion and application error handling separately. Manufacturer context specifications do not establish your key’s effective limit or guarantee successful execution of a multipart request.

Public model observations

Over the last 24 hours: mean successful response time — 17 s; service error rate — 9.1%. 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-sol","messages":[{"role":"user","content":"For def first(xs): return xs[0], suggest behavior for an empty list and one test. 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-sol"
if model not in available:
    raise ValueError("Model is unavailable to this key")
payload = json.loads("{\"model\":\"gpt-6-sol\",\"messages\":[{\"role\":\"user\",\"content\":\"For def first(xs): return xs[0], suggest behavior for an empty list and one test. 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-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-sol","messages":[{"role":"user","content":"For def first(xs): return xs[0], suggest behavior for an empty list and one test. 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

  1. Create an HTTP Request with method POST and URL https://gateway.api429.com/v1/chat/completions.
  2. Store the Bearer key in credentials; add Content-Type: application/json. Do not put the key in an exported workflow example.
  3. Send the JSON from curl: the exact model gpt-6-sol, messages, max_tokens and stream=false.
  4. After the HTTP node, check choices, finish_reason and 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.

ModelAPI429: input / output per 1M tokens
GPT 6 Lunagpt-6-luna$0.03 / $0.15
GPT 5.6 Lunagpt-5.6-luna$0.3 / $1.8
GPT 6.1 Solgpt-6.1-sol$0.6 / $3

Version and release date

Manufacturer API release date, OpenAI: . The exact predecessor has not been established in the reviewed sources. 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

Should I automatically change the ID to GPT-6.1 Sol?

Test the new option on your tasks first. The models differ in none support and native tool calling in Chat Completions. Preserve the original configuration and compare results, usage and error handling before switching all requests.

Are functions allowed in Chat Completions?

For the direct OpenAI API, GPT-6 Sol describes this only with reasoning_effort none. The manufacturer directs reasoning with tools to Responses. This API429 page shows the basic text contract and does not promise transfer of tool modes.

Why should I retest screenshot tasks?

OpenAI announced an image encoding fix for GPT-6 Sol on September 25, 2026 and recommended repeating image evaluations. Compare results using the same files and record the date; this is not an existing quality measurement for your gateway scenario.

Is the 272K boundary the model’s technical limit?

No. Here it is a manufacturer pricing condition. Maximum context and the effective request limit through API429 are separate specifications. For large requests, count the full input including history rather than only the latest user message.

Sources and review

Editorial review: . Pricing and general calculator · Getting started · Compare the family: OpenAI

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