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GPT-6 Sol API

Access OpenAI GPT-6 Sol—also searched as GPT 6 Sol / gpt-6-sol— through EvoLink's unified chat API. Test coding, multi-step agents, tool use before integrating.

OpenAIText GenerationAvailable
from $1.800 / 1M input tokens$2.000 official price-10%
API docs
Chat CompletionsResponses APIReasoning effortPrompt cachingTool use
Production routeLive
Context
1.05M context · 128K max output
Best For
Complex coding, agentic workflows, tool-heavy tasks
Input
Text + images
Output
Text · JSON (structured output) · tool calls
ModelsPricingReadmeAPI

Choose GPT-6 Sol

One exact model ID on the OpenAI-compatible integration. Short- and long-context rates for all four token roles are listed on the card.

GPT-6 Sol

OpenAI's GPT-6 model built to power complex coding and agentic workflows.

Selected
Model ID
gpt-6-sol
Best for

Complex coding, agent loops with many tool calls, and long-running implementation tasks

< 272K
Input
$1.800 / 1M-10%
122.4 cr / 1M$2.000official price
Cache read
$0.181 / 1M-10%
12.3 cr / 1M$0.200official price
Cache write
$2.250 / 1M-10%
153 cr / 1M$2.500official price
Output
$9.000 / 1M-10%
612 cr / 1M$10.000official price
≥ 272K
Input
$3.600 / 1M-10%
244.8 cr / 1M$4.000official price
Cache read
$0.362 / 1M-10%
24.6 cr / 1M$0.400official price
Cache write
$4.500 / 1M-10%
306 cr / 1M$5.000official price
Output
$13.500 / 1M-10%
918 cr / 1M$15.000official price

All rates are per 1M tokens, shown in USD and credits, and reflect your account's current pricing. Above 272K prompt tokens the long-context tier applies: ×2 for input and cache roles, ×1.5 for output.

GPT-6 Sol pricing

Estimate what one GPT-6 Sol request costs before you integrate. The calculator uses your account's current rates, with official pricing as a reference.

Request calculator

Enter the token mix for one request.

Estimated request cost

GPT-6 Sol
Short-context rate
USD$0.0046
Credits0.3085

Official estimate $0.0051 · save $0.0006 (10%)

Input tokens0.1224 cr
Cache read tokens0.0025 cr
Cache write tokens0 cr
Output tokens0.1836 cr

Minimum charge: 0.01 credits per request. At 272K prompt tokens, input and cache roles are billed at ×2 and output at ×1.5. Tool calls are not multiplied.

Budget guide

Approximate requests using the current token mix.
Add credits
$10
About 2106 requests

For quick testing

$50
About 11021 requests

For regular development

$100
About 22042 requests

For production evaluation

GPT-6 Sol API

OpenAI's GPT-6 model built to power complex coding and agentic workflows — multi-file changes, tool-driven agents, and long-running implementation tasks — available through one EvoLink API key with Chat Completions and Responses.

GPT-6 Sol is served on EvoLink under the model ID gpt-6-sol through Chat Completions · Responses, with the same API key and balance you use for every other model. It offers a 1.05M context window and up to 128K output tokens, plus coding, multi-step agents, tool use.

GPT-6 Sol

GPT-6 Sol specs and capabilities

Numbers come from the EvoLink route configuration; capabilities are what the API exposes today.

Context window
1.05M tokens
Max output
128K tokens
Input
Text + images
Output
Text · JSON (structured output) · tool calls
Reasoning
Configurable reasoning effort
Tool use
Function calling with multi-step tool sequences
Prompt caching
Cache write + cache read rates
Long-context tier
×2 in / ×1.5 out above 272K prompt tokens
Protocols
Chat Completions · Responses
Model ID
gpt-6-sol

What GPT-6 Sol is best for

One exact model ID for coding and agent traffic; move the hardest end-to-end work to GPT-6 Astra and focused high-volume calls to GPT-6 Luna. OpenAI reports DeepSWE v1.1 68.8% at max effort and a 1.3% coding-deception rate, down from 10.4% for GPT-5.6 Sol (vendor-reported).

Ship Multi-File Code Changes

Plan, edit, and test across several files in one task, and iterate on failing tests instead of stopping at a single plausible suggestion.

Run Tool-Driven Agents

Chain function calls, file search, and other built-in tools on the Responses API; on Chat Completions, function calling requires reasoning_effort none.

Tune Reasoning per Request

Choose reasoning_effort from none to max on each call: none for fast tool routing, higher levels for debugging and design reviews. Reasoning tokens are billed as output.

Keep Project Context in One Window

Fit repository files, specs, and tool results into a 1.05M-token window; test retrieval and instruction retention on your own workloads before relying on the full window.

Two ways to use GPT-6 Sol: EvoLink API or Agent

Use the EvoLink API for product backends and batch jobs, or call GPT-6 Sol from Codex, Claude, or Gemini for coding and analysis workflows. Both paths share the same EvoLink API key, balance, model ID, and request history.

Option 1

Integrate with the EvoLink API

Best for: product backends, batch jobs, automated pipelines

Send OpenAI-compatible Chat Completions (or Anthropic Messages) requests to EvoLink and control the model ID, system prompt, output budget, tools, and structured output.

  1. 1Create an EvoLink API key in the console
  2. 2Point your OpenAI or Anthropic SDK at the EvoLink base URL and select the model ID shown above
  3. 3Send one representative request and read the usage field for input, cached, and output tokens
  4. 4Set max_tokens and retries per task; keep tool-call IDs and results across turns
Create EvoLink API Key
Option 2

Call it with an Agent

Best for: coding, review, and analysis tasks in Codex, Claude, and Gemini

Give the Agent the task, the inputs to include, and the acceptance criteria. It assembles the request, calls GPT-6 Sol through EvoLink, and returns the answer with token usage.

  1. 1Set EVOLINK_API_KEY in your local environment; never put it in code or a prompt
  2. 2Describe the task, the inputs to include, and the expected output format
  3. 3Ask the Agent to call GPT-6 Sol through EvoLink and show the request before sending
  4. 4Let the Agent report the answer, token usage, and any error body

GPT-6 Sol API code example and error handling

This example shows the shortest runnable request: an OpenAI-compatible Chat Completions call with a system prompt, a user message, and an output budget. Open the API tab for the complete parameter and response reference.

View complete API docs
cURL
curl -X POST https://api.evolink.ai/v1/chat/completions \
  -H "Authorization: Bearer $EVOLINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-sol",
    "messages": [
      { "role": "system", "content": "You are a senior engineer fixing a failing test suite." },
      { "role": "user", "content": "These tests fail after the refactor. Find the root cause and propose a minimal patch:\n<test output>" }
    ],
    "reasoning_effort": "medium",
    "max_tokens": 4096
  }'

# Use the exact ID gpt-6-sol on both Chat Completions and Responses.
# reasoning_effort accepts none, low, medium (default), high, xhigh, and
# max; reasoning tokens are billed as output. On Chat Completions, function
# calling works only with reasoning_effort set to none; use Responses for
# built-in tools and function calling at higher effort.

Invalid request or unsupported parameter

Check the model ID, the messages array, and parameter ranges against the API reference; remove fields this route does not support.

Authentication or balance issue

Check the Authorization bearer token and confirm the available balance in the console.

Context length exceeded

Prompt tokens exceed the GPT-6 Sol context window. Trim or retrieve only the relevant evidence and reuse cached prefixes.

Rate limited (429)

Back off and retry with jitter; batch or queue requests instead of sending parallel bursts.

Content or tool call rejected

Review sensitive content, malformed tool-call arguments, and JSON schema mismatches before retrying.

Before you take GPT-6 Sol to production

Four checks that decide cost and stability.

01

Pick the exact GPT-6 Sol model ID

Use gpt-6-sol on both Chat Completions and Responses; there is no generic gpt-6 alias.

Required
02

Choose the endpoint for tool calls

Use Responses for built-in tools and function calling; on Chat Completions, function calling works only with reasoning_effort none.

Required
03

Structure prompts for cache reads

Put stable instructions and reference material first so cache reads replace full-price input on repeated calls.

Recommended
04

Budget for the 272K threshold and measure with usage

Above 272K prompt tokens the whole request uses long-context rates; reconcile cost from the usage object before scaling traffic.

Recommended

Four-part prompt caching

Uncached input, cache reads, cache writes, and output are settled separately from the final usage object. Cache writes are not charged again as ordinary input.

Long-context threshold

At more than 272K prompt tokens, input and both cache roles use 2x rates while output uses 1.5x rates for the entire request.

GPT-6 Sol vs GPT-6 Astra and GPT-5.6 Sol

EvoLink

Compare GPT-6 Sol with GPT-6 Astra and GPT-5.6 Sol using EvoLink prices per 1M tokens.

GPT-6 Sol
EvoLink price / 1M (input / output)$1.8 / $9
Context window1.05M
Prompt cachingCache read + cache write
Best forComplex coding, agent loops with many tool calls, and long-running implementation tasks
GPT-6 Astra
EvoLink price / 1M (input / output)$9 / $45
Context window1.05M
Prompt cachingCache read + cache write
Best forThe hardest end-to-end reasoning, coding, computer-use, and research work
GPT-5.6 Sol
EvoLink price / 1M (input / output)$3.6 / $18
Context window1.05M
Prompt cachingCache read + cache write
Best forPrevious-generation Sol tier for hard reasoning and coding agents

Base-context prices reflect your account's current pricing; signed-out visitors see Default rates. Long-context multipliers are listed in the Pricing section.

GPT Model Family

Same API key and balance — switch tiers without changing your integration.

Compare all GPT models
GPT-6.1 Sol

GPT-6.1 Sol

Updated Sol for multi-file coding and Responses-based agents

View model
GPT-6 Astra

GPT-6 Astra

OpenAI's most capable model for the hardest end-to-end work

View model
GPT-6 Luna

GPT-6 Luna

The most efficient GPT-6 model for focused, high-volume tasks

View model

Other text models on EvoLink besides GPT-6 Sol

Claude Opus 5

Claude Opus 5

Anthropic's previous Opus-tier flagship for repository-scale coding, long-running agents, and judgment-heavy review.

View model
DeepSeek V4 Pro

DeepSeek V4 Pro

DeepSeek's strongest tier, with thinking mode and a 1M context window for advanced reasoning.

View model
Gemini 3.8 Flash

Gemini 3.8 Flash

Google's most capable Flash model, with gains on software engineering and agentic tasks and a 1M context window.

View model
Grok 4.7

Grok 4.7

xAI reasoning and tool-use model with a 500K context window and metered server-side tools.

View model

GPT-6 Sol guides and related reading

GPT-6 Sol release date and rollout

GPT-6 Sol release date and rollout

Released September 22, 2026; rollout by channel and what OpenAI reports for coding and agent honesty

Read guide
GPT-6 Sol vs GPT-5.6 Sol

GPT-6 Sol vs GPT-5.6 Sol

Half the list price, same 1.05M context, the Chat Completions tool-calling rule, and a cost-per-accepted-task upgrade gate

Read guide
GPT-6 API guide (Astra)

GPT-6 API guide (Astra)

First call, reasoning effort, the 272K and cache billing rules shared across the GPT-6 family

Read guide

GPT-6 Sol API - FAQ

Which model ID should I use?

Use gpt-6-sol. There is no generic gpt-6 alias, and the same ID works on Chat Completions and Responses.

How much does GPT-6 Sol cost on EvoLink?

EvoLink default rates are $1.800 input and $9.000 output per 1M tokens, against OpenAI’s $2.000 / $10.000 list price. Cache reads are $0.181 and cache writes $2.250 per 1M tokens; logged-in users see the rates for their own account.

How is cached input billed?

Cache reads and cache writes are separate token segments. Neither segment is billed again as ordinary input.

When do long-context rates apply?

When total prompt tokens exceed 272,000, the whole request uses the long-context multipliers: 2x for input and both cache roles, 1.5x for output.

Which reasoning effort levels are supported?

reasoning_effort on Chat Completions and reasoning.effort on Responses accept none, low, medium (the default), high, xhigh, and max. Higher effort spends more reasoning tokens, which are billed as output; none skips reasoning for the lowest latency.

Can I use function calling on Chat Completions?

Only with reasoning_effort set to none. For built-in tools, or function calling at a higher reasoning effort, call the Responses API with the same gpt-6-sol ID.

What are the context window and maximum output?

GPT-6 Sol has a 1,050,000-token context window, with up to 922,000 input tokens and up to 128,000 output tokens per request.

Should I upgrade from GPT-5.6 Sol to GPT-6 Sol?

GPT-6 Sol keeps the 1.05M context, 128K output and six reasoning levels of GPT-5.6 Sol, at half the OpenAI list price, with a newer knowledge cutoff (April 20, 2026). OpenAI reports DeepSWE v1.1 68.8% at max effort and a coding-deception rate of 1.3% versus 10.4% for GPT-5.6 Sol; those are vendor numbers. Decide on your own tasks with cost per accepted task, not price per token; the GPT-6 Sol vs GPT-5.6 Sol guide below has the acceptance set and the one contract change to check first: function calling on Chat Completions requires reasoning_effort none.

GPT-6 Sol, Astra or Luna: which one should I use?

Use Sol for complex coding and tool-driven agents; move the hardest end-to-end reasoning, research and computer-use work to GPT-6 Astra; send focused, high-volume calls such as classification, extraction and routing to GPT-6 Luna. All three share the 1.05M context, the same integration and the same 272K long-context rule, so you can split traffic by task and keep one API key. The GPT collection page compares them side by side.