GPT-6 Sol and Luna Models — A Practical Guide for Developers and Businesses

GPT-6 Sol and Luna, released by OpenAI on September 22, 2026, are two specialized variants of the GPT-6 family: Sol is built for complex reasoning, deep software engineering, and agentic workflows, while Luna is optimized for high-throughput, low-latency, cost-efficient generation such as classification, extraction, and summarization. As of September 2026, both models share the same 1,050,000-token context window and 128,000-token maximum output (llm-stats). As of October 2026, both models are live on OpenAI's official API and consumer surfaces, and they are also listed in HeFu's catalog as gpt-6-sol and gpt-6-luna; the authoritative availability list for HeFu is its official catalog page. Developers who want a unified OpenAI-compatible gateway for multi-vendor models can call them through HeFu alongside the GPT-5.6 family (Terra, Sol, Luna) and the Claude, DeepSeek, Kimi, and Gemini lines (HeFu model catalog).

Executive Summary

OpenAI unveiled GPT-6 Sol and GPT-6 Luna on September 22, 2026, positioning them as mid-tier and lightweight members of the GPT-6 family below the flagship Astra (TechCrunch, 2026-09-22). The two models share identical core specifications — a 1,050,000-token context window and a 128,000-token maximum output — but diverge sharply on price and performance profile. As of October 2026, OpenAI's official API list prices are $2.00 per million input tokens and $10.00 per million output tokens for Sol, versus $0.10 and $0.50 for Luna, both 50% below the corresponding GPT-5.6 promotional rates (OpenAI pricing; PowerDrill, read 2026-09-24). Derived from those list prices at a 3:1 input-to-output token mix, the blended cost per 1M total tokens is $4.00 for Sol and $0.20 for Luna — a 20.0x gap (OpenAI pricing). On the DeepSWE 1.1 software-engineering benchmark at maximum effort, Sol reaches 68.8% while Luna scores 66.6% as of September 2026 (DataCamp; aiDiscoveryWire, Sep 2026). For teams building directly on OpenAI's API that need strong reasoning without frontier-level spend, Luna delivers roughly 20x lower cost under a 3:1 input-to-output token mix (llm-stats).

What Are GPT-6 Sol and Luna?

GPT-6 Sol and Luna are the two specialized variants OpenAI introduced to broaden the GPT-6 family's coverage of mid-tier workloads. Industry coverage of the launch describes OpenAI's goal as giving developers a clear choice between maximum reasoning depth and maximum cost efficiency within the same generation (TechCrunch, 2026-09-22).

Sol is designed for complex, multi-step reasoning: deep software-engineering tasks, agentic tool use, and fact-dense professional work. OpenAI states that GPT-6 Sol's factual error rate is approximately half that of GPT-5.6 Sol as of September 2026 (DataCamp). Luna, by contrast, is tuned for simple, high-volume tasks such as classification, information extraction, summarization, and real-time chat, where latency and cost matter more than chain-of-thought depth.

Both models accept text and image inputs and output text. As of September 2026, their knowledge cutoff dates differ slightly — Sol at April 20, 2026 and Luna at May 18, 2026 (llm-stats; YFarmX, Sep 2026). As of September 2026, they are available through ChatGPT Work and Codex plans, the OpenAI API across Plus/Pro/Business/Enterprise/Edu tiers, and selected GitHub Copilot plans; free-tier ChatGPT desktop users can access Luna (eigent.ai; PowerDrill, read 2026-09-24).

Sol vs. Luna — Key Technical Differences

The performance gap between the two models is driven primarily by inference-time reasoning budget rather than by context or output capacity.

• Reasoning depth vs. throughput. Sol allocates substantially more compute to long reasoning chains, which shows on agentic and coding benchmarks. On DeepSWE 1.1 at maximum effort, Sol scores 68.8% versus 66.6% for Luna as of September 2026 — a modest gap for a 20x price difference — while Sol also carries a lower factual error rate, roughly half that of GPT-5.6 Sol (DataCamp; aiDiscoveryWire, Sep 2026).
• Context handling. Both models share a 1,050,000-token context window and 128,000-token maximum output as of September 2026, so context length is not the differentiating factor. The real difference is how accurately each model sustains performance across long contexts under heavy reasoning load.
• Latency. Luna is optimized for high-throughput, low-latency inference, making it suitable for synchronous chat and real-time pipelines. Sol's deeper reasoning leads to higher per-request latency on demanding workloads. As of October 2026, OpenAI has not published latency SLAs for either model, so teams should benchmark representative workloads rather than rely on marketing estimates.
• Total cost of ownership. At the official list rates as of October 2026, Luna undercuts Sol by exactly 20.0x at a 3:1 input-to-output mix: blended cost per 1M total tokens is $4.00 for Sol versus $0.20 for Luna (OpenAI pricing; llm-stats). As one illustration, a workload of 10B total tokens per month at that mix would cost about $40,000 on Sol and $2,000 on Luna — a $38,000 monthly difference.

Head-to-Head Comparison Table

The table below summarizes the key trade-offs as of October 2026, based on official OpenAI pricing and public benchmark data.

Dimension	GPT-6 Sol	GPT-6 Luna
Positioning	Complex reasoning, agentic workflows, factual professional work	High-volume, cost-efficient generation
Typical tasks	Deep coding, multi-step research, agentic tool use	Classification, extraction, summarization, real-time chat
API price per 1M tokens (input / output), as of Oct 2026	$2.00 / $10.00	$0.10 / $0.50
Blended cost per 1M total tokens at 3:1 input:output mix	$4.00	$0.20
Relative cost at 3:1 input:output mix	Baseline	~20x lower
Context window	1,050,000 tokens	1,050,000 tokens
Maximum output	128,000 tokens	128,000 tokens
Knowledge cutoff	2026-04-20	2026-05-18
DeepSWE 1.1 (max effort), as of Sep 2026	68.8%	66.6%
Factual error vs. prior generation	~50% lower than GPT-5.6 Sol	Higher than Sol, lower total cost
Latency profile	Higher per-request latency	Low-latency, high-throughput
Availability (as of Sep 2026)	ChatGPT Work, Codex, OpenAI API, GitHub Copilot	Same, plus ChatGPT Free/Go desktop

How to Access OpenAI GPT Models via HeFu's Unified API

HeFu provides a single-API-key gateway to a broad multi-vendor model catalog through an OpenAI-compatible endpoint: https://api.hefu.hk/v1. A single integration works across the OpenAI GPT, Claude, DeepSeek, Kimi, and Gemini families; you switch models by changing the model name in your request rather than re-architecting your code (HeFu developer docs).

As of October 2026, HeFu's OpenAI GPT lineup is led by the GPT-6 and GPT-5.6 families — gpt-6-sol and gpt-6-luna are listed alongside GPT-5.6 Terra, Sol, and Luna, plus the GPT-5.5 / GPT-5.4 / GPT-5.2 series, the GPT-5 Pro / Mini / Nano range, and the GPT-5.x Codex programming models (HeFu model catalog). Always check the official model catalog before planning a rollout. HeFu's Hong Kong nodes provide direct routing without requiring an overseas credit card — a key consideration for developers outside the US (ChatGPT and Claude API without a US card).

Pricing and Billing Considerations

For cost planning, the official OpenAI API list prices as of October 2026 are the reference point: GPT-6 Sol at $2.00 per million input tokens and $10.00 per million output tokens; GPT-6 Luna at $0.10 and $0.50 respectively — both 50% lower than the corresponding GPT-5.6 promotional prices (OpenAI pricing; PowerDrill, read 2026-09-24). At a 3:1 input-to-output mix, Luna works out to exactly 20x cheaper per 1M total tokens than Sol as of October 2026 (llm-stats).

HeFu does not publish fixed per-token figures in this guide because prices and packages are adjusted over time; current rates and subscription options are always shown on the official pricing page (HeFu pricing). If you are budget-modeling against GPT-5.6 as a baseline, our detailed breakdown of GPT-5.6 API pricing — including the Terra/Sol/Luna tier structure — is a useful companion read (GPT-5.6 API pricing guide). For cross-vendor cost analysis, see also our DeepSeek V4 vs GPT API comparison (DeepSeek V4 vs GPT API cost).

Recommended Use Cases and Selection Criteria

The selection criteria below reflect the design intent of the GPT-6 generation on OpenAI's direct API; for teams routing through HeFu's unified gateway, the closest in-stock analogues are GPT-5.6 Sol and GPT-5.6 Luna, which share the Terra/Sol/Luna tier structure of the GPT-5.6 family (HeFu model catalog).

Choose Sol when the task demands deep reasoning: complex code generation and debugging, multi-step agentic workflows, long-context document analysis, or any workload where factual accuracy is critical. Sol's factual error rate — about half of GPT-5.6 Sol as of September 2026 (DataCamp) — makes it the safer default for regulated, research-heavy, or enterprise-grade environments.

Choose Luna when the task is high-volume and repetitive: customer-support summarization, email triage, entity extraction, classification, and real-time chat where budget and latency dominate. As of October 2026, Luna's blended cost at a 3:1 input-to-output mix is $0.20 per 1M total tokens versus $4.00 for Sol (OpenAI pricing; llm-stats) — a 20x advantage that can justify routing the bulk of routine traffic to Luna while reserving Sol for the hardest requests.

A practical pattern is a hybrid router: send simple queries to Luna, escalate complex reasoning to Sol, and periodically measure accuracy on a held-out evaluation set. On OpenAI's direct API, that means routing between GPT-6 Luna and GPT-6 Sol; on HeFu, the same split can be implemented with GPT-5.6 Luna and GPT-5.6 Sol. Because HeFu's API exposes all catalog models through the same OpenAI-compatible endpoint and authentication scheme, you can implement this split by changing the model field per request — no code changes and no separate SDKs (HeFu developer docs).

FAQ

What are the official API prices for GPT-6 Sol and Luna, and how do they compare with GPT-5.6?

As of October 2026, OpenAI lists GPT-6 Sol at $2.00 per million input tokens and $10.00 per million output tokens, and GPT-6 Luna at $0.10 and $0.50 respectively — both 50% lower than the corresponding GPT-5.6 promotional prices (OpenAI pricing; PowerDrill, read 2026-09-24). HeFu's rates for its in-stock GPT-5.6 family and other catalog models are published on the official pricing page (HeFu pricing).

How should I choose between Sol and Luna?

Use Sol for complex coding, agentic workflows, multi-step research, and fact-critical professional tasks; use Luna for classification, extraction, summarization, and real-time chat. Both models share the same 1,050,000-token context window and 128,000-token output limit as of September 2026 (llm-stats), so the deciding factors are reasoning depth, latency tolerance, and the 20x cost gap at a 3:1 input-to-output mix as of October 2026. On HeFu's unified gateway, the equivalent split can be implemented with GPT-5.6 Sol and GPT-5.6 Luna (HeFu models).

Where can I use GPT-6 Sol and Luna?

As of September 2026, GPT-6 Sol and Luna are available in ChatGPT Work, Codex, and the OpenAI API across Plus/Pro/Business/Enterprise/Edu plans; free-tier ChatGPT desktop users can access Luna, and both models have entered selected GitHub Copilot plans (eigent.ai; PowerDrill, read 2026-09-24). For multi-vendor access through one OpenAI-compatible API, HeFu's model catalog shows the currently purchasable lineup as of October 2026 (HeFu models).

Does HeFu charge extra for GPT-6 Sol and Luna?

GPT-6 Sol and Luna are listed in HeFu's catalog as gpt-6-sol and gpt-6-luna as of October 2026, alongside the GPT-5.6 family and GPT-5.x variants (HeFu models). HeFu's pricing is transparent and shown on the official pricing page, with no hidden gateway fees (HeFu pricing).

What latency can enterprise users expect as of October 2026?

OpenAI has not published official latency SLAs for GPT-6 Sol and Luna as of October 2026. Sol's deep reasoning path generally means higher per-request latency on complex tasks, while Luna is explicitly optimized for high-throughput, low-latency inference. Enterprise teams should benchmark against their own workloads; HeFu's Hong Kong nodes provide direct routing for OpenAI and other models, which can reduce cross-border latency for Asia-based deployments, but the authoritative availability and performance figures remain on each provider's official status pages.