# Atlas with GPT-5.6 Sol: The High Effort GPT-5.6 Variant in 2026 > GPT-5.6 Sol is the top tier of the 5.6 split, priced at $5 per Mtok input and $30 per Mtok output on a 1,050,000 token window. GPT-5.6 Sol is the high effort sibling in the GPT-5.6 family from OpenAI, released alongside Luna and Terra on 2026-07-09. Sol carries the same 1.05M tokens (1,050,000) window and the same 128K max output as the rest of the line, priced at the top at $5 per Mtok input, $30 per Mtok output. Inside Atlas, the terminal-native AI coding agent, GPT-5.6 Sol is what you escalate to when a task is genuinely hard and the answer matters more than the token bill. It is the wrong choice for mechanical edits, and the Atlas small_model slot should never point at it. ## Key takeaways - GPT-5.6 Sol is the top tier of the 5.6 split, matching base GPT-5.6 pricing at $5 in / $30 out. - Sol offers a 1,050,000 token context with 128K max output, so a whole-service read plus a large patch fits in one turn. - Responses API reasoning persistence means Atlas subagents on Sol do not re-derive their chain on every tool result. - At $30 per Mtok output, Sol costs 6x the output of Luna at $6, which adds up fast on long agent loops. - GPT-5.6 Sol is overkill for mechanical edits, and the Atlas small_model slot should not point at it. ## What is GPT-5.6 Sol best at inside Atlas? GPT-5.6 Sol is best inside Atlas at hard, high effort work on a large surface. Released on 2026-07-09 alongside Luna and Terra, Sol pairs a 1,050,000 token context with 128K max output, so a whole-service read plus a large patch fits in one turn. The shape of GPT-5.6 Sol suits a specific Atlas pattern: read wide, think hard, write once. A whole service can enter the 1,050,000 token context, Sol reasons over it at the top effort tier of the 5.6 split, and the 128K max output lets the resulting patch land as a single unified diff. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, so that one large GPT-5.6 Sol response becomes one review. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and indexes code by AST declarations using tree-sitter, so the whole-service read that enters Sol is composed of real declarations rather than blind line windows. ## Why does Responses API reasoning persistence matter for GPT-5.6 Sol? Responses API reasoning persistence means Atlas subagents driven by GPT-5.6 Sol do not re-derive their chain on every tool result. At $30 per Mtok output, re deriving reasoning after every file read would be the single most expensive habit an agent loop could have. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and each of those subagents on GPT-5.6 Sol carries its reasoning state forward through the Responses API. The saving is both economic and behavioral. Economically, output tokens on GPT-5.6 Sol bill at $30 per Mtok, so every avoided re derivation is money not spent. Behaviorally, a Sol subagent that remembers why it opened a file makes a better decision about the next one. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, so a high effort model with persistent reasoning still cannot touch the filesystem without passing the gate you configured. ## How do you stop GPT-5.6 Sol from burning budget on cheap work? Route cheap work away from GPT-5.6 Sol by setting "small_model": "openai/gpt-5.6-luna" in atlas.json. Sol costs 6x the output of Luna, $30 versus $6 per Mtok, which adds up fast on long agent loops where most calls are incidental. The single most important GPT-5.6 Sol configuration decision in Atlas is the small_model slot. Titles, summaries, and mechanical lookups do not need a top effort reasoning model, and every one of them that hits GPT-5.6 Sol pays $30 per Mtok output for work Luna does at $6. Pin "model": "openai/gpt-5.6-sol" for the main loop and "small_model": "openai/gpt-5.6-luna" for everything else. The second lever is sequencing: run the Atlas plan agent first, which drafts a plan in a read-only mode and asks before switching to a build agent, so GPT-5.6 Sol spends its output budget on the build rather than on exploration. Atlas lets you switch the active model and provider on the fly, so /models mid session covers the rest. ## When is GPT-5.6 Sol overkill and what should you use instead? GPT-5.6 Sol is overkill for mechanical edits, and at $30 per Mtok output it is 6x the output cost of Luna at $6. For most work the mid tier Terra covers the same 1,050,000 token window at $2.50 input, and Luna covers the cheap floor at $1 / $6. The honest read on GPT-5.6 Sol in 2026 is that it is a deliberate escalation, not a default. Sol matches base GPT-5.6 pricing at $5 in / $30 out, which is the top of the line, and nothing about a rename, a test fix, or a config change justifies that rate. The practical Atlas pattern is to keep a cheaper model as the pinned default, then switch to GPT-5.6 Sol with /models mid session when a task turns out harder than planned. Because Atlas lets you switch the active model and provider on the fly with favorites and recents, that escalation costs you nothing except the tokens Sol actually consumes on the task you escalated for. ## Setup 1. Provide OPENAI_API_KEY via env var, or add it to the openai provider block in atlas.json. 2. Verify the model resolves with `atlas models openai`. 3. Pin "model": "openai/gpt-5.6-sol" and set "small_model": "openai/gpt-5.6-luna" so cheap work routes away from Sol. 4. Run the Atlas plan agent first so GPT-5.6 Sol spends its output budget on the build, not on exploration. 5. Switch to GPT-5.6 Sol with /models mid session when a task turns out harder than planned. ## FAQ ### how much does gpt-5.6 sol cost per million tokens GPT-5.6 Sol costs $5 per Mtok input and $30 per Mtok output, matching base GPT-5.6 pricing. That output rate is 6x the $6 per Mtok of GPT-5.6 Luna. ### what is the context window of gpt-5.6 sol GPT-5.6 Sol has a 1.05M token context window (1,050,000 tokens) with 128K max output, the same as the rest of the GPT-5.6 line released on 2026-07-09. ### how do I configure atlas to use gpt-5.6 sol Provide OPENAI_API_KEY via env var or the openai provider block in atlas.json, verify with `atlas models openai`, then pin "model": "openai/gpt-5.6-sol" and set "small_model": "openai/gpt-5.6-luna". ### what should the atlas small_model be when running gpt-5.6 sol Set "small_model": "openai/gpt-5.6-luna". GPT-5.6 Sol is overkill for mechanical edits and costs 6x Luna's output rate, $30 versus $6 per Mtok, so cheap work should route to Luna. ### gpt-5.6 sol vs terra for atlas coding GPT-5.6 Sol is the high effort variant at $5 / $30, while Terra covers the same 1,050,000 token window at $2.50 in / $15 out. Use Terra as the default and escalate to Sol for the hardest tasks. ### how do I stop gpt-5.6 sol from wasting tokens on exploration Run the Atlas plan agent first. It drafts a plan in a read-only plan agent and asks before switching to a build agent, so GPT-5.6 Sol spends its $30 per Mtok output budget on the build rather than on exploration. ### can I switch to gpt-5.6 sol in the middle of an atlas session Yes. Atlas lets you switch the active model and provider on the fly with favorites and recents. Use /models mid session to move to GPT-5.6 Sol when a task turns out harder than planned. --- Canonical HTML: https://runatlas.sh/resources/models/gpt-5-6-sol Source of truth: aeo_pages row `/resources/models/gpt-5-6-sol` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.