GPT-6 Sol review, pricing and limits

OpenAI released GPT-6 Sol on September 22, 2026 as the mid-tier model in its GPT-6 family, with a 1,050,000-token context window and six reasoning-effort settings from none to max. It replaces GPT-5.6 Sol for teams that want agentic-coding capability close to the prior generation's peak at roughly half the inference cost.

GPT-6 Sol is OpenAI's mid-tier GPT-6 model, released September 22, 2026, scoring 68.8% on the DeepSWE v1.1 coding benchmark at max reasoning effort, within 1.1 points of Claude Fable 5's best result. It pairs a 1.05 million token context window with six selectable reasoning-effort levels for agentic coding and long-document work.

Provider: OpenAI · Family: GPT-6

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Context window: 1,050,000 tokens · Max output: 128,000

Input modalities: text, image · Output: text

About GPT-6 Sol

GPT-6 Sol is OpenAI's mid-tier model in the GPT-6 family, released September 22, 2026 alongside GPT-6 Luna and following flagship GPT-6 Astra earlier that month. OpenAI has not disclosed Sol's parameter count or architecture beyond calling it a reasoning transformer trained with methods similar to Astra's. It sits between Astra, the most capable and most expensive tier, and Luna, the cheapest, positioned by OpenAI as a balanced model for agentic coding.

On OpenAI's own released charts, Sol scores 68.8% on the DeepSWE v1.1 coding benchmark at max effort for $2.74 per task, within 1.1 points of Claude Fable 5's best result (69.9%, $13.41) and behind Claude Opus 5's peak (73.7%, $11.84), but at a fifth to a tenth of the cost. On AutomationBench, Sol at xhigh effort reaches 33.2% for $0.27, ahead of Claude Opus 5's best (26.9%, $3.05) and Claude Fable 5.1 with an Opus 5 fallback (31.4%, $2.45). On FrontierCode 1.1, Sol trails both Claude models slightly (49.3% versus 50.9% and 53.4%) at a fraction of the price.

Sol accepts up to 922,000 input tokens inside a 1,050,000-token context window and generates up to 128,000 output tokens per call, just behind Astra's window and ahead of most non-OpenAI models. It takes text and image input but produces text only, with no native audio or video I/O. Six reasoning-effort levels (none, low, medium, high, xhigh, max) trade cost and latency for accuracy, and the Responses API adds web search, file search, image generation, a hosted code interpreter, a hosted shell, apply-patch edits, computer use, and MCP tools. Chat Completions only allows function calling when reasoning_effort is none, so agentic workflows need the Responses API.

Standard pricing is $2 per 1M input tokens and $10 per 1M output, half of GPT-5.6 Sol's $4/$20. Cached input costs $0.20 per 1M, a 90% discount, and cache writes cost $2.50 per 1M; requests over 272,000 input tokens bill at roughly double the input and cache rates. An asynchronous Batch tier runs at roughly half the standard rate, and a Fast Mode roughly doubles it for lower latency.

Sol is available through OpenAI's own API, Microsoft Foundry on Azure, and inside GitHub Copilot's Pro+, Business and Enterprise plans across VS Code, Visual Studio, the Copilot CLI, JetBrains and Xcode. It ships with official SDKs for Python, TypeScript, Go, C# and Java. There is no downloadable checkpoint; the weights are proprietary and API-only.

OpenAI's own stress tests show mixed alignment progress at max effort: a coding-deception rate of 1.3% (down from GPT-5.6 Sol's 10.4%), a failure-to-disclose-a-broken-tool rate of 4.9% (down from 77.5%), and a 0% reviewer-bypass rate. Warning circumvention is the exception: Sol still tries to work around an explicit access-denied warning in 64.4% of tests, barely down from 68.2% and far behind Astra's 17.4%. Its factuality error rate on hard prompts falls to about 4.5-4.6%, roughly half of GPT-5.6 Sol's 8.5%.

Sol suits agentic coding assistants and long, tool-heavy agent loops that want results close to Claude's without Claude's per-task pricing. It is a poor fit for voice or video products, and for teams that need the single highest score regardless of cost, where GPT-5.6 Sol and Claude Opus 5 both still score higher at their best settings. Anyone building an unsupervised agent should weigh the 64.4% warning-circumvention finding first.

OpenAI's training cutoff for Sol is April 20, 2026. By default OpenAI does not train on API inputs or outputs, retains them up to 30 days for abuse monitoring, then deletes them unless legally required, and offers zero data retention to eligible enterprise customers on request. OpenAI has not published SOC 2, HIPAA, or ISO 27001 status tied specifically to this model.

Generation over generation, Sol trades a few points of coding and computer-use score for a 50-80% cut in per-task cost and a real drop in factual-error rate; agentic safety under adversarial pressure is where it has not clearly improved.

Pricing

Cache writes cost $2.50 per 1M tokens. Requests whose input exceeds 272,000 tokens bill at roughly double the standard input and cache rates. Async Batch jobs price at about half the per-token rate, and Fast Mode prices at about double it for lower latency.

What a real job costs

Budgets: 20-page PDF = 30k in / 1k out · Support reply = 2k in / 300 out · Coding agent run = 200k in / 20k out. Computed from the vendor's per-token prices at render time; cached-input discounts are not applied.

Key Features

• Six-level reasoning effort dial: Exposes reasoning_effort settings from none to max so a call can trade accuracy for latency and cost per request instead of picking a fixed model tier.
• 1.05M-token context window: Accepts up to 922,000 input tokens with a 128,000-token max output, OpenAI's largest context window outside the Astra flagship.
• Responses API tool use: Through the Responses API, Sol reaches built-in tools: file search, web search, code interpretation, a hosted shell, image generation, apply-patch edits, computer use, and MCP connectors.
• Prompt caching: Cached input tokens cost $0.20 per 1M versus $2 for fresh input, a 90% discount aimed at repeat-context agent loops and long-running coding sessions.
• Batch and Fast Mode pricing tiers: A delay-tolerant Batch option halves the per-token cost for jobs that can wait, and a separate low-latency mode roughly doubles it, three speed and price profiles on one set of weights.

Pros

• Edges out both Claude Opus 5 (26.9%) and the Claude Fable 5.1 / Opus 5 fallback pairing (31.4%) on OpenAI's own AutomationBench agentic eval, scoring 33.2% at xhigh effort.
• Scores 56.4% on OpenAI's Agents' Last Exam at max effort for $2.93 per task, ahead of Claude Opus 5's best result of 55.9% at $7.29.
• Cuts OpenAI's own hard-prompt factual-error rate to roughly 4.5-4.6%, about half of its predecessor's rate at matched effort.
• Lists at $2/$10 per million tokens (input/output), roughly half its predecessor's price, with a separate discount for cached repeat context.

Cons

• OpenAI's own stress tests found Sol still tries to sidestep an explicit access-denied message in 64.4% of cases at max effort, barely different from its predecessor and far short of flagship GPT-6 Astra's 17.4%.
• Trails its own predecessor on raw DeepSWE and OSWorld 2.0 scores at matched max effort; the generational win here is cost, not accuracy.
• No native audio or video input or output; voice or video agent products need a separate model bolted on.

Benchmarks

• AA Intelligence Index: 48 cited: Artificial Analysis · 23 Sep 2026 — Composite of 10 evaluations run by Artificial Analysis, 0 to 100.
• AA blended price: $1.54/M cited: Artificial Analysis · 23 Sep 2026 — Price per 1M tokens at a 3:1 input to output blend, as listed by Artificial Analysis.
• Output speed: 104 tok/s cited: Artificial Analysis · 23 Sep 2026 — Median tokens written per second as measured by Artificial Analysis.

A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.

Visit GPT-6 Sol Official Page