GPT-6 Sol vs GPT-5.6 Sol: What Changed?

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The GPT-6 Sol vs GPT-5.6 Sol decision is easier on price than on capability. GPT-6 Sol costs half as much and edges ahead on the composite intelligence score, but it is not strictly better than the model it replaces. It trades a little ground on some benchmark evaluations for a large cut in cost, so the right choice depends on your workload. For the full performance picture on Sol alone, see our GPT-6 Sol benchmarks breakdown.

Both models sit on the same Artificial Analysis board, version 4.3.2, which makes this the cleanest comparison in the GPT-6 lineup. There is no vendor-versus-independent mismatch here, just one lab measuring two models from one vendor.

GPT-6 Sol vs GPT-5.6 Sol at a glance

GPT-6 Sol wins clearly on cost and marginally on the composite, while GPT-5.6 Sol keeps the larger listed context window and leads on a few component evaluations. The table below sets the headline metrics side by side.

Table 1 - GPT-6 Sol versus GPT-5.6 Sol, key metrics.

The pattern is consistent across the row set. Sol is cheaper and a little smarter on the composite, and faster at producing tokens once it starts. Artificial Analysis lists GPT-5.6 Sol with the larger context window, 1,000K against Sol's 872K, so the older model holds a small edge there.

What GPT-6 Sol improves

GPT-6 Sol's biggest gain is economic, and the savings are real on both sides of the bill. Standard input drops from $4 to $2 per 1M tokens and output from $20 to $10, a 50% cut that OpenAI applied across cached input and cache writes too. On Artificial Analysis, cost per task falls from $1.99 to $1.06 and the blended price from $3.08 to $1.54.

Capability moved as well, in specific places. Sol makes about half as many mistakes as GPT-5.6 Sol on OpenAI's internal factuality evaluation, which is built from error-inducing, user-flagged conversations rather than typical traffic, so read it as directional. Its Artificial Analysis omniscience score rises from 22 to 27. On the same board, Sol gains on AutomationBench-AA (62% versus 60%), Terminal-Bench 4.0 (44% versus 40%), and SciCode (58% versus 57%).

Safety improved too. OpenAI reports that GPT-6 Sol continues to improve on alignment over GPT-5.6 Sol, and directs readers to the model's system card for the detailed safety evaluations.

Output speed is the last gain. Once Sol starts generating, it produces tokens faster than GPT-5.6 Sol at max effort in Artificial Analysis's measurements, which helps it finish long tasks sooner.

Where GPT-6 Sol regressed or stood still

The one-point Index gain hides a mix of gains and regressions, so Sol is a different point on the frontier, not a clean step up. On the same Artificial Analysis board, Sol scores lower than GPT-5.6 Sol on several evaluations: AA-Briefcase v1.1, Humanity's Last Exam, GDP.pdf, and CritPt, and it ties on long-context reasoning.

The regression that stands out is on agentic knowledge work. Artificial Analysis measured GPT-6 Sol at 1487 on GDPval-AA v2.1, down from GPT-5.6 Sol's 1588, a drop of about 100 points on an evaluation built to price economically valuable work. This is one benchmark, not all knowledge work, and the composite still rises by a point. But it is a material regression on this specific evaluation, so test knowledge-heavy tasks on your own data rather than trust the composite alone.

Table 2 - GPT-6 Sol versus GPT-5.6 Sol on Artificial Analysis component evaluations.

Latency: high at max effort, so measure it

At max effort, both models are slow to produce a first token, well over 100 seconds against a board median of a few seconds, because each does extensive reasoning before it responds. That is the number to watch for anything interactive, and it applies to both.

Which model starts faster is not stable. Artificial Analysis measures this daily, and the ordering has flipped between readings: a launch-day measurement puts GPT-6 Sol ahead on time to first token, a later one put GPT-5.6 Sol ahead. Treat first-token latency as too noisy to decide on from the benchmark alone. What does hold across readings is throughput: once generation begins, GPT-6 Sol produces tokens faster, so it tends to finish long tasks sooner even when it starts later.

For anything with a person waiting, lower the effort setting and measure p50 and p95 time to first token on your own prompts before you switch.

Where GPT-5.6 Sol still wins

GPT-5.6 Sol keeps a few advantages worth weighing. On the same Artificial Analysis harness, it lists the larger context window, 1,000K against Sol's 872K, and it scores higher on several component evaluations, including the GDPval-AA v2.1 knowledge-work benchmark, Humanity's Last Exam, GDP.pdf, and CritPt. For workloads shaped like those, the older model still leads despite Sol's one-point composite edge.

Pricing compared

On standard rates, GPT-6 Sol is half the price of GPT-5.6 Sol across every line. The one caveat is that GPT-5.6 Sol's $4/$20 is itself promotional pricing, which OpenAI lists at least through November 21, 2026, so the comparison is against a discounted rate rather than a permanent list price.

Table 3 - GPT-6 Sol versus GPT-5.6 Sol standard pricing (per 1M tokens, as of September 2026).

The cut holds at long context too, which is easy to get wrong. Once a request passes 272,000 input tokens, GPT-6 Sol reprices to $4 input and $15 output per 1M, but GPT-5.6 Sol reprices further, to $8 and $30. So Sol stays about 50% cheaper on long inputs, not just short ones. Reuters noted the reduction is measured against GPT-5.6 Sol's promotional prices, which is the fair way to frame it.

Table 4 - Long-context pricing above 272K input tokens (per 1M tokens).

Availability and how to migrate

At launch, GPT-6 Sol is available through the API and in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users, and OpenAI says it is not yet in standard ChatGPT Chat. Confirm your team can reach it before planning a migration. Above both sits OpenAI's highest-capability model, GPT-6 Astra, for workloads that need more than either Sol can offer.

A short migration checklist keeps the decision grounded:

• Measure your context distribution. Sol is about 50% cheaper than GPT-5.6 Sol at both short-context and long-context standard rates, though crossing the 272,000-token threshold raises the absolute rate on both models.
• Check who waits on the first token. First-token latency is high on both models at max effort and varies between readings, so route interactive work to lower effort and measure latency on both before deciding.
• Run your own evaluations on knowledge-heavy tasks before cutting over, given the GDPval-AA v2.1 regression.
• Keep GPT-5.6 Sol wired in as a fallback while GPT-6 Sol availability settles.

Which one should you use?

Match the model to the workload rather than defaulting to the newer one. The table below maps common cases.

Table 5 - Choosing between GPT-6 Sol and GPT-5.6 Sol.

Build with GPT-6 Sol on Emergent

You can put either model to work without managing an OpenAI account. Emergent is a vibe coding platform where you describe the app you want and its multi-agent architecture builds, tests, and deploys the full stack for you. GPT-6 Sol is available on Emergent today, so its lower cost per task flows straight into cheaper agentic features.

The Universal LLM Key makes the choice a one-line decision. It gives you GPT, Claude, and Gemini through a single credential and unified billing on Emergent Credits, so you can run Sol for high-volume steps and route a demanding step to a heavier model without juggling accounts.

Start Building on Emergent and ship your next app on GPT-6 Sol.