GPT-6.1 Sol vs the GPT-6 Generation: A Generation of One

The vendor added a fourth name to its model line on 29 September 2026 and called it a generation, and the arithmetic of the lineup is the most interesting thing about it: GPT-6.1 Sol is the entire generation. GPT-6 Luna and GPT-6 Sol, both released on 22 September 2026, are unchanged; GPT-6 Astra, out since 4 September 2026, is unchanged; the 6.1 refresh touched exactly one tier. Meanwhile GPT-6.1 Sol, at $2.00 in and $10.00 out per million tokens, scores 51.83 on the Artificial Analysis Intelligence Index for $0.724 per task — within 0.84 points of GPT-6 Astra, which costs $3.2575 per task at $10.00/$50.00. Same generation on the label, four different economics underneath.

That makes "should I use the GPT-6 generation" the wrong question. The lineup spans a 48× range in cost per task and a 14.6-point range in measured intelligence, and the answer depends entirely on which tier you mean. Here is the ladder, measured.

The four rungs, as measured

• GPT-6 Luna — Index 38.12, $0.10 / $0.50 per 1M, $0.01 cached, $0.0678 per task, 50,012 output tokens, 100.1 s to first token
• GPT-6 Sol — Index 47.63, $2.00 / $10.00, $0.20 cached, $1.045 per task, 31,000 output tokens, 124.3 s
• GPT-6.1 Sol — Index 51.83, $2.00 / $10.00, $0.10 cached, $0.724 per task, 38,128 output tokens, 332.0 s
• GPT-6 Astra — Index 52.67, $10.00 / $50.00, $1.00 cached, $3.2575 per task, 27,206 output tokens, 400.4 s

Every one of them holds the same 1,050,000-token context window and the same 128,000-token maximum output, and every one takes text, image and file input. The tiers are not differentiated by capability flags. They are differentiated by how much thinking you are willing to pay for, and the two ends of that ladder are 48 times apart per task.

What the 6.1 refresh actually changed

Three things, and only one of them is a score.

The score moved: 47.63 to 51.83, a 4.2-point gain, in a week. The cached-input rate halved, from $0.20 to $0.10 per million, which is why the measured cost per task fell from $1.045 to $0.724 despite the list prices being identical — the same rate card, a different bill. And the output-token count rose from 31,000 to 38,128 while the wall-clock time per task went from 321 seconds to 640, which is the one place the refresh went backwards and the least visible on any spec sheet.

Against Astra the comparison is the one that surprises people. GPT-6.1 Sol is 0.84 index points behind the flagship and 4.5 times cheaper per task. Astra's remaining advantage is not its index score; it is the set of evaluations where it alone is measured, and the one capability OpenAI describes as unique to it — finding novel security vulnerabilities, which is why the model is rated "critical" under the vendor's own Preparedness Framework and its most capable settings are restricted to vetted partners.

The OrcaRouter card for GPT-6 Astra is the flagship end of the same ladder: $10.00 / $50.00, a 1.64-second p50 to first token, 40.7 million tokens over seven days, and the same 1,050,000-token context window every tier shares.

Against the 6.0 Sol the refresh is close to a straight improvement — more index, less money — with the caveat that the 6.1 model is a different model and not a checkpoint, and it re-emits its reasoning more verbosely. Against Luna it is not a comparison at all: 10.7 times the cost for 13.7 index points, which is a good trade for hard work and a terrible one for high-volume work.

What our own routing data says the market already decided

One thing this comparison has that a spec sheet does not is a view of actual usage. Across the seven days ending 7 October 2026 on our own routing layer, GPT-6.1 Sol carried 284.2 million tokens and the GPT-6 Sol it superseded carried 950 thousand — a 300-fold difference for two models a week apart in age, which is what a tier replacement looks like from the inside. GPT-6 Luna, the cheap tier, carried 641.6 million, more than both Sol models combined. GPT-6 Astra carried 40.7 million.

Read that as three separate behaviours. High-volume work went to the cheap tier in the largest volume. Serious work consolidated onto the newest Sol within a week of its release, abandoning the model it replaced. And the flagship stayed a niche: the most capable rung, used least, by teams whose problem is the one only it solves. The lineup is not a ladder people climb; it is a set of tools people pick from, and they pick by tier rather than by generation.

Which is why the tier belongs in the request, not in the architecture

The practical problem with a generation of four tiers is that the right answer changes per task and per week — as the traffic numbers above show, the market re-decided within seven days. Hard-coding one model name into an application is what turns a lineup into a commitment.

All four are reachable through one OrcaRouter endpoint: a single API for 200+ models with the provider's list price passed through at 0% markup. That last part is load-bearing here specifically because three of the four tiers share a list price or a cached rate that has already moved once — GPT-6.1 Sol's cache rate halved inside a week of launch, and a pass-through meter is what makes that arrive on your invoice automatically.

Two features of the routing layer are worth naming for this particular lineup. The routing DSL lets the tier be a parameter of the request rather than a deployment — cheap tier for the extraction pass, 6.1 Sol for the reasoning pass, Astra only for the calls that need what Astra uniquely does. And model fusion lets a panel of them answer one question together, which is the honest way to use a flagship you cannot afford to route everything to: it becomes one voice in a panel instead of the whole bill.

What to do with a generation of one

Do not buy the generation. Buy a rung, and re-buy it when the rungs move.

For high-volume and latency-sensitive work, GPT-6 Luna at $0.0678 a task and 100 seconds to first token is the tier that already carries the most traffic, and the price is an order of magnitude below anything else on the ladder. For general reasoning and coding, GPT-6.1 Sol is now the default the 6.0 model used to be — same list price, better index, half the cache cost, and the 640-second task time is the one thing to test against your own workload before you assume the upgrade is free. For the tasks that need frontier measurement or the security work only the flagship does, GPT-6 Astra remains the only rung that does them, at 4.5 times the cost of the rung below it.

What to watch is whether the 6.1 refresh reaches the other tiers. If Luna or Astra get the same treatment, the ladder's shape changes and the per-task arithmetic in this article with it. Until then, "GPT-6.1" names one model, and treating it as a family is how teams end up paying flagship rates for extraction.