GPT-6 Sol Pro vs GPT-6 Astra: The Price Multiple Everyone Quotes Is Wrong

GPT-6 Sol Pro and GPT-6 Astra are the same vendor's cheap tier and flagship, and the multiple most coverage attaches to that gap is 2.5×. It is not 2.5×. GPT-6 Sol — the vendor's model that the gpt-6-sol-pro configuration runs, generally available since September 22, 2026 at $2.00 per million input tokens and $10.00 per million output tokens — is priced against GPT-6 Astra, the flagship that reached general availability on September 3, 2026, at $10.00 and $50.00. That is five times on input and five times on output. The 2.5× figure belongs to a different comparison: Astra measured against GPT-5.6 Sol, the previous generation, which cost $4.00 and $20.00.

The mix-up is worth correcting before anything else, because it changes the decision. A 2.5× premium for the flagship is a rounding error on a project budget. A 5× premium is the largest single line item in most inference plans, and it is the number that determines whether the flagship is a default or an exception.

Where the wrong multiple came from

When Astra launched on September 3, the comparison OpenAI and most outlets drew was against GPT-5.6 Sol, which was the shipping mid-tier at the time. Astra cost 2.5× that model. Nineteen days later OpenAI shipped GPT-6 Sol and cut the mid-tier price in half, from $4.00 and $20.00 to $2.00 and $10.00, and described the new numbers as permanent rather than promotional. Every "vs" page in the top results for this matchup was written in that nineteen-day window and compares Astra to the model that no longer occupies the slot.

Nothing about Astra changed. The tier below it moved, and the multiple doubled.

The two rate cards, line by line

Both are OpenAI's own published numbers, passed through unchanged by the platforms that host them.

• Input — GPT-6 Sol $2.00 per million tokens vs GPT-6 Astra $10.00 per million tokens

• Output — GPT-6 Sol $10.00 per million tokens vs GPT-6 Astra $50.00 per million tokens

• Cached input — GPT-6 Sol $0.20 per million tokens vs GPT-6 Astra $1.00 per million tokens; both a flat 10% of the uncached rate

• Cache write — GPT-6 Sol $2.50 per million tokens vs GPT-6 Astra $12.50 per million tokens, both at 1.25× the uncached input rate

• Long-context handling — GPT-6 Sol reprices a request above its input threshold at twice the input and cache rates and 1.5× the output rate, for the whole request; GPT-6 Astra does the same above 272,000 input tokens, at $20.00, $2.00, $25.00 and $75.00

• Batch — GPT-6 Sol at half the standard rates vs GPT-6 Astra at half its standard rates, $5.00 and $25.00, which preserves the 5× ratio exactly

• Context window — 1,050,000 tokens for both

• Maximum output — 128,000 tokens for both

• Input modalities — text and image for both, with GPT-6 Astra also accepting file input

• Knowledge cutoff — GPT-6 Sol April 20, 2026 vs GPT-6 Astra April 30, 2026, ten days apart

The two cards are not just similar in structure; they are the same structure with a five-times multiplier applied to every row, including the batch tier and the long-context tier. That is a deliberate product design, and it means the choice between them is genuinely a capability decision rather than a pricing-strategy decision. There is no discount to be found by changing your request shape.

The effort dial is the asymmetry nobody writes about

Both models expose a reasoning-effort parameter. The two ladders are not identical, and the difference has migration consequences.

GPT-6 Sol's documented set is none, low, medium, high, xhigh and max, with medium as the default. GPT-6 Astra's is low, medium, high, xhigh and max — there is no none. You cannot turn reasoning off on the flagship. If your pipeline has a latency-sensitive classification step that currently runs at effort none, that step does not port to Astra unchanged; it has to be re-tuned, moved to a different model, or accepted at a higher latency floor.

This is a small thing that costs a real amount of engineering time, and it is absent from every comparison page on this matchup because those pages were written from price tables rather than from the API documentation.

The independent numbers, with the index version attached

Artificial Analysis has re-cut its Intelligence Index several times in 2026, and this matchup is where that matters most, because the most-repeated Astra score is retired.

Astra scored 61 on index version v4.1.1 at launch — tied with GPT-5.6 Sol, which is why so much launch coverage described the flagship as a modest step. That 61 is real and it is from an index that no longer exists. On the current board, Astra at max effort scores 53 at $3.26 per index task, and at high effort scores 51 at $1.73. GPT-6 Sol at max effort scores 48 at $1.06 per index task, and 43 at high effort for $0.37.

Read those four lines together and the shape of the decision appears.

• Astra's advantage over Sol at the top of both dials is five index points, at roughly three times the cost per completed task.

• Astra at high effort costs $1.73 per task. Sol at max effort costs $1.06. The flagship running at its default effort setting is more expensive than the cheap tier running flat out, and the index scores are 51 and 48.

• The cost gap per task — roughly 3× — is smaller than the 5× rate gap, because Sol is the more token-hungry of the two per task. That is a genuine point in Astra's favour and it is the one piece of the "2.5×" framing that survives contact with the data.

Two honesty caveats belong attached. These are different pages on the same index family rather than a single published head-to-head run, and Astra's own latency figures are effort-dependent to a degree that makes a single number misleading — the same model reports a time to first token measured in tens of seconds at one effort level and several minutes at another. Do not quote an Astra latency figure without naming the setting.

What the flagship buys at 5×

The capability differences are real and they are concentrated in agentic and multimodal work rather than in general reasoning.

• Coding-agent index — GPT-6 Astra 62 in the Codex harness against GPT-6 Sol 57, at $7.09 and $2.99 per task respectively. The flagship uses roughly a third of the tokens per task, which is why the per-task gap is smaller than the rate gap.

• Terminal-Bench 4.0 — 59% for Astra against 43% for Sol on the same independent board, a sixteen-point separation that is the largest single capability gap between the two.

• Vendor-reported agentic rows — OpenAI's own launch table shows Astra ahead on OSWorld 2.0, ScreenSpot-Pro, AutomationBench and Agents' Last Exam, all vendor-run and none independently replicated. It also shows Sol ahead on FrontierMath T1–3 and BrowseComp, which is worth noting for anyone inclined to read the vendor table as a clean sweep.

• Safety posture — Astra was the first OpenAI model designated at the Critical cybersecurity level under the company's Preparedness Framework, and its advanced offensive-cyber capabilities are gated behind a separate access program. That gating has a production consequence beyond permissions: a misalignment monitor runs on tool-using requests, and in the API a flagged task stops rather than pausing for review as it does in the consumer clients.

The short version is that Astra is better at the things that are hard to substitute — long agentic loops, terminal work, computer use — and roughly level with Sol on the things that are easy to substitute. Five index points of general reasoning is not worth 5×. Sixteen points of Terminal-Bench and a coding-agent gap of five, on work where a failed run costs a human hour to clean up, sometimes is.

Where a routing layer changes the arithmetic

GPT-6 Astra is on OrcaRouter's catalogue at $10.00 input and $50.00 output per million tokens below a 272,000-token input threshold, with the long-context tier at $20.00 and $75.00 above it, a 1,050,000-token context window, a 128,000-token output cap and a p50 time to first token of 7.82 seconds as measured on our own model page. Provider list pricing passes through with no markup on top.

GPT-6 Sol is not one of our routes, so nothing here is a claim about its price through us.

A 5× price ratio is the clearest case there is for routing rather than choosing. The correct answer for most teams is not "the flagship" or "the cheap tier" — it is both, with the decision made per request rather than per quarter. The work that needs Astra is identifiable at the call site: it is the agentic run, the terminal task, the computer-use step. The work that does not is everything else. Putting both behind one key with automatic failover makes that a routing rule instead of a migration, and it means the expensive model is only ever invoked where it is the thing being paid for.

The decision rule

• High-volume text generation, extraction, classification and summarisation — GPT-6 Sol. Five times cheaper on every row, three index points behind on general reasoning, and the correct default.

• Agentic coding, terminal work and computer use — GPT-6 Astra. Sixteen points on Terminal-Bench and five on the coding-agent index, on work where a failed run is measured in human hours.

• Pipelines that currently run a step at reasoning effort none — neither, without a re-tune. Astra has no none setting, and that step will not port unchanged.

• Work that needs file input alongside images — GPT-6 Astra, which is the only one of the two that accepts a file.

• Anyone who read that the flagship is a 2.5× premium — re-run the arithmetic at 5×. It is the same model at the same price; the tier beneath it moved, and the decision is different at the corrected number.