GPT-6 Astra vs Solar Pro 4: Twelve Index Points and a Price Ratio That Changes on Sunday

On the two independently-run evaluations this pair has in common, GPT-6 Astra leads Solar Pro 4 by a wide margin on general knowledge and a narrower one where the models are actually used. Artificial Analysis puts GPT-6 Astra at 54.7 on its Intelligence Index and 96.1 on GPQA Diamond; the same evaluator puts Solar Pro 4 at 42 and 86.8. On agentic work the gap compresses to eleven and six points on Terminal-Bench 2.1 and τ²-Banking. Meanwhile the price story carries a deadline almost nobody attached to it: Solar Pro 4's current promotion ends at 00:00 UTC on Sunday, 11 October 2026, when the model moves from $0.09 to $0.15 per million input tokens and from $0.36 to $0.60 per million output tokens. So the question this comparison has to answer is not which model is better. It is whether a roughly twelve-and-a-half-point capability gap justifies between 42 and 139 times the output rate, and that answer is a property of your workload rather than of either model.

The rate cards, including the one that moves this weekend

Solar Pro 4 list price is $0.30 input, $0.06 cached input and $1.20 output per million tokens, and Upstage has run it below list continuously since launch. The schedule on Upstage's own pricing page is explicit, so there is no need to guess which number to budget against:

• Upstage promotion through 11 October 2026, 00:00 UTC — $0.09 input, $0.018 cached, $0.36 output per 1M tokensbr /> • Upstage promotion from 11 October 2026 onward — $0.15 input, $0.03 cached, $0.60 output per 1M tokens, listed with no end date, which means budget against itbr /> • Upstage list price — $0.30 input, $0.06 cached, $1.20 output per 1M tokensbr /> • GPT-6 Astra up to 272,000 input tokens — $10.00 input, $1.00 cached, $12.50 cache write, $50.00 output per 1M tokensbr /> • GPT-6 Astra above 272,000 input tokens — $20.00 input, $2.00 cached, $25.00 cache write, $75.00 output per 1M tokens, applied to the whole request once the threshold is crossed

Read as ratios, that is 11× on input and 5.6× on output at Solar Pro 4's post-promotion rates, 33× and 42× against its list price, and 111× and 139× if the two models are measured on today's promotional numbers. The spread is that large because both sides of the fraction move: Astra's card is the top of the market and Solar Pro 4 is currently near the bottom of it.

The long-context rules are the other line worth reading twice, because they are not symmetric. Astra's threshold sits at 272,000 tokens and its rule is a whole-request reprice — every token in a long request bills at double input and 1.5× output, not just the tokens past the boundary. Upstage's card carries no equivalent step, so a 400,000-token Solar Pro 4 request bills at exactly the same per-token rate as a 4,000-token one. If your prompts routinely run past a quarter of a million tokens, Astra's effective input rate is $20.00 rather than $10.00, and the gap you are paying widens on the exact workload where a 512K-context model is competing.

Where the twelve-point gap comes from

Both models have third-party numbers, which makes this an easier comparison than most in this tier. The pattern across them is consistent: Solar Pro 4's gains over its own predecessor were concentrated in agentic tasks, and that is where it is closest to Astra.

• Intelligence Index — GPT-6 Astra 54.7, Solar Pro 4 42br /> • GPQA Diamond, graduate-level science — Astra 96.1, Solar Pro 4 86.8br /> • Terminal-Bench 2.1, driving a real terminal — Astra 88.4, Solar Pro 4 75.1br /> • τ²-Banking, tool-use against policy — Astra 41.4, Solar Pro 4 35.0br /> • Long-context recall, AA-LCR — Astra 80.7, Solar Pro 4 71br /> • Cost to run the average evaluation task — Astra $0.0516, Solar Pro 4 between $0.0039 and $0.0155br /> • Wall-clock per evaluation task — Astra roughly 8.6 minutes, on a model that returns about 70 tokens per second

The task-cost line is the one that reframes the argument. Running a single hard evaluation task on Solar Pro 4 costs between four and thirteen cents depending on which tier of the promotion applies, against five cents on Astra at standard rates. "Forty-two times the output price" and "thirteen times the cost per task" are both true statements about the same pair of models, and the second one is the number that shows up on an invoice. The difference is that Astra, on Artificial Analysis's measurements, reaches its answer in fewer tokens than Solar Pro 4's predecessor did — 43,000 output tokens per task — and it is still the more expensive model to finish a task with, just nowhere near 42× more.

Two caveats on the Solar Pro 4 column. It is slower than Astra on wall-clock per task even while using fewer tokens, and its published agentic scores come with a specificity worth knowing: on its predecessor's evaluation it improved its honesty score largely by declining to answer, attempting 41% of questions against Pro 3's 92%. For an agent that has to act, a refusal is often better than a confident wrong answer — but it changes how you read any pass-rate comparison.

The comparison neither vendor's card gives you

The benchmark rows above are matched: each side ran the same evaluation. The rows that are absent are the interesting ones, because the two vendors disagree about what to publish.

• Reasoning effort — Astra exposes five named levels (low, medium, high, xhigh, max); Solar Pro 4 documents a reasoning_effort parameter with at least a medium setting, and Upstage's material on the ladder is thinbr /> • Architecture — Astra's parameter count is undisclosed; Solar Pro 4's parameter count is also undisclosed, so neither side offers the serving-economics disclosure their cheaper siblings dobr /> • Long-context behaviour under load — Astra publishes a cost threshold and a documented recall score; Upstage publishes the window and no recall evaluation of its ownbr /> • Input surface — Astra takes text, image and file; Solar Pro 4 is a text modelbr /> • Modality ceiling — 128,000 maximum output tokens on both sides, which is an unusual place for a cheap model to reach parity and is the single most useful spec on Solar Pro 4's sheet for long-generation work

The reasoning-effort line matters more than it looks. A model that lets you turn the thinking budget down is a model whose effective price per easy request is far below its headline rate, because you are paying for fewer reasoning tokens on work that does not need them. Both vendors expose the parameter; neither publishes what the levels cost in tokens, which means the only way to find the real multiple between these two models is to measure it on your own traffic.

The break-even, stated plainly

Ignore index points for a moment and hold the workload fixed. If your application is extraction, classification, summarisation or structured output over documents up to half a million tokens, Solar Pro 4 does the job at a rate Astra cannot approach, and it does not need the reasoning ladder at all — no plausible quality difference in a JSON extraction task is worth 42× on output. If your application is a long-horizon agent that plans, calls tools, recovers from a failed step and has to finish, the eleven-point Terminal-Bench gap and the six-point τ²-Banking gap are the numbers that predict whether it finishes, and a failed agent run costs the full price of the run plus the retry.

The genuinely difficult case is the middle: short, hard, single-shot reasoning tasks where Astra's GPQA advantage is large and its per-task cost is still only a few cents. There the deciding factor is not the rate card but the token count — and the only measurement that answers it is running your own prompts through both models and reading usage. That is also the case where a price cut lands hardest, because the cheaper model is the one being tested against the expensive one, and a 67% move in the cheaper model's rate changes the arithmetic of the test between Monday and today.

Why this is a routing decision rather than a purchase order

GPT-6 Astra is in the OrcaRouter catalogue as openai/gpt-6-astra at OpenAI's own list rates, passed through with 0% markup, which is the whole reason a comparison like this one resolves into a rule instead of a contract. Upstage's Solar family is a different case and the honest line is the one we always use: OrcaRouter does not route any Upstage model, so Solar Pro 4 is not available here — it comes from Upstage's own API and from several third-party platforms, and the pricing schedule above is theirs rather than ours.

What a router is actually for in this pairing is the tier split. The cheap model and the expensive model sit behind one OpenAI-compatible endpoint, a routing rule sends the extraction traffic to the tier that is right for it, and automatic failover promotes a call to the stronger model when the cheaper one errors or returns output your parser rejects. That last mechanism is the practical answer to the misrouting risk this comparison creates: the expensive failure mode is not choosing the wrong model, it is choosing the wrong model and paying for it twice.

What to do before Sunday

If Solar Pro 4 is a candidate for your stack, this is the cheapest week you will get to test it. The promotional rate holds until 00:00 UTC on 11 October, the post-promotion rate of $0.15 and $0.60 is already a third below list, and the model's own published evidence — the Terminal-Bench and τ²-Banking numbers — describes work you can reproduce on your own task set in an afternoon. Run both models on the same prompts, hold the reasoning settings constant, and record tokens per completed task rather than tokens per call. The number that comes out is the only multiple that matters, and it will not be 42×. Then decide, with the knowledge that if the cheaper side wins, the price you were testing it at expires on Sunday — and if the expensive side wins, the twelve and a half index points are the ones you are paying for.

GPT-6 Astra is in the OrcaRouter catalogue as openai/gpt-6-astra at OpenAI's own list rates, passed through with 0% markup.