GPT-6 Astra vs GPT-5.6 Sol: Same Index Score, 2.5x the Price — Here's What You're Buying

Start with the number OpenAI's launch presentation would prefer you not lead with: on the Artificial Analysis Intelligence Index, GPT-6 Astra and GPT-5.6 Sol both score 61. OpenAI prices one of them at $10.00 per million input tokens and $50.00 per million output, and the other at $4.00 and $20.00 — exactly 2.5 times less. The same independent lab that produced that equal score also measured Astra hallucinating at 51% under max effort against Sol's 92%, using about a third of Sol's tokens on the same coding-agent tasks, and scoring 67 to Sol's 65 on the coding-agent index. So the honest answer to "is GPT-6 Astra worth 2.5x GPT-5.6 Sol?" is: not because it is two and a half times smarter — by the one neutral yardstick that exists it is not smarter at all — but because it is dramatically more reliable per token, and on agentic coding it is cheaper per completed task despite the higher rate.

This is an unusually clean comparison because both models are OpenAI's, both are current, and both are still for sale. GPT-5.6 Sol launched July 9, 2026, as the flagship of the GPT-5.6 family, and it has not been retired or even deprecated: it sits on OpenAI's pricing page today, repositioned as the prior-generation tier under the new flagship. GPT-6 Astra launched September 3, 2026, roughly two months later, sharing Sol's ~1.05M-token context window and its text-plus-image modality envelope while adding a newer knowledge cutoff (April 30, 2026, against Sol's February 16), a wider reasoning-effort dial, and — the part OpenAI is leaning on hardest — a "Critical" capability rating under its internal safety framework that no previous OpenAI model has carried. Every benchmark below is either OpenAI's own claim or Artificial Analysis' independent run, and this article keeps the two labeled, because in this matchup the labeling is the substance.

OpenAI's own case for the leap

OpenAI's launch table compares Astra directly against Sol, and on its own measurements the gap is enormous: ARC-AGI-3 at 99.9% against Sol's 7.8%, ExploitBench at 100% against 78.5%, FrontierMath Tier 4 at 97.6% against 83.0%, Terminal-Bench 4.0 at 57.9% against 37.3%, and a ~47% faster completion time on computer-use tasks. These are the numbers behind the "generational leap" language and the AGI-era framing. They are also all vendor-reported, produced on harnesses OpenAI chose, and — as the next section shows — the ARC-AGI-3 figure in particular depends so heavily on the harness that quoting it without context is misleading in both directions.

The ARC-AGI-3 number that shouldn't survive contact

ARC-AGI-3 is where this comparison gets genuinely strange, because OpenAI has published three different Sol scores and two different Astra scores depending on the harness. In its own July 31 post, OpenAI reported GPT-5.6 Sol at 38.3% on ARC-AGI-3 after enabling retained-reasoning and context-compaction settings — up from 13.3%, and above Claude Opus 5's 30.2%. Yet the Astra launch table quotes Sol at 7.8%, the stock-harness figure. On the Astra side, OpenAI reports 99.9% on its own "Provider Adapter" harness, while the independent ARC Prize run on the standard harness measured 62.7%. The two numbers OpenAI wants you to compare — 99.9 versus 7.8 — come from incompatible setups, and so does the more defensible comparison of 62.7 (independent Astra) versus 38.3 (OpenAI's best Sol configuration). The generational leap is real on every version of this benchmark; its size is a product of the harness, not the model.

What independent measurement actually shows

Artificial Analysis ran Astra within a day of launch, and its numbers give this matchup its real shape. On the Intelligence Index, Astra at 61 equals Sol at 61 — no measured general-reasoning gain. On the coding-agent index, Astra at 67 (in Codex) edges Sol at 65 (also in Codex) by two points while using roughly a third of the tokens — which means the two models cost about the same per completed coding task, with Astra scoring modestly higher. The largest independent delta is reliability: Astra's measured 51% hallucination rate at max effort is nearly half Sol's 92%, and its accuracy on the same index tasks runs about four points higher. The trade-off appears at the margins: AA estimates Astra is roughly 75% more expensive per Intelligence-Index task at max effort than Sol, and on a few specific evals (banking, scientific code, a long-context retrieval suite) Astra posts small regressions against its predecessor rather than gains. Read soberly, the independent data says OpenAI shipped a model that is not generally smarter than Sol but is far less prone to fabrication, far more token-efficient on agentic work, and narrowly better at coding — and priced it at 2.5x.

Specs side by side

• Released — GPT-6 Astra: September 3, 2026. GPT-5.6 Sol: July 9, 2026.

• Price — GPT-6 Astra: $10.00 / $50.00 per 1M, $1.00 cached input. GPT-5.6 Sol: $4.00 / $20.00 per 1M (down from a $5/$30 launch list), $0.40 cached input.

• Context / output ceiling — both ~1.05M input; Astra 128K output, Sol 128K output.

• Knowledge cutoff — GPT-6 Astra: April 30, 2026. GPT-5.6 Sol: February 16, 2026.

• Inputs / outputs — both text + image in, text out.

• Reasoning controls — GPT-6 Astra: effort low–max (API default low). GPT-5.6 Sol: effort none–max.

• Status — GPT-6 Astra: current flagship, staged rollout, enterprise opt-in. GPT-5.6 Sol: still sold, repositioned as the prior-generation tier.

The decision: what 2.5x actually buys

If your workload is ordinary generation, extraction, or reasoning where tokens are a small cost and a wrong answer is cheap to catch, GPT-6 Astra is not worth 2.5x GPT-5.6 Sol — the independent index says they are the same model at that altitude, and the per-task cost of running Astra at max effort is higher. If your workload is agentic coding, long-horizon computer use, or anything where a hallucination propagates through many steps and token spend scales with task length, Astra's case is strong: the halved hallucination rate and the third-of-the-tokens efficiency flip the value equation despite the rate, which is exactly what the independent per-task cost estimates show. The customers OpenAI is courting — the ones who pay for Astra Pro on subscriptions and the enterprises switching it on manually — are buying reliability and token economy, not a smarter model. Whether that is worth the premium is a workload question, not a benchmark question.

The unusually clean way to answer it is to run both, and this is one matchup where the routing layer is ready today. GPT-5.6 Sol is on OrcaRouter at OpenAI's current list price, passed through with no markup, so the 2.5x question can be measured against a live Sol endpoint behind the same API key as the other 200+ models in the catalog. GPT-6 Astra is not on OrcaRouter yet as of this writing — we checked before publishing — and when a provider begins serving gpt-6-astra its price will appear on the model page the same day, at OpenAI's list rate. Until then, the automatic-failover rules that keep production traffic alive across providers are the same mechanism a team can use to A/B the two OpenAI flagships the moment Astra lands on a router, deciding per request rather than on a launch table.

The verdict

GPT-6 Astra vs GPT-5.6 Sol is the rare generational comparison where the honest answer fits in one sentence: OpenAI is charging 2.5x for a model that independent testing finds no smarter than its predecessor, and the premium is justified only for workloads where the halved hallucination rate and the dramatic token efficiency pay for themselves — which is to say, agentic and long-horizon work, not routine reasoning. The launch table, with its harness-dependent ARC-AGI-3 numbers and its 2.5x price tag, is selling a leap. The independent data sells something more specific and more useful: a reliability upgrade with a coding-agent edge, priced for the workloads that actually need it. GPT-5.6 Sol remains the rational choice for the long tail of cheaper, shorter, less critical work — and the fact that OpenAI kept it on sale at $4/$20 rather than retiring it is the company quietly agreeing.