Gemini 4 vs GPT-6 Astra: A Dead Heat on the Index, a 5x Gap on the Invoice

Two frontier models, one independent index, one identical score. Artificial Analysis puts Gemini 4 Argon at high effort and GPT-6 Astra at maximum effort at 53 apiece on Intelligence Index v4.3.2, the composite of ten evaluations the evaluator runs on revision v4.3.2. Then it prices the run behind each score: $1.99 of pay-per-token cost per index task for Gemini 4 Argon against $3.26 for GPT-6 Astra. Not a tie.

That is the useful frame for this matchup, because the headline rate cards make the gap look even wider than it is and the per-task numbers make it look narrower. Gemini 4 Argon is listed at $2.00 per million input and $10.00 per million output on an introductory rate that doubles on 2026-12-01. GPT-6 Astra is listed at $10.00 per million input, $1.00 cached input and $50.00 per million output. Both scores are equal; the rate cards differ by five times on output and the per-task bill by 1.6 times.

The models: Gemini 4 Argon is the only named member of G​oogle's Gemini 4 line, announced 2026-09-30 and generally available on G​oogle's paid tier since 2026-10-13. GPT-6 Astra is O​penAI's flagship, published 2026-09-03 and rolled out under a gated access programme. Both sit in the same 1M-token context class and both are reasoning models with configurable effort.

Why a five-fold rate can be a 1.6x bill

The reconciliation is token counts. Astra is priced per token at a premium and answers in fewer of them than the rate card implies, while Argon is cheap per token but not so lean per task that the advantage compounds to five. On the evaluator's own accounting, Argon's cost per index task lands at $1.99 and Astra's at $3.26 — the price-per-million differential compresses because most of what the harness measures is reasoning work that both models are burning tokens on.

The same evaluator lists Argon's blended cost at $1.47 per million tokens against Astra's $7.70 on the standard 7:2:1 cache-hit/input/output mix. That blend flatters Argon, and it is worth saying so: the mix assumes a very high cache-hit share, and Astra's $1.00 per million cached-input rate is ten times Argon's $0.10. If your workload is mostly cache hits rather than mostly fresh output, the effective gap between these two is smaller than either the rate card or the blend suggests.

Where Astra does not trade away anything is access posture. It is the first model O​penAI has shipped that crosses the Critical cybersecurity capability threshold under its own Preparedness Framework, and the rollout follows from that: limited organisations first, enterprise access switched off by default until an administrator enables it under an applicable rate card and agreement, dedicated admin controls for browsing and uploads, and a planned widening through the Daybreak programme. Nothing equivalent is documented for Gemini 4 Argon, which is generally available on G​oogle's paid tier to anyone with a billing relationship. For a team that needs procurement paperwork, that difference is the decision; for a team that needs a model on a key this afternoon, it points the other way.

Output ceilings, tiers and effort settings

Both models carry a 1M-token context window. The published decode ceilings diverge: G​oogle publishes 1,000,000 tokens of output for Argon, and Argon is documented as a single model with four service tiers — Standard at $2/$10, Priority at $3.60/$18, Flex and Batch at $1/$5. Astra is documented as one model on a single rate card with a batch tier at half rate, and its differentiated access is organisational rather than a billing option.

Effort is where this comparison is least settled. The index scores above are Argon at high and Astra at maximum, and those are not matched settings in any meaningful sense — the evaluator labels each model at its own default. Ars Technica's October reporting on the Argon rollout notes the model is reachable for paying subscribers, and G​oogle's own launch material describes a model built for its hardest problems; neither vendor publishes the effort curve that would let a reader normalise the two. Where a head-to-head at matched settings does not exist, this page will not manufacture one.

On independent availability, one of these two is a phone call away and the other is a key away. GPT-6 Astra is in our catalogue at provider list price with 0% markup added, so a vendor price move reaches your invoice the same day rather than after a repricing cycle, and automatic failover across providers means a rollout that is still settling does not have to be a single point of failure on a production path. Gemini 4 Argon is not in that catalogue as of 2026-10-14; it is reachable through G​oogle's own API and several third-party platforms, and a reader who wants both behind one key cannot have that here yet.

Do not confuse this page with the Pro-tier comparison

A version of this matchup already circulates in the form of "Gemini 4 Pro vs GPT-6 Astra", built around a single-outcome test. That is a different question about a model G​oogle has not shipped, and it is a page we have already written. This page compares the two lines as they actually exist: one Gemini 4 model against one O​penAI flagship.

The rule

If the deliverable is long-form output at volume, or an agent that writes large artifacts in one pass, Gemini 4 Argon's per-token economics and 1M-token decode ceiling are the reason to pick it — provided you plan the 2026-12-01 rate step into the budget rather than discovering it. If the work is long-horizon professional output where a stronger answer on hard reasoning is worth paying for, or you need enterprise administration, audit and a vendor with a documented access-control framework, GPT-6 Astra is the more complete product, and it is the one you can call here today.

Both models are worth running behind a router rather than on a single provider, and that is not a pitch for one vendor's worth of either: it is the general answer to a rollout that is still settling. Astra's access terms were still moving through September and Argon's rate card has a dated step in it, so neither is the model to wire into a path with no fallback.

The one fact that most often decides it: the index says these are the same model, and the bill says they are not. If your workload's cost is dominated by fresh output tokens rather than cached input, Argon's advantage is closer to the rate-card ratio than to the per-task one — measure before you standardise.

Both of these, one endpoint

GPT-6 Astra is served through OrcaRouter at OpenAI's list price with 0% markup added, and because the catalogue routes across providers of the same model, a rollout that is still settling does not have to be a single point of failure. Gemini 4 Argon is not in that catalogue as of 2026-10-14 — it is reachable through G​oogle's own API and several third-party platforms.

The rung below Astra is a fifth of its rate card. GPT-6.1 Sol Both are on one key, so the substitution is a model string rather than a procurement cycle.

A model choice is also a question of what has gone wrong before: the documented AI incidents and agent failures for models of this class are collected in the OrcaRouter AI Incident Archive , where each entry carries its own sources, severity, confidence grade and disputed flag.