Gemini 4 vs GPT-6: One Model Against Three Rungs, and the Rung That Lines Up

Neither side of this search term is a single product, and that is the first thing the comparison has to fix. Gemini 4 is one model: Gemini 4 Argon, the only entry under the Gemini 4 heading in G​oogle's own model documentation, priced on the paid tier at $2.00 per million input tokens and $10.00 per million output through 30 November 2026, stepping to $4.00 and $20.00 from 1 December. GPT-6 is three: GPT-6 Astra, the flagship, at $10.00/$50.00 before its long-context step; GPT-6.1 Sol, the middle rung, at $2.00/$10.00; and GPT-6 Luna, the efficiency tier, at $0.10/$0.50 — each listed by OpenAI with a 1.05M-token context window, a 128K-token output ceiling and a knowledge cutoff. So the head-to-head a reader is asking for has to be assembled rung by rung, and exactly one rung lines up on the rate card.

That framing is not a dodge. It is the answer, because the two lines are shaped differently on purpose. G​oogle's 2026 flagship generation is a single operating point you buy at four service levels. OpenAI's is three operating points you choose between. Everything a reader can act on today follows from which shape fits the work.

What each vendor actually publishes, and where the paper stops

The two sides are not documented to the same depth, and it is worth saying so once rather than letting it quietly distort the rest of the page.

OpenAI's model documentation gives, per model, a model ID, the reasoning-effort values the API accepts, the input and output price, the maximum output, the context window, a knowledge cutoff and the tool surface. For GPT-6 Astra and GPT-6.1 Sol the cutoff is 30 April 2026 and the effort ladder runs low to max; GPT-6 Luna carries a 18 May 2026 cutoff and additionally accepts none. OpenAI's pricing page then breaks each model into short-context and long-context columns across Standard, Batch, Flex, Fast and Ultrafast service levels. That is an unusually complete paper trail: a reader can compute a budget without asking anyone.

G​oogle's side is thinner in the specifics that usually settle a comparison. Its model page lists "All Gemini 4 models" and that table has exactly one row — Gemini 4 Argon, endpoint gemini-4-argon, described as a flagship reasoning model with "a unified 1M context window and long decodes up to 1M tokens", badged Stable. Its pricing page carries the four service tiers with the December step. What is not published is a model card, a parameter count, a licence term or a published training-knowledge cutoff. Argon was announced on 30 September 2026, and pages that date its wider rollout are dating G​oogle's access plan; no Gemini 4 model card has followed the one model.

Read that asymmetry for what it is: a fact about what the two vendors chose to write down, not a statement about either model's quality. G​oogle has published enough to transact and not much to compare. That bounds what this page can honestly do.

The rung that lines up, and what happens to it on 1 December

Line the two rate cards up by price and the pairing resolves almost completely into one comparison.

• Gemini 4 Argon, paid tier — $2.00 input / $10.00 output per million, context caching at $0.10, stepping on 1 December 2026 to $4.00 / $20.00 and $0.20

• GPT-6.1 Sol — $2.00 input / $10.00 output per million short context, $0.10 cached input, $4.00 / $15.00 once a request runs long

• GPT-6 Astra — $10.00 / $50.00 short context, $20.00 / $75.00 long, $1.00 cached input

• GPT-6 Luna — $0.10 / $0.50, $0.01 cached input, and again at that rate in long context

Argon's introductory rate and GPT-6.1 Sol's standing rate are the same two numbers, which makes that the one pairing where "which is cheaper" has a real answer rather than a rate-card answer. It is also the pairing with a dated expiry on one side: G​oogle has said its rate is introductory, and the December step moves Argon's standard tier to $4.00/$20.00 — above Sol's short-context output rate and above Sol's long-context output rate of $15.00, with cached input at twice Sol's. Nothing comparable is scheduled on the GPT-6 side in the material published today. A team budgeting a twelve-month run should treat $2.00/$10.00 for Argon as a November number, not a planning number.

The rungs that do not line up are still worth one line each, because they are where the shapes differ. Argon's batch and flex tiers land at $1.00/$5.00, which is cheaper than anything in the GPT-6 line at that service level and is the cheapest way to run the G​oogle model per token. And GPT-6 Luna at $0.10/$0.50 sits an order of magnitude below everything else in this comparison — there is no response from G​oogle's side of this page to a ten-cent input token, because Gemini 4 has one tier of capability rather than a cheap rung.

The one measurement where the same evaluator ran both, and why it is not a head-to-head

One third party has measured both sides on the same suite: Artificial Analysis, Intelligence Index v4.3.2, on the revision current when this page was read on 14 October 2026. The numbers, all of them the evaluator's rather than either vendor's:

• Gemini 4 Argon (High) — index 53, $1.99 per index task, 110M output tokens across the suite, no output speed published by the evaluator

• GPT-6 Astra (Max) — index 53, $3.26 per index task, 60M output tokens, 60.7 tokens per second

• GPT-6.1 Sol (Max) — index 52, $0.72 per index task, 67M output tokens, 59.0 tokens per second

• GPT-6 Sol (Max) — index 48, $1.04 per index task, 77M output tokens, 122.7 tokens per second

• GPT-6 Luna (Max) — index 38, $0.07 per index task, 140M output tokens, 135.0 tokens per second

Two cautions before anyone reads a winner out of that list. First, these are not matched settings. Argon's run is at high reasoning effort and every GPT-6 entry in the table is at maximum, and the evaluator labels each model at its own configuration. Neither vendor publishes the effort curve that would let a reader normalise the two, so the pair of 53s at the top of the table is a coincidence of two different configurations, not a tie decided on identical terms. Second, and this is the more useful half: the same harness that scores the models also prices the run behind the score, and the pricing is internally consistent in a way the headline scores are not. Argon finishing a task in 110M output tokens at $2.00/$10.00 costs $1.99; GPT-6.1 Sol finishing the same suite in 67M tokens at the same $2.00/$10.00 costs $0.72. The rate cards are identical and the bills differ by 2.7 times, because token use, not the price per token, is what the meter is actually counting.

That is the most transferable number on this page, and it applies well beyond these two models: on any workload where output volume is the cost driver, a per-token comparison between two models of the same class is the least informative number available.

Where the comparison cannot be made

Three questions a reader will bring here have no honest answer yet, and it is better to name them than to fill them.

There is no like-for-like capability comparison between the two lines at matched settings. The evaluator's scores come from a different effort setting on each side, and G​oogle publishes no effort curve for Argon. Anyone presenting a clean single-number verdict for this matchup is presenting a configuration difference as a capability difference.

There is no published architecture comparison, because one side has not published its architecture. No parameter count exists for Gemini 4 Argon that can be pointed at, and no context-length figure beyond the unified 1M window and the long-decode path that G​oogle states. Where a third-party estimate circulates, it is an estimate.

And there is no cheaper Gemini 4 to compare against GPT-6 Luna. If the deciding factor in someone's workload is the lowest possible cost per token, the GPT-6 line answers it with a rung and the Gemini 4 line does not answer it at all.

What a reader can call today, and from where

This is the axis where the two lines diverge most sharply in practice, and it is a coverage fact rather than a quality claim, so it should be stated plainly.

Every GPT-6 model described on this page is served through OrcaRouter — GPT-6 Astra, GPT-6.1 Sol, GPT-6 Sol and GPT-6 Luna — at each provider's list price with 0% markup added, so a vendor price move reaches the invoice the same day rather than after a repricing cycle. Because the catalogue routes across providers of the same model, an access posture that is still settling does not have to be a single point of failure on a production path, and swapping between Sol and Luna to find a cost target is a model string rather than a procurement exercise.

Nothing on the Gemini 4 stem is in that catalogue as of 14 October 2026 — of the 207 model identifiers we route, none matches it. Gemini 4 Argon is reachable through G​oogle's own API and several third-party platforms, and a reader who wants both lines behind one key cannot have that here today. That is our gap, stated once, and not a recommendation about anyone else's.

How to decide

Ask which of these describes the work:

• One workload, one level of capability, and the real question is whether the bill lands at $2/$10 or at $4/$20 — Gemini 4 Argon is the model, and the 1 December step is the number to plan against rather than the introductory rate you would be quoted this month.

• A portfolio of workloads with genuinely different cost and capability needs, where a cheap rung and a strong rung both have to exist under one contract — GPT-6 is the structure, and the open question becomes which rung, not which vendor.

If the second describes you, the decision is rung-by-rung and the measurement above is the one to carry into it: at the same advertised $2.00/$10.00, GPT-6.1 Sol completed the evaluator's suite on $0.72 of tokens against Argon's $1.99, so a token-count comparison of your own traffic is worth more than either rate card. If the first describes you, then the interesting question is not Gemini 4 against GPT-6 at all but Gemini 4 Argon against whichever single GPT-6 rung matches your budget, and the honest answer is that Argon is competitive on score and expensive on tokens.

The one fact that most often decides it: Gemini 4 is a model you tune a service tier around, and GPT-6 is a line you shop inside — so if your cost problem is "we need a cheaper tier", only one of these two answers the question.

If the deciding factor is the lowest cost per token, the line answers it with a rung — GPT-6 Luna at $0.10 input per million tokens, routed through the same key as the rest of the line.

Comparisons like this one are also where the failure modes show up: the documented AI incidents and agent failures behind models in this class are collected in the OrcaRouter AI Incident Archive , where each entry carries its own sources, severity, confidence grade and disputed flag.