GPT-6 Astra vs Gemini 3.1 Pro: A New Flagship Against a Six-Month-Old Bargain

Here is the whole matchup in two numbers before any nuance: Google's Gemini 3.1 Pro charges $2.00 per million input tokens and $12.00 per million output for prompts up to 200K tokens, and OpenAI's GPT-6 Astra charges $10.00 and $50.00 flat. By the calendar the cheaper model is also the older one by a wide margin — Gemini 3.1 Pro shipped February 19, 2026, and GPT-6 Astra shipped September 3, 2026 — which makes this comparison unusual in the other direction too: usually the incumbent is the expensive one and the newcomer undercuts it. Here the newcomer costs five times as much on input, and the question is whether GPT-6 Astra is five times the model that Gemini 3.1 Pro still is after six and a half months without a Pro-tier refresh from Google.

Both sit at the top of their maker's product lines, but under very different stewardship. Gemini 3.1 Pro is Google's current Pro flagship — released in preview in February, still branded "preview" today, and never refreshed while Google iterated its Flash line through three releases and began talking about a "Gemini 4." GPT-6 Astra is OpenAI's newest flagship and its first "Critical"-rated model under its internal Preparedness Framework, arriving with a staged rollout, an enterprise opt-in, and self-reported benchmark claims that its maker describes as a generational leap. One of these models has been static and cheap for half a year. The other is two days old and priced like the frontier moved. For a developer choosing between them, the real question is which of those two states is a better match for the workload — because on paper, they are closer than the price gap suggests.

Two pricing tables, two theories of length

Google prices Gemini 3.1 Pro by prompt length: $2 in / $12 out up to 200K input tokens, stepping to $4 in / $18 out above that, with cache reads at $0.20–$0.40 and batch at half price. The message is that short prompts are cheap and long ones cost more — a pricing model tuned for the search-and-grounding workloads Google expects most traffic to be. OpenAI prices GPT-6 Astra flat at $10/$50 with cached input at $1.00, and then hits very long prompts with a different kind of penalty: anything above roughly 272K input tokens is repriced at 2x input and 1.5x output — effectively $20 in / $75 out. The two models punish long context in opposite places. Gemini is cheap until 200K and then moderately more expensive; Astra is flat until 272K and then sharply more expensive. A 300K-token task is therefore far more expensive on GPT-6 Astra than on Gemini 3.1 Pro, while a 50K-token task is four-to-five times cheaper on Gemini. For workloads that hug the bottom of the context range, the Astra price is hard to justify; for workloads that genuinely need the top of it, both vendors extract a surcharge, and OpenAI's is the steeper one.

Where Gemini 3.1 Pro is genuinely ahead

Google's flagship has three advantages that no OpenAI model matches, and they are easy to miss because the launch noise around Astra is so loud. First, input modalities: Gemini 3.1 Pro takes text, image, audio, video, and PDF in a single prompt; GPT-6 Astra is text-plus-image only, with text output — the same modality envelope as its predecessor. Second, native grounding: Gemini ships with Google Search, Maps, and code execution wired into the API, which is a genuine capability difference for agentic research and any task that needs fresh, verified information rather than parametric recall. Third, price on everything that stays under 200K tokens, including batch at $1/$6 — the cheapest way to run a large offline job against either model by a wide margin. None of these show up on a reasoning benchmark, which is exactly why they get lost in a flagship-vs-flagship comparison that only quotes scores.

The spec sheet, side by side

• Released — GPT-6 Astra: September 3, 2026. Gemini 3.1 Pro: February 19, 2026 (still "preview").

• Price (≤200K prompt) — GPT-6 Astra: $10.00 / $50.00 per 1M flat. Gemini 3.1 Pro: $2.00 / $12.00 per 1M.

• Price (long prompt) — GPT-6 Astra: ~$20.00 / $75.00 per 1M above 272K input. Gemini 3.1 Pro: $4.00 / $18.00 per 1M above 200K input.

• Context / output ceiling — GPT-6 Astra: ~1.05M input, 128K output. Gemini 3.1 Pro: 1M input, ~65K output.

• Inputs — GPT-6 Astra: text + image. Gemini 3.1 Pro: text, image, audio, video, PDF.

• Grounding / tools — GPT-6 Astra: web search, file search, code execution. Gemini 3.1 Pro: native Google Search, Maps, code execution, function calling.

• Reasoning controls — GPT-6 Astra: effort low–max. Gemini 3.1 Pro: adjustable reasoning effort.

Where the six months show

Gemini 3.1 Pro led the Artificial Analysis Intelligence Index when it shipped, and it no longer does: the late-summer flagships — Claude Opus 5, GPT-5.6 Sol, and now GPT-6 Astra — all measure above it on the current index scale, and Google has not shipped a Pro-tier answer. The most dramatic gap is on ARC-AGI-3, the benchmark built to be unsolvable by ordinary chain-of-thought. OpenAI reports 99.9% for Astra on its own harness, and the independent ARC Prize run of the standard harness measured 62.7% — while independent runs put Gemini 3.1 Pro below one percent, the same near-zero band as the rest of the pre-Astra frontier. On the original ARC-AGI-2, Google's own figure for Gemini 3.1 Pro was a strong 77.1% at launch; the successor benchmark is a different regime, and it is the one place where OpenAI's new paradigm genuinely leaves a six-month-old model behind.

The subtler regression is long-context reliability. Independent multi-needle testing (MRCR v2) shows Gemini 3.1 Pro retrieving accurately at 128K and degrading sharply toward the top of its 1M window — a collapse pattern Google has not fully fixed in the six months since launch. Astra's 1.05M context is new and unproven at scale, but it is the area OpenAI most aggressively claims to have engineered, including a 128K output ceiling that lets a single response carry a much larger generated artifact than Gemini's ~65K. If your workload is one enormous context that must be read faithfully end to end, the choice is between a model with a measured drop-off and a model whose top-of-window behavior nobody has stress-tested yet. That is not a comfortable choice in either direction.

The practical decision

For the large majority of production workloads — RAG over documents, grounding-heavy research, multimodal input, anything that fits under 200K tokens — Gemini 3.1 Pro is the economically rational pick today, and the gap is not close: at five-times-cheaper input and a modality and grounding advantage, GPT-6 Astra is paying a huge premium for reasoning headroom those workloads will never use. For agentic coding, long-horizon computer use, and problems that genuinely need a 128K generated artifact or a reliable near-1M context, GPT-6 Astra is the model to evaluate — with the honest caveat that its headline numbers are OpenAI's and the independent day-one read, while strong on token efficiency, shows a smaller capability gap than the marketing does. Gemini's six-month freeze is the deciding context: you are choosing between a cheap, capable, mature model that Google has stopped improving and an expensive, newly shipped one that OpenAI is actively racing forward.

Because both are hosted and both are worth keeping in reach, the practical answer is to route rather than replace. Gemini 3.1 Pro is on OrcaRouter at Google's list price, passed through with no markup, so its $2/$12 economics survive contact with a router — and the routing DSL makes the sub-200K-versus-long-prompt split a rule rather than a manual decision, sending short grounded queries to Gemini and reserving GPT-6 Astra for the tasks that justify its rate. GPT-6 Astra is not on OrcaRouter yet as of this writing; when a provider begins serving gpt-6-astra, the model page will carry OpenAI's list rate the same day, and the failover rule that already keeps a workload alive across providers is the same mechanism that lets you adopt Astra on a measured slice of traffic without betting the whole pipeline on a two-day-old model.

The verdict

GPT-6 Astra vs Gemini 3.1 Pro is a matchup between a model Google has left alone for six months and a model OpenAI shipped two days ago — and the price gap mostly reflects that asymmetry, not a capability gap of the same size. If you need cheap, multimodal, grounded reasoning at scale, Gemini 3.1 Pro wins this comparison outright and has for months. If you need the new-paradigm reasoning, the large output ceiling, or a serious shot at reliable top-of-window context, GPT-6 Astra is worth its premium on the workloads that use those things — measured on your own tasks, not on OpenAI's launch table. The mistake would be treating a six-month-old bargain and a two-day-old flagship as if the age difference did not matter, in either direction. It is the entire story.