AI SDR Cost With GPT-6, What the Bill Actually Looks Like

AI SDR cost with GPT-6 runs about 90 to 260 dollars a month in model tokens for a small outbound team, depending on how aggressively you cache. That token line is the smallest of four bills, model, enrichment, sending, and human review. Seat vendors bundle all four into one number and charge a fifth for margin.

That is the whole finding. The rest of this piece sizes a real month of AI SDR work at GPT-6's new rates, prices it against the platform seats that dominate the category, and points out where the operator saving actually lives. It is not the number on the model rate card. It is what you do with the other three lines.

What GPT-6 Astra shipped, and what it charges to run

OpenAI released GPT-6 Astra on 3 September 2026 at 10 dollars per million input tokens and 50 dollars per million output tokens, with cached input at 1 dollar per million and cache writes at 12.50 dollars, per aipricing.guru's OpenAI rate card. Above 272,000 input tokens the long context tier applies at 20 dollars input and 75 dollars output. Batch and Flex modes run at half the standard rate; Fast mode runs at double. That is the full sticker.

On the comparison line, GPT-6 is 2.5 times the promotional rate of GPT-5.6 Sol (4 dollars input, 20 dollars output) and matches Anthropic's Fable 5.1 on both headline numbers, per Yahoo Finance's frontier pricing breakdown. Every ranking article for "AI SDR cost with GPT-6" quotes those numbers and stops. Numbers on a rate card are not a bill. Bills come from workloads, and workloads for an AI SDR are four lines, not one.

The four cost lines of an AI SDR, not one

An AI SDR bill is never a single line. It is four, and confusing them is the mechanism that makes seat pricing look reasonable. Take the four apart and the pricing question becomes tractable.

• Model tokens. The reasoning line. Research, scoring, drafting, and reply classification. This is the only line GPT-6 changes.
• Enrichment data. Firmographic and contact data for the accounts the agent researches. Crustdata, FullEnrich, ZoomInfo, whichever provider supplies the layer.
• Sending infrastructure. Warmed domains, secondary mailboxes, sequencer, LinkedIn access. This is the layer that carries your messages or throttles your domain, regardless of what model wrote the copy.
• Human review gate. Someone approves messaging, handles the replies the classifier flags, and steps into live conversations when a lead escalates. Budget zero hours a week and the whole loop breaks.

Seat vendors bundle these four into one opaque monthly number and price the bundle against the human SDR the product replaces. The seat looks reasonable next to a fully loaded rep at 90,000 to 100,000 dollars a year, and it looks extortionate next to the token bill for the same work. Both readings are correct. They anchor to different comparisons, which is why the operator answer starts by pulling the bundle apart. The full AI SDR cost breakdown covers that decomposition end to end; this piece prices the top line at the new GPT-6 rates and then walks the other three.

Sizing one month of AI SDR work on GPT-6

To price the model line you have to define the workload. A realistic month for a small outbound team looks like this. Research 1,000 target accounts. Score them against the ICP. Draft 2,000 personalized touches. Classify the 2,000 replies that come back. This is the same monthly shape used in the AI SDR cost on Kimi K3 walkthrough, which keeps the two models directly comparable.

Every token figure that follows is an estimate labeled as such. Real numbers move with prompt length, caching hit rate, and how much reasoning each step actually needs. The point is order of magnitude, not a quote.

• Account research. Roughly 6,000 input tokens per dossier and 1,000 output tokens. Across 1,000 accounts, 6 million input and 1 million output. GPT-6's million token input window means the full site copy, funding history, exec bios, and product changelog fit into one call rather than a retrieval loop.
• Scoring and qualification. About 2,000 input tokens and 300 output tokens per account. Roughly 2 million input and 0.3 million output for the month. This is the step where GPT-6's reasoning depth pays back first, and where the lead qualification skill is designed to run as an editable gate rather than a black box.
• Drafting personalized touches. Two touches per qualified account, 2,000 total. About 3,000 input tokens and 350 output tokens per touch, so roughly 6 million input and 0.7 million output.
• Reply classification. 2,000 replies at about 500 input tokens and 100 output tokens each. Roughly 1 million input and 0.2 million output for the month.

Total estimated budget for the month: about 15 million input tokens and 2.2 million output tokens.

Now the arithmetic. Applied to GPT-6 Astra at 10 dollars per million input and 50 dollars per million output, 15 million input tokens cost 150 dollars and 2.2 million output tokens cost 110 dollars. Uncached total: about 260 dollars for the month.

If half the input reads hit the cache, and that is realistic once the ICP definition, the tone guide, and the objection playbook get reused across scoring, drafting, and classification, 7.5 million cached input tokens cost 7.50 dollars instead of 75 dollars. The month drops to about 190 dollars. Cache more aggressively and the same workload lands closer to 90 dollars, because cached input on GPT-6 is 1 dollar per million versus 10 dollars per million uncached, per aipricing.guru, and most of the input on a well run outbound loop is stable rulebook, not fresh dossier text.

That is the 90 to 260 dollar range in the direct answer. It is the token line for a full month of AI SDR work on GPT-6, honestly bounded on both sides.

What the same month costs on AI SDR platform seats

Now the anchor most buyers actually compare against. What does the seat cost for the same volume?

• 11x sits at roughly 5,000 dollars a month on annual contract, per the Altitude pricing index. That is 60,000 dollars a year.
• Artisan advertises its Employee plan at 600 dollars a month billed annually, with reported real world entry points closer to 999 to 2,000 dollars a month, per the 11x guide on Artisan pricing.
• AiSDR, verified this week on the AiSDR pricing page, publishes three tiers. Solo at 250 dollars a month for 200 researched contacts, 1 domain, and 3 mailboxes. Explore at 900 dollars a month for 800 contacts and 2 domains. Scale at 2,500 dollars a month for 2,500 contacts and 6 domains. Explore and Scale ship on quarterly contracts with an annual option at a 20 percent discount.

Compare against the token math. The Artisan Employee plan at 600 dollars a month is about 6 times the low end of the GPT-6 token bill. AiSDR Scale at 2,500 dollars is roughly 25 times it. 11x is more than 50 times it. Even sizing the platform bundle generously against a small team's actual send volume, no seat lands within striking distance of the model line.

The seat is not primarily selling you tokens. It is selling you the other three lines, the enrichment data, the sending infrastructure, and the operator work that would otherwise fall on your team. Those lines are real. They cost real money. They just do not cost 5,000 dollars a month at small team volume, which is exactly the ranking gap operators end up paying. The full vendor by vendor grading of the category is in the best AI SDR platforms of 2026, and the operator's map of the tool categories underneath sits in AI SDR tools mapped by what they actually do.

Why the model line is the smallest bill

This is the finding that decides the whole procurement question. The model line on an AI SDR bill has stopped being the interesting one. At 90 to 260 dollars a month it is noise next to the seat prices, and it moves less than any of the other three lines when you swap a model.

The enrichment data line often runs 200 to 800 dollars a month for a small team, depending on whether you already have a provider and how many waterfall hops you use. The sending infrastructure line, including sequencer, warmed domains, and LinkedIn access, sits at 50 to 400 dollars a month. The human review gate, budgeted honestly at 45 minutes a week, is not a dollar line at all but an operator time line, and it is the one that keeps deliverability clean and messaging on brand. Together those three lines dwarf the model bill at every reasonable team size.

What that means for procurement is direct. Choosing between GPT-6 at 10 dollars input and GPT-5.6 Sol at 4 dollars input is a fight over 90 to 200 dollars a month in a total bill that might be 800 to 1,500 dollars all in. It is worth doing. It is not the fight that decides whether outbound works. The fight that decides outbound is the enrichment layer, the deliverability layer, and whether a human reads the drafts before they go out. The operator's read on GPT-6 for outbound covers the same finding from the model side and lands in the same place.

The corollary is the reason to run this stack yourself rather than buy a seat. A seat prices all four lines together and never tells you which one is which. You cannot swap the model. You cannot inspect the prompt. You cannot see what fraction of the seat is orchestration versus margin. An own stack decouples the four lines and prices each honestly.

Cache the parts that never change, or the number breaks

The single largest lever on the token bill is not model choice. It is caching. GPT-6 charges 10 dollars per million uncached input tokens and 1 dollar per million cached input tokens, a ten times gap. Ignore that gap and the bill roughly triples.

Most of the input on a real AI SDR loop is stable across prospects. The ICP definition. The tone guide. The objection playbook. The do not target list. The scoring rubric. The reply classification rules. None of these change per account, and all of them get re read on every call. Pin them into a cached system context and the input rate drops from 10 dollars per million to 1 dollar per million on 70 to 90 percent of the input volume. That is the difference between the 260 dollar uncached month and the 90 dollar aggressively cached month.

The related lever is per step routing. GPT-6's premium over GPT-5.6 Sol is 2.5 times on input and 2.5 times on output. Pay it where reasoning depth actually decides the outcome. Account research and scoring go to GPT-6 first, because a longer coherent chain over more context is exactly what the new model does better. Plain drafting on a well targeted list often runs fine on the cheaper tier, and reply classification always does. Routing model choice by step, not by stack, is where the last 30 to 50 percent of the bill hides.

Set up and forget economics, price against hours, not seats

The frame that clarifies this decision is not model versus seat. It is hours saved versus dollars spent. A platform seat's implicit pitch is that it replaces a person. A GPT-6 stack's implicit pitch is that it replaces the middle mile of a person's week, the sourcing, enrichment, drafting, and classification work, and leaves the human on the first mile and the last mile.

At a small team scale, the middle mile is roughly 15 to 25 hours a week of operator time before automation. A GPT-6 stack that costs 90 to 260 dollars a month in tokens plus 500 to 1,000 dollars in enrichment and sending infrastructure absorbs that middle mile and leaves the operator with about 45 minutes of weekly review, prompt tuning, and escalated reply handling. Under that 45 minute bar the stack works. Above it, the review gate eats the model saving and the whole thing starts to look like a seat.

The runtime that makes 45 minutes a week actually enough is where an operating system layer sits underneath. Yalc's AI sales agents run middle mile GTM work on top of your existing CRM, sequencer, and data providers, with the prompts in markdown files an operator can edit, the model choice as a config value, and the review gate written into the loop by default. When a cheaper capable model ships after GPT-6, and one will, the swap is a config change, not a procurement cycle. That is the property that turns model economics from a one time saving into a compounding one, and it is the property no seat can copy. For a quick sanity check against your own OTE and headcount, the AI SDR cost calculator runs the seat versus own stack math with your numbers.

What to do this week

Three moves for an operator sizing the upgrade this week.

First, run the four line decomposition on your current bill. Write down what you actually pay for enrichment, sending, and orchestration, plus the estimated model tokens. Most teams find that the seat they are on is 40 to 70 percent margin and orchestration wrapper, not enrichment or infrastructure they could not buy directly. Once the four lines are separate, the swap decision is arithmetic.

Second, cache aggressively before you compare models. Move the ICP, the rulebook, the tone guide, and the persistent context into a cached system prompt. Rerun the same workload for a week. Compare the uncached and cached bills. If you have not caught the 90 percent input rate drop, the model comparison is not meaningful.

Third, route the reasoning tier per step. Research and scoring on GPT-6. Drafting on the cheaper tier if your list is well targeted and the copy already reads clean. Classification on the cheaper tier always. Run a 100 prospect A/B for two weeks with reply rate as the only measure. If GPT-6 does not lift reply rate outside noise on the plain drafting step, it belongs only on the research step in your loop. That test costs less than a day of the uncached workflow and it settles the model question for the quarter.