The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.
Share
DeepSeek Just Changed Analytics
Published about 6 hours agoΒ β’Β 6 min read
β
Use Data Or Be Used By Data!
The August 3 issue of Seotistics is here for you!
DeepSeek is the buzz of the moment and it's not even random hype.
Chinese models are revolutionizing the LLM world because they are sooo cheap and good.
This has some consequences on Web Analytics too, mind you!
Please move this email to your Primary inbox or reply to it. This is to prevent Seotistics goes into spam by accident. Gmail users can read this tutorial to do it.
On July 31, DeepSeek released the public beta of V4 Flash 0731.
No new architecture.
It's still the same 284B-parameter mixture-of-experts model with a 1-million-token context window.
β
What changed is the post-training: significantly better agentic behavior, coding, and tool-calling.
They also added native support for the OpenAI Responses API format, which means it's a drop-in replacement for any workflow already calling OpenAI.
The pricing: $0.14 per million input tokens, $0.28 per million output tokens. Cache hits cost $0.003 per million input tokens.
For context: GPT-4o is roughly 5-10x more expensive depending on the task, Claude Sonnet is in a similar range.
V4 Flash 0731 is close to Claude Opus 4.8 but costs a fraction of it. Why even use Opus 4.8? Screenshot from OpenRouter.
Look, the model quality debate is interesting but not the point here.
The point is that the price-to-capability ratio just moved in a direction that makes some analytics use cases genuinely viable that weren't before.
Model
Input /1M
Output /1M
100K queries
Tier
GPT-4.1 Nano
$0.05
$0.20
$0.10
Budget
Gemini 2.5 Flash-Lite
$0.10
$0.40
$0.20
Budget
β DeepSeek V4 Flash 0731
$0.14
$0.28
$0.28
Budget
GPT-4o mini
$0.15
$0.60
$0.30
Budget
Gemini 2.5 Flash β‘
$0.15
$1.25
$0.30
Mid
GPT-4.1 Mini
$0.40
$1.60
$0.80
Mid
Claude Haiku 4.5
$1.00
$5.00
$2.00
Mid
Gemini 2.5 Pro
$1.25
$10.00
$2.50
Premium
Claude Sonnet 5 β
$2.00
$10.00
$4.00
Premium
GPT-4o
$2.50
$10.00
$5.00
Premium
Claude Opus 4.8
$5.00
$25.00
$10.00
Top-end
* "100K queries" = cost to classify 100K GSC queries (~2M input tokens, ~20 tokens/query).
β Claude Sonnet 5 introductory price, rises to $3/$15 after Aug 31 2026.
β‘ Gemini 2.5 Flash scheduled for deprecation Oct 16 2026.
GPT-5 excluded β pricing unconfirmed across sources. Pricing as of August 2026.
The table above doesn't include caching and it's just an estimate, please consider it as an indication!
P.S. Let me know if some of the numbers are wrong too, I am testing some new method to extract that data π
What The F Is This Jargon
You'll see these terms like MoE and parameters around everywhere, so let me explain them easily.
Parameters are roughly the "knowledge" stored in a model.
More parameters generally means more capability, but also more compute required to run each query, which means higher cost.
284 billion is a very large number.
Here's the twist: V4 Flash uses a Mixture of Experts (MoE) architecture.
β
Instead of activating all 284B parameters for every single token it processes, the model is split into many specialized sub-networks ("experts") and only a small fraction of them activate for any given input.
Think of it like a company where you have 284 specialists on payroll, but each customer call only routes to 2-3 of them depending on the topic.
The full team is there but most of them are dozing off (for now).
Post-training means the model is improved after it's trained at specific tasks.
Some Use Cases For You (Again)
Since it's dirty cheap and actually better than previous models, I urge you to reconsider some use cases.
It's important to remember that most of these tasks are handled manually today or with worse processes π
This is your chance to actually upskill and make the difference.
Content Audit/Research
Auditing is self-explanatory, instead of auditing content manually, you could:
pull the data from somewhere like BigQuery
define what to calculate
get some tables as output
LLM to interpret the results based on your guidelines
As usual, don't use MCPs or pull data straight from Google APIs like you read on LinkedIn (for serious projects).
Consider Dataform to be actually part of BQ but the idea stays. You store the data somewhere, then process it with automation tools and LLMs.
There is no data retention and the process will be a nightmare over time.
I am not a huge fan of writing content with AI BUT in the right hands, this can also work.
The issue is that the right hands aren't who you think π
Automated Reporting
This is the easiest yet more valuable use case.
Apart from dashboards, which I have discussed in my latest newsletter, normal reporting is needed.
You can create a process in a software like n8n and schedule it to send an email to the right person.
The actual part where AI is present would be in summarization.
Btw this was one of the first examples I showed in n8n. You just need to replace the trigger and you are done.
Chatting With Data?
You'll hear people say this unlocks "just ask your data a question in plain English."
And technically, yes, you can wire up a chat interface over your BigQuery data.
But look, the problem with conversational analytics isn't the model.
It's that most analytics data is badly structured, inconsistently named and missing the business context that makes a question meaningful.
And natural languages like the one you are reading are terribad at being clear.
Tell a machine you want a glass of water. β What's a glass? What's water? How is the water inside the glass? β Use code and that's a whole different story...
There are some instances where this can be good but ultimately not your life goal.
Cheaper But Not More Popular
Yes, in terms of output and cost, this is one if not the best model.
BUT... LLM usage isn't as high as you imagine.
At least, based on my purely personal evidence, most companies still ignore these topics.
The bar is quite low if you have some willingness to work on projects with LLMs.
I gave you even too many examples today... and these are already enough!
We continue next week, I got so much to do now!
π₯ Join Our Community
Our Discord community offers a small place where we can talk business and web data.
If you hate all the noise of social media, then this place is for you.
I will start posting more there as we have a forum channel now.
This is the best way to stay updated in real time on Seotistics:
The Seotistics newsletter is written by Marco Giordano, a Data/Web Analyst with the goal of combining business and web data. Tired of the usual boring Analytics content without any business impact? Seotistics teaches you how to use Analytics, web data and even content in your workflow while helping you with Strategy.
Read more from Seotistics - Web Analytics + Business + Strategy
Use Data Or Be Used By Data! The July 27 issue of Seotistics is here for you! Dashboards are everywhere and tbqh often overengineered for simple use cases. I've audited too many organizations having this issue here. So what can we do instead? The answers here are simple and feasible, you just need to know them! Please move this email to your Primary inbox or reply to it. This is to prevent Seotistics goes into spam by accident. Gmail users can read this tutorial to do it. Read this in your...
Use Data Or Be Used By Data! The July 20 issue of Seotistics is here for you! LLMs are everywhere and they have some interesting applications for our daily tasks in Analytics. Yeah, they won't make you lose your job as the news claim... but for sure you can't sit watching and sacrifice opportunities. Please move this email to your Primary inbox or reply to it. This is to prevent Seotistics goes into spam by accident. Gmail users can read this tutorial to do it. Read this in your browser π£π£...
Use Data Or Be Used By Data! The July 13 issue of Seotistics is here for you! Scraping is one of the most important skills you must have as an analyst AND marketer. But what changed after LLMs? Well, a lot! This issue is about YOU scraping websites to get data, not how LLM providers scrape your website. Please move this email to your Primary inbox or reply to it. This is to prevent Seotistics goes into spam by accident. Gmail users can read this tutorial to do it. Read this in your browser π£π£...