171 models came out in 2026 before October, and 26 of them arrived in September alone. Nobody can test them all, so here are the trackers worth saving and an easy system for choosing which releases deserve your time.
Just look at the final ten days of September 2026. xAI released Grok 4.7. Anthropic released Claude Opus 5.5, followed by Sonnet 5.5 a few days later. OpenAI rolled out new GPT-6 Sol variants. Every one of them came with charts claiming it was the best so far.
I'd barely finished one launch post before the next one started trending.
If it feels impossible to keep up, that's because it nearly is. LLM Gateway's release timeline now counts 416 models from 54 providers since 2022. 171 of them came out in 2026, and 26 arrived in September alone, from 16 separate providers.
No one can test all of that, including you, me, and the people sharing benchmark charts on launch day. So this post does two jobs: it shows you the AI model release trackers worth saving, and it hands you an easy filter for spotting which releases matter to your work.
Why keeping up got so hard
Three years ago, a big model launch was an event. Today it's more like the weather.
Here's why the pace took off:
More labs. On top of the big US names, there's Qwen, DeepSeek, Moonshot's Kimi, Z.ai's GLM, Xiaomi's MiMo, Mistral, and plenty more, each releasing on its own timeline.
More sizes. A single "release" is often a whole family: a flagship, a mid-range model, a quick cheap model, and sometimes a coding version.
Point releases. Labs now release 5.5, 5.6, and 5.7 rather than waiting for a big version jump.
Price changes count too. When a model gets 80% cheaper, as OpenAI's GPT-5.6 Luna did on July 30, that can matter more than a brand-new model.
Forget knowing every model. Know the three that matter for your work, and notice when one of them gets replaced.
The trackers worth bookmarking
These are the free trackers I'd use, depending on what you want to follow.
LLM Gateway timeline: the biggest list
LLM Gateway covers 416 models from 54 providers and 33 model families, with release counts for every month. It's the quickest way to see "what came out this month?"
Use it for: a big-picture view of the entire market.
Evertune: the clean summary
Evertune's AI Model Release Tracker is more focused, with about 133 releases and updates from six major providers (OpenAI, Anthropic, Google, Meta, DeepSeek, and others). Each entry has a date, a plain-English summary, and a link to the official announcement, and it's updated every day.
Use it for: getting a quick, hype-free read on what a major release changed.
Opper, BenchLM and LMMarketCap: quick checks
Opper's release tracker, BenchLM's live updates, and LMMarketCap's tracker are handy for checking the past 24 to 48 hours, with benchmark or pricing context shown beside each release.
Use it for: "did something big drop today?"
Pricing trackers: the underrated one
Releases grab the headlines, but changes to price and limits are what hit your budget. UsagePricing's activity log records dated pricing changes across AI and SaaS vendors.
Use it for: spotting cost changes before your invoice shows them.
The three-question filter
This is the filter I use to decide whether a new release gets any of my time. It takes around 30 seconds.
Is it from a provider I already use, or a direct replacement for a model I use? If not, move on unless the next two answers are a strong yes.
Does it change price, speed, or limits by at least 20%? **Tiny benchmark gains rarely matter for real work. Big shifts in cost or speed do.
**Does it unlock something I couldn't do before? That could be longer context, a new modality, stronger tool use, or a much higher coding score on tasks like mine.
If all three answers are no, I close the tab. That one rule cuts out most of the noise. For most people, only a few releases each month will get through.
Benchmarks show how a model did on someone else's test. Your own five-task test shows how it'll do for you.
Build your own five-task test
When a release gets through the filter, skip the launch-day charts and run your own quick test. Here's how to build one once and reuse it forever.
Pick five real tasks from your work. Mine include turning a messy HTML page into JSON, writing a Python function for pagination, summarizing a client brief, cleaning a CSV with mixed date formats, and drafting a short reply to a client.
Save the exact inputs. Use the same inputs every time so the comparison stays fair.
Save your current best outputs **to use as your baseline.
**Score simply: better, same, or worse, along with cost and time.
Keep a log. Give each model a row and each task a column.
After a few rounds, your log will be more useful than any public leaderboard, since it's based on your work.
A weekly routine that takes 15 minutes
Here's the routine I'd recommend for anyone who builds with AI but doesn't want keeping up to turn into a second job.
Monday, 5 minutes: scan one big tracker for last week's releases and run each through the three-question filter.
Monday, 5 minutes: look at a pricing tracker for changes to the models you pay for.
Once a month, 30 minutes: run your five-task test on any release that got through the filter.
Once a quarter: go over which models you pay for and whether a cheaper one can now do the same job.
That's all. You won't know everything, but you'll know what matters.
Keep a "my models" sheet
Public trackers cover the entire market. You also need a small tracker for your market, meaning the models you actually use. Mine is a basic sheet with one row for each model and these columns:
Model name and provider
What I use it for (scraping parsers, writing, coding, summaries)
Current price per million input and output tokens
Where I use it (API, a subscription, a tool like Cursor)
Known deprecation or retirement date
The date I last tested it
The cheaper model I'd switch to if prices rose
Setting it up takes 20 minutes. From then on, each release faces one easy question: does it beat anything in my sheet? And when a provider announces a retirement, like Moonshot ending its Kimi K2.5 and moonshot-v1 API family on August 31, you'll know right away whether it affects you.
Open models deserve a separate glance
Many of those 171 releases in 2026 are open-weight models from labs such as Qwen, DeepSeek, Z.ai, Moonshot, and Mistral. If you only use chat apps, it's easy to overlook them. I'd still check them briefly each month, for two reasons.
First, price. Open models hosted by inference providers are often far cheaper for bulk jobs like classification, extraction, and cleanup. In scraping pipelines that handle thousands of pages, the savings add up quickly.
Second, control. You can run many of them on your own hardware, fine-tune them, and keep data inside your own environment. For some clients, that's the deciding factor.
There's no need to track every open release. Just keep one open model in your five-task test so you always know what the budget option can handle.
Signals that a release matters
Over time, I've spotted a few signs that separate real changes from noise:
Tool builders switch fast. When coding tools and API gateways add a model within 48 hours and set it as a default, take notice.
The price drops at the same quality. Opus 5.5 launching at $4 input and $20 output per million tokens, down from Opus 5's $5 and $25, is the sort of change that moves real budgets.
Old models get deprecated. Once your current model gets a retirement date, you have to pay attention, like it or not.
Developers you trust actually change their stack, beyond just tweeting about it.
What to ignore
Launch-day leaderboard screenshots that skip the methodology
"This changes everything" posts within the first six hours
Benchmarks on tasks that look nothing like what you do
Small version updates from providers you don't use
Call it self-defense. It's the only way to stay sane when a new model comes out almost every day.
You don't need to see everything
An AI model release tracker can't help you keep up with 400+ models, and nothing else can either. What it can do is give you a fast, reliable look at what changed, so you can run a simple filter and move on.
Save one big tracker and one pricing tracker. Build your five-task test. Give it 15 minutes a week.
You'll miss lots of releases. That's fine. You'll catch the ones that matter for your work, and that's the only kind of keeping up that pays off.
Enjoyed this read? Be sure to follow me *for more stories that make people feel things. You can also connect with me on *LinkedIn or Telegram @kawsarlog for more updates on my work.
I do freelance scraping projects, so if you need some data collected feel free to contact me on LinkedIn or email at hi@kawsarlog.com
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