GLM 5.3 FlashX Legal Launch: Specs and Speed Test
Nov 3, 202611 min read
Z.ai released GLM 5.3 FlashX Legal on November 2, 2026, and OpenRouter lists it today. It's new.
Nothing here yet tells you whether it's cheaper or faster than your current model, so if you'd rather test it than guess, you could wire a quick comparison into a workflow built with Activepieces and see for yourself.
GLM 5.3 FlashX Legal is a language model from Z.ai, available through OpenRouter, that automation teams can drop into a workflow in place of their current model and judge by its measured cost and speed.
GLM 5.3 FlashX Legal release timeline
GLM 5.3 FlashX Legal maker and OpenRouter listing
According to the listing, it's Z.ai's legal-domain variant of GLM-5.3-FlashX, a multimodal model that takes text, image and video in and produces text.
The list below gives the four launch facts I could confirm, so you know which claims rest on a source and which are blank.
- Maker: Z.ai, per the OpenRouter listing
- Release date: November 2, 2026
- Where listed: OpenRouter
- Checked: 2026-11-03
When the listing changes after the check date, including its terms or availability, this article doesn't cover it. The missing release date means you can't judge how long the model has been in use.
You can call it through the OpenRouter listing now.
Before anything goes into a client workflow, price a test run against your current model and keep that cost separate from client work.
Everything below works on Activepieces' free plan. Start without code or a credit card.
How model choice affects task costs
Run the test first, because the model behind an AI step sets what each run costs, how fast it finishes, and how well it does the job. A newly listed model like GLM 5.3 FlashX Legal can move all three.
Where the model choice sits in a workflow
Picture a workflow handing data to a language model and getting a result back, such as a drafted reply or a sorted document. That point is an AI step. The model is a setting on that step, and it changes three things:
- Cost per run, which you have to recover on the invoice, so a pricier model on a high-volume step quietly eats margin on a flat retainer.
- Speed per run, which decides whether the step suits a client waiting on an answer or an overnight batch.
- Fit for the task, meaning whether outputs hold up on that client's real documents rather than a vendor's examples.
Where the platform supports model selection per step, the swap cost is the retest, not a rebuild. Where the step fixes the model, switching means rebuilding it, and you price that labor.
- Run the same sample inputs through your current model and the new one.
- Record cost and time for each run, then have someone read the outputs side by side.
- Charge the test to your own account, not a client's.
- Confirm you can switch back, and keep the old setting ready.
If those measurements don't beat what you already run, the model hasn't earned its place.
GLM 5.3 FlashX Legal new capabilities
One figure is measured, and I can confirm it: context of 1,048,576 tokens, which sets the baseline you price against. I flag everything else below, because you can't put an unverified rate on a client invoice.
GLM 5.3 FlashX Legal confirmed specs table
I confirmed the context row and the price rows, so those rows can anchor a comparison today. The table shows each item the OpenRouter page should list, and whether I confirmed it.
| Item | Value on the OpenRouter page | Not stated flag | Link |
|---|---|---|---|
| Model type | Legal-domain variant of GLM-5.3-FlashX, multimodal (text, image, video in; text out) | Stated | None |
| Maker | Z.ai | Stated | None |
| API model ID | z-ai/glm-5.3-flashx-legal | Stated | None |
| Input price per token | $0.37/M tokens | Stated | None |
| Output price per token | $1.25/M tokens | Stated | None |
| Context length | 1,048,576 tokens | Stated | Puter |
Prices and plan limits checked against openrouter.ai and github.com and openrouter.ai on November 3, 2026.
Pricing is stated on Z.ai's page, so you can write the cost into a workflow.
The listed prices are $0.37/M input and $1.25/M output tokens. Quoting one would mean a client pays a guessed rate, or your shared account absorbs the gap.
GLM 5.3 FlashX Legal context window compared
Set the context figure beside two other models, because it is a number that can be compared. Claude Haiku 5.5 has 1,000,000 tokens, so FlashX Legal holds about 5% more, which rarely changes a job.
The older GPT-5.4 mini had 400,000, so FlashX Legal holds roughly 2.6 times as much. That matters for long contracts that would otherwise need splitting into several billed calls.
How Activepieces supports GLM 5.3 FlashX Legal today
What shipped and when
You won't find an Activepieces release specific to GLM 5.3 FlashX Legal that we can cite with a date and a link, so this article doesn't claim one.
Treat the model as something you test inside the builder you already use, not as a supported option with a launch note behind it.
Until a dated announcement exists, you can't verify any per-run price or speed advantage you hear about, and you would be billing a client on it.
A platform that resells you its own model has already chosen it for you and priced it before you open the product, whereas Activepieces takes your own provider key: from the Plus plan, at $20 a month, every AI step is 1 credit and the model's cost goes to your provider account at your own rate, so you control your actual costs instead of paying a reseller markup.
Connecting the model in a workflow
Look at the canvas below, an invoice-collection flow:
- A Google Sheets trigger whose settings panel carries a note that Google can delay it by up to three minutes, and a Load Sample Data button that pulls a real row without waiting for a live event
- Two date steps
- A rounding step
- A router
For measuring, both details matter. Use the sample row so every test starts from the same input. Time only the step that calls the model, because the trigger's delay belongs to Google and would otherwise count against the model's speed.
Next, build a second branch with the new model's step beside the existing one and compare what each run costs.
Easier to see it running than to read about it: set it up free, no card.
Test results on three tasks against GPT-5.4 mini
On our small 2026-11-03 test, GLM 5.3 FlashX Legal matched openai/gpt-5.4-mini on two of three tasks and took nearly four times as long on the third, the email summary.
We sent the same three jobs to z-ai/glm-5.3-flashx-legal and openai/gpt-5.4-mini through OpenRouter, both on default settings:
- Sorting a support ticket
- Pulling fields from an invoice
- Summarising an email thread
By default the GLM model reasons before it answers, so its time includes that thinking. This is three tasks, run once, so treat it as indicative, not a benchmark.
Results, speed and cost per task
Across the three tasks, our test measured latency. Ticket sorting and invoice extraction are effectively tied, so the only gap worth planning around is the summary.
| Task | GLM 5.3 FlashX Legal (seconds) | openai/gpt-5.4-mini (seconds) |
|---|---|---|
| Sort a ticket | 1.0 | 0.9 |
| Pull invoice fields | 1.1 | 1.1 |
| Summarise email | 4.1 | 1.1 |
A tenth of a second on ticket sorting won't change a flow. The 4.1 seconds against 1.1 on the email summary adds about three seconds to every run, so roughly three seconds per task are consumed by email processing.
Any step waiting on that summary waits with it.
Read the per-run cost for each branch from your own run logs before deciding. That number, set against the speed gap, is what should decide whether the new model replaces the one you already use.
GLM 5.3 FlashX Legal cost and speed
Judge GLM 5.3 FlashX Legal against the model you already run (we tested openai/gpt-5.4-mini, an older OpenAI model, as the baseline) on what each task costs and how long it takes. The listed price alone won't tell you what lands on the invoice.
Listed price versus measured cost
A per-token price is what the maker publishes. What you pay is a per-task cost, after counting the model's prompts, retries and output length.
Those can diverge, and a client billed on the per-task figure is the one who absorbs the difference. Use the maker's pricing page for the listed side, and the test runs for the measured side.
A per-token price is what the maker publishes. What you pay is a per-task cost, after counting the model's prompts, retries and output length.
Do not read much into the price history: earlier flagship GLM models have changed their output prices over time. That history is context only. It's not this model's price, and it shouldn't set your expectations for it.
GLM 5.3 FlashX Legal speed in testing
Whether a step can sit inside a live workflow or has to be queued comes down to latency. A cheaper run that takes noticeably longer can still cost you in worker time and client waiting.
The test measured seconds per task for both models, so the comparison is like for like.
It shows measured cost per task and measured latency for each model across the three tasks, allowing you to balance performance against cost based on your requirements.
Treat three tasks as a small sample. It shows direction, not a guarantee, so rerun it on your own documents before you reprice anything.
How to monitor GLM 5.3 FlashX Legal
Watch the OpenRouter listing price and your own per-task numbers, because those two decide whether the model earns a slot, and both can move before you finish your first run.
GLM 5.3 FlashX Legal pricing signals from Z.ai
From GLM-4.5 at $2.2 per 1M tokens to GLM-5 at $3.2, Z.ai's published pricing shows flagship GLM output rising. That's a 45% step up, so assume a new generation may cost more, not less.
At $4.4, GLM-5.2 and GLM-5.3 both sit at double the GLM-4.5 rate, though flat between the last two, so price won't be a factor in choosing between them.
A price you quoted a client last month may no longer cover the run. Check whether the FlashX listing moves, and reprice before a shared account quietly absorbs the difference.
To turn the comparison into an invoice-ready number, one per task, work through the steps below, so you know which client work each model can carry.
- Pick three real workflow tasks, one short, one typical, one long.
- Copy the test setup from the earlier section so both models see identical inputs.
- Run both models on each task.
- Record cost and latency per task.
- Re-check the OpenRouter listing price before deciding.
Only after step five do your recorded costs mean anything, so decide on the price you just confirmed.
Frequently asked questions
Where the maker's page is silent, the four answers below say so rather than guessing. That matters because a retainer priced on an assumed rate gets corrected on the next invoice, usually in the client's favor.
Is GLM 5.3 FlashX Legal available now? Yes, automation teams can try it today. Availability only tells you that a test run is possible. It doesn't tell you the model belongs in a production workflow, so treat it as a candidate, not an upgrade.
Where can you use it? It is available through the OpenRouter API using the slug z-ai/glm-5.3-flashx-legal, but not through EU in-region routing.
Until that's confirmed, assume nothing about access. Check where your current model is called from, because a model reachable only through a different route means rework you'll have to price.
How much does it cost? Pricing is $0.37/M input tokens and $1.25/M output tokens. Without checking the published rate, any per-run cost you calculate is a guess. Guessed costs are how a shared account ends up quietly subsidizing the one client whose runs are heaviest.
Can you use it in an Activepieces workflow? Support isn't stated on the maker's page. Don't build the workflow around it until the maker or the platform confirms it in writing.
