FREE GLM200K CONTEXT128K OUTPUTFREE TIERTEXT + CODE
GLM-4.7 Flash API Pricing
GLM-4.7 Flash is Zhipu's free GLM-4.7 series model for registered-user experimentation and high-frequency light workloads. The live vendor table lists $0/M input and $0/M output; access is free on the public Z.AI pricing page. Pulled directly from docs.z.ai daily.
Input - per 1M tokens
$0.00/M
Source Z.AI free
Output - per 1M tokens
$0.00/M
Context 200K free
Cached input - free
$0.00/M
Free per-token use free
Effective - agentic blend
$0.00/M
92/8 split - 82% cache
§ 01 / TERMINAL
Run the numbers.
Live calculator pre-loaded with current GLM-4.7 Flash rates. Tweak spend, output mix, or cache assumptions and share the URL to share the calculation.
$ /mo
Workload split
Prompt cache hit rate
Tokens you can process
—
Words equivalent (English)
—
Effective rate
—
§ 02 / SCENARIOS
Real-world presets.
CODING AGENT
Prototype implementation
$0.000/task
CODE REVIEW
Pull request review
$0.000/review
RAG
Knowledge base answer
$0.000/query
CHATBOT
Support assistant
$0.000/turn
§ 03 / TAPE
Price history.
Input · $0.00/M
Output · $0.00/M
Cached · $0.00/M
JAN 19 Launch-wave list price stored as free input and free outputMAY 19 Live verification kept free input and free output
§ 04 / TOKENIZER
Paste text. See tokens. See cost.
Estimate · zhipu-tokenizer-estimate · ≈3.85 chars/token Auto-counts as you type
This is a chars-per-token approximation, not a real tokenizer. Actual tokens vary by language, code density, and tool-call overhead — counts are typically ±10–20% off for English prose, more for code or non-Latin scripts. For exact billing, use the vendor's official tokenizer.
Characters —
Words —
Tokens (estimated) —
Cost as input · uncached —
Cost as output · uncached —
Cost as cached input —
| Model | Input /M | Output /M | Effective blended | Context | Best for |
|---|---|---|---|---|---|
| GLM-4.7 Flash Current | $0.00 cache $0.00 | $0.00 | $0.00 agentic 92/8 | 200K | Free registered-user tier |
| GLM-4.5 Flash | $0.00 cache $0.00 | $0.00 | $0.00 same blend | 128K | Free GLM-4.5 fallback |
| GLM-4.7 FlashX | $0.07 cache $0.01 | $0.40 | $0.05 pricier | 200K | Ultra-cheap GLM traffic |
| GLM-4.5 Air | $0.20 cache $0.03 | $1.10 | $0.14 pricier | 128K | Budget GLM-4.5 production |
| GLM-4.7 | $0.60 cache $0.11 | $2.20 | $0.36 pricier | 200K | Mid-tier GLM agents |
| DeepSeek V4 Flash | $0.14 cache $0.00 | $0.28 | $0.05 pricier | 1M | Budget reasoning and coding |
| Qwen3 Coder Flash | $0.30 | $1.50 | $0.40 pricier | 1M | Cheap code-focused Qwen |
Frequently asked.
Practical pricing questions, separated from calculator assumptions and regional tiers.
Q · 01 What is GLM-4.7 Flash priced at? +
GLM-4.7 Flash is listed as
$0/M input and $0/M output on the live Z.AI pricing table. This page stores USD per-million-token pricing, so the calculator treats the token charge as zero.Q · 02 How is the effective price calculated? +
AI//COST uses the same 92/8 agentic blend everywhere. With an 82% cache hit rate, GLM-4.7 Flash's effective blended cost is
$0/M.Q · 03 Is prompt caching priced separately? +
The Z.AI table lists cached input as free for GLM-4.7 Flash, matching the free input and output rows. For real deployments, verify account-level rate limits because a free token price does not guarantee unlimited throughput.
Q · 04 Are regional prices different? +
Z.AI publishes the official developer pricing page in USD. Chinese BigModel pages may surface overlapping model catalogs, but the quote tiles use the baseline row from the Z.AI developer pricing page, not a reseller or proxy price.
Q · 05 Is there a batch discount? +
No separate batch discount row is listed on the Z.AI pricing page for GLM-4.7 Flash. The quote tiles show real-time list pricing; batch economics should be treated as a separate calculator variant only when the vendor documents it.
Q · 06 How accurate is the tokenizer estimate? +
The browser widget uses a
zhipu-tokenizer-estimate chars-per-token estimate for English text. It is useful for rough planning, but actual billing comes from the vendor API usage fields and can differ for Chinese, code, or mixed-language prompts.