Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
Workload (per month)
DeepSeek: DeepSeek V4.1 Flash
Z.ai: GLM 5.3 (batch)
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$21.50
$73.75
DeepSeek: DeepSeek V4.1 Flash saves $52.25
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$12.40
$74.00
DeepSeek: DeepSeek V4.1 Flash saves $61.60
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$14.40
$78.00
DeepSeek: DeepSeek V4.1 Flash saves $63.60
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$7.60
$48.00
DeepSeek: DeepSeek V4.1 Flash saves $40.40
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$2.00
$24.50
DeepSeek: DeepSeek V4.1 Flash saves $22.50
Capabilities side by side
Capability
DeepSeek: DeepSeek V4.1 Flash
Z.ai: GLM 5.3 (batch)
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
✓ Yes
spend extra tokens thinking before answering hard problems
Image input
✓ Yes
—
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
—
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
—
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
✓ Yes
repeat a generation for tests and evals
Log-probabilities
✓ Yes
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
cost matters at scale — it is 68% cheaper on a typical 3:1 input/output mix ($21.50 vs $73.75 a month for 50,000 support chats)
you need image input — to read screenshots, charts and scanned pages; Z.ai: GLM 5.3 (batch) does not advertise it
you need log-probabilities — to read token confidence for classification and scoring; Z.ai: GLM 5.3 (batch) does not advertise it
you generate long outputs in one go — up to 944K tokens (about 707,800 words) per response
Choose Z.ai: GLM 5.3 (batch) if…
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Frequently asked questions
Is DeepSeek: DeepSeek V4.1 Flash better than Z.ai: GLM 5.3 (batch)?
DeepSeek: DeepSeek V4.1 Flash comes out ahead on the data we track: 4 advantages and none for Z.ai: GLM 5.3 (batch).
Which is cheaper, DeepSeek: DeepSeek V4.1 Flash or Z.ai: GLM 5.3 (batch)?
DeepSeek: DeepSeek V4.1 Flash is cheaper: $0.27 vs $0.84 per 1M tokens at a typical 3:1 input/output mix, 68% less. For 50,000 support conversations a month that is $21.50 against $73.75.
How much do DeepSeek: DeepSeek V4.1 Flash and Z.ai: GLM 5.3 (batch) cost per million tokens?
DeepSeek: DeepSeek V4.1 Flash costs $0.02 per 1M input tokens and $1.00 per 1M output tokens; Z.ai: GLM 5.3 (batch) costs $0.45 input and $2.00 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, DeepSeek: DeepSeek V4.1 Flash or Z.ai: GLM 5.3 (batch)?
Both take 1.0M tokens per request — roughly 1,573 pages of text.
Do DeepSeek: DeepSeek V4.1 Flash and Z.ai: GLM 5.3 (batch) support function calling and JSON output?
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...