Model comparison

Kimi K3 vs GLM 5.3: pricing, context and features compared

Kimi K3 costs $0.80 per million input tokens and $10 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Here is how they compare on specs, on what real workloads cost, and on which to pick for what, from prices updated daily.

Moonshot’s Kimi and Z.ai’s GLM are open-weight models from Chinese labs that developers compare for coding and agent work, alongside DeepSeek.

Kimi K3Moonshot AI

Input / 1M: $0.80
Output / 1M: $10
Context: 1,048,576
Max output: Not published

Open weights: this is the price of OpenRouter’s top-ranked host, which may differ from Moonshot AI’s own API price.

Full Kimi K3 pricing and specs

GLM 5.3Z.ai

Input / 1M: $1.40
Output / 1M: $4.40
Context: 1,048,576
Max output: Not published

Open weights: this is the price of OpenRouter’s top-ranked host, which may differ from Z.ai’s own API price.

Full GLM 5.3 pricing and specs

Verdict

Kimi K3 or GLM 5.3: which to pick

Kimi K3 is cheaper per input token (1.7×) and GLM 5.3 per output token (2.3×). On our data, Kimi K3 has the edge for very long prompts and non-text inputs, and GLM 5.3 for repeated prompts. For scale, a support chatbot handling 1,000 requests a day costs about $152.46 a month on Kimi K3 and $91.40 on GLM 5.3.

Lowest bill for typical workloadsEither: It depends on the shape of the work: Kimi K3 is cheaper for the document summariser; GLM 5.3 for the support chatbot and coding agent; they cost about the same for the RAG app.
Very long prompts (whole codebases, long documents)Pick Kimi K3: Both read about 1,048,576 tokens per request. One 830,000-token prompt with a 2,000-token reply costs $0.68 on Kimi K3 and $1.17 on GLM 5.3. Neither has a long-prompt surcharge in our data.
Long replies (reports, large code files)Either: Neither provider publishes a separate cap on reply length, so the context window is the only published limit.
Images, audio, video or files in the promptPick Kimi K3: Kimi K3 also accepts images and video, which GLM 5.3 doesn’t.
Repeated long prompts (prompt caching)Pick GLM 5.3: GLM 5.3 bills cached input at $0.26 per 1M (19% of its input price), against $0.55 per 1M (69% of its input price) for Kimi K3.
Overnight batch jobsEither: Neither has a batch price in our data, so jobs that can wait cost the same as real-time requests.
Self-hosting or choosing your own hostEither: Both have open weights, so you can run either yourself or buy it from several hosts; prices here are typical hosted prices.

Criteria use only list prices, published limits and the features each provider declares. We don’t rank answer quality; test both models on your own prompts before you commit.

Specs

Kimi K3 vs GLM 5.3 side by side

Specs of Kimi K3 and GLM 5.3; the better value on each row is marked
Spec	Kimi K3	GLM 5.3
Provider	Moonshot AI	Z.ai
Input / 1M tokens	$0.80 (better)	$1.40
Output / 1M tokens	$10	$4.40 (better)
Cached input / 1M	$0.55	$0.26 (better)
Batch in / out	No batch price	No batch price
Long-prompt price	None	None
Context window	1,048,576	1,048,576
Max output	Not published	Not published
Accepts	text, images, video	text
Image input	Yes (better)	No
Tool calling	Yes	Yes
Reasoning mode	Yes	Yes
Prompt caching	Yes	Yes
Structured output	Yes	Yes
Open weights	Yes	Yes
Knowledge cutoff	Not published	Not published
First listed	2026-07-16	2026-08-18

Prices in US dollars per million tokens. Highlighted: the lower price or larger limit where the difference is over 5%. Features are as declared by each provider’s API; “First listed” is when our data source first listed the model. Long-prompt prices apply to the whole request once the prompt passes the threshold.

Costs

What Kimi K3 and GLM 5.3 cost for real workloads

Workload	Tokens in / out	Requests/day	Kimi K3 / month	GLM 5.3 / month	Cheaper
Support chatbotCalculator: Kimi K3 · GLM 5.3	1,500 / 400	1,000	$152.4650% cached	$91.4050% cached	GLM 5.3 (1.7×)
RAG appCalculator: Kimi K3 · GLM 5.3	6,000 / 500	500	$144.4820% cached	$140.4020% cached	About the same
Coding agentCalculator: Kimi K3 · GLM 5.3	40,000 / 2,000	200	$267.6780% cached	$172.2880% cached	GLM 5.3 (1.6×)
Document summariserCalculator: Kimi K3 · GLM 5.3	8,000 / 600	300	$113.15no batch price	$126.29no batch price	Kimi K3 (1.1×)

Each row uses the same token counts for both models and list prices (for an open-weight model, the top-ranked host’s price on OpenRouter), with caching and batch discounts only where the provider publishes them. A month is 365 ÷ 12 days. The support chatbot reads 50% of its prompt from the cache. The RAG app reads 20% of its prompt from the cache. The coding agent reads 80% of its prompt from the cache. The document summariser runs as a batch job where a batch price exists. Open either model in the LLM cost calculator to change any number.

Tokenizers

Are their per-token prices comparable?

Not exactly. Each model splits text into tokens its own way, so the same prompt can be a different number of tokens on Kimi K3 and GLM 5.3, and the cheaper per-token price isn’t always the cheaper bill. Where a tokenizer isn’t public or measured we say so rather than guess.

Model	Tokenizer	Tokens per 1,000 words	Output per 1M words
Kimi K3	We haven’t measured Kimi K3’s tokenizer, so its count for a given text isn’t known here.
GLM 5.3	We haven’t measured GLM 5.3’s tokenizer, so its count for a given text isn’t known here.

English prose, measured on the Universal Declaration of Human Rights (2026-10-11) or taken from the provider’s published words-per-token figure. Code, JSON and other languages use more tokens per word. Count your own text in the token counter or convert with tokens to words.

FAQ

Kimi K3 vs GLM 5.3 questions

Is Kimi K3 cheaper than GLM 5.3?

Kimi K3 costs $0.80 per million input tokens and $10 per million output tokens; GLM 5.3 costs $1.40 and $4.40. For a support chatbot handling 1,000 requests a day, that is about $152.46 a month on Kimi K3 against $91.40 on GLM 5.3, so GLM 5.3 is 1.7× cheaper there. Try your own numbers in the LLM cost calculator.

Which has the bigger context window, Kimi K3 or GLM 5.3?

Their context windows are about the same size. Kimi K3 reads up to 1,048,576 tokens (roughly 786,432 English words) and publishes no separate cap on reply length. GLM 5.3 reads up to 1,048,576 tokens (roughly 786,432 English words) and publishes no separate cap on reply length. The window is shared between your prompt and the reply. Word counts use a rough 0.75 words per token; real ratios depend on the tokenizer.

Is Kimi K3 or GLM 5.3 better for coding?

We don’t publish benchmark scores, so this page can’t say which writes better code. What we can show is cost: a coding agent re-sending 40,000 tokens of context (80% cached) and writing 2,000 tokens, 200 times a day, costs about $267.67 a month on Kimi K3 and $172.28 on GLM 5.3. Both support tool calling. Test both on tasks from your own repository.

Can Kimi K3 and GLM 5.3 read images?

Kimi K3 accepts images and video as well as text. GLM 5.3 accepts text only. Only Kimi K3 can read screenshots, photos or scanned pages, so for image work the choice is made for you. This is what each provider declares for its API, not a measure of how well it works.

Are Kimi K3 and GLM 5.3 open source?

Both have open weights: you can download them, run them on your own hardware, or buy them from several hosting providers. The prices on this page are typical hosted prices, so shop around. Check each model’s licence for commercial use, and see the VRAM calculator for the hardware a model needs.

Do Kimi K3 and GLM 5.3 count tokens the same way?

Not necessarily. Each model splits text into tokens with its own tokenizer, so the same prompt can be a different number of tokens on each, and per-token prices aren’t directly comparable. We haven’t measured Kimi K3’s tokenizer, so its count for a given text isn’t known here. We haven’t measured GLM 5.3’s tokenizer, so its count for a given text isn’t known here. Count your own text with the token counter or convert with tokens to words.

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