Model comparison

vs

GLM-5.3 vs Kimi K3.1 Pro

Zhipu AI against Moonshot AI, compared on listed price, real workload cost, context and capabilities.

The short answer

  • GLM-5.3 lists lower input and output prices.
  • Kimi K3.1 Pro supports vision; GLM-5.3 does not.
  • Kimi K3.1 Pro reads cached input for less ($0.2/M against GLM-5.3's $0.26/M), which decides the bill for agents that resend long context.

Side-by-side specs

SpecGLM-5.3Kimi K3.1 Pro
ProviderZhipu AIMoonshot AI
Input price$1.4/M$2/M
Output price$4.4/M$10/M
Cached input$0.26/M$0.2/M
Blended price (3:1)$2.15/M$4/M
Context window1M tokens1M tokens
Max output128k tokens—
InputTextText, Images
Structured outputYesYes
WeightsOpen (GLM-5.3 License (MIT-style; Model-as-a-Service operators above $10B revenue must pass a Z.AI security review))Closed
ReleasedAugust 14, 2026October 2026

Same work, both models

WorkloadGLM-5.3Kimi K3.1 Pro
One chat reply3,000 tokens in, 500 outKimi K3.1 Pro costs 1.7× as much as GLM-5.3$0.0064$0.01
Summarise a 100-page report50,000 tokens in, 1,500 out, one requestKimi K3.1 Pro costs 1.5× as much as GLM-5.3$0.08$0.12
1,000 classification calls500 tokens in, 20 out, per call$0.79$1.20
Coding-agent session2M tokens in across ~60 turns, 60,000 out, no cache$3.06$4.60
Same session, cached85% of input read from the prompt cache, the rest written to itKimi K3.1 Pro costs 1.4× as much as GLM-5.3$1.13$1.54

At listed rates. Token counts are typical sizes, not measurements.

GLM-5.3 pricing notes

  • Cache hits bill at $0.26 per 1M input tokens; there is no separate cache-write charge.

Kimi K3.1 Pro pricing notes

  • Not included in any plan: every request bills from credits.
  • Context caching is automatic: cache hits bill at $0.20 per 1M input tokens, a tenth of the input rate.
  • Thinking is always on, at max effort by default, and reasoning tokens bill at the output rate.

When to pick which

GLM-5.3

GLM-5.3 is Z.ai's open-weight, text-only reasoning model for coding and long-horizon tasks. It keeps GLM-5.2's base model and price, with its gains coming from post-training, and reads up to 1M tokens of context.

Pick it if

  • you want open weights for coding and long-horizon tasks
  • you work with long inputs: 1M context, cache hits at $0.26 per 1M
  • you need JSON structured output from a reasoning model

Skip it if

  • you need image input; GLM-5.3 Flash reads images for far less
  • you want answers without reasoning; thinking cannot be switched off

Kimi K3.1 Pro

The first model of Moonshot AI's K3.1 series and its most capable Kimi to date: a 3T-class reasoning model with native vision and a 1M-token context. It costs a third less per token than Kimi K3; Moonshot says full weights ship by November 4, 2026.

Pick it if

  • you want Moonshot's current flagship at a lower rate than Kimi K3
  • your prompts outgrow the 256K context of Kimi 2.6 and 2.7 Code
  • you resend long prompts, so cache hits bill at a tenth of input

Skip it if

  • you need temperature or top_p control: they are fixed
  • Kimi 2.6 or 2.7 Code covers your workload at a lower rate

FAQ

Which is cheaper, GLM-5.3 or Kimi K3.1 Pro?

GLM-5.3 lists lower input and output prices. On a coding-agent session (2M tokens in, 60,000 out) GLM-5.3 costs $3.06 and Kimi K3.1 Pro $4.60.

Which one accepts images?

Kimi K3.1 Pro supports vision; GLM-5.3 does not.

Can I switch between GLM-5.3 and Kimi K3.1 Pro mid-task?

Yes. In an Octomind session, /model octohub:glm-5.3 and /model octohub:kimi-k3.1-pro switch models without losing the conversation, so you can draft on one and review on the other.

Try either model

Switch between models inside the same Octomind session.

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Prices from the catalog of 2026-10-31