L LLM Cloud Hub
Side-by-side comparison

Llama 3.3 70B Instruct (free) vs GLM 4.5 Air (free)

Meta

Llama 3.3 70B Instruct (free)

πŸ”§ Tools
Input / 1M
$0.0000
Output / 1M
$0.0000
View Llama 3.3 70B Instruct (free) β†’
Z.ai

GLM 4.5 Air (free)

πŸ”§ Tools
Input / 1M
$0.0000
Output / 1M
$0.0000
View GLM 4.5 Air (free) β†’
Llama 3.3 70B Instruct (free)GLM 4.5 Air (free)
Provider Meta Z.ai
Context window Maximum tokens (input + output) the model can process in a single request. Glossary β†’ 65,536 131,072
Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). tools tools
Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary β†’ 0.0000 0.0000
Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5Γ— pricier than input. Glossary β†’ 0.0000 0.0000

Frequently asked questions

Which is cheaper, Llama 3.3 70B Instruct (free) or GLM 4.5 Air (free)?

Llama 3.3 70B Instruct (free) is cheaper than GLM 4.5 Air (free) on a 50/50 input/output blend by about $0 per 1M tokens. Exact savings depend on your input-vs-output ratio β€” use the cost calculator on this page for a workload-specific estimate.

Which has a larger context window, Llama 3.3 70B Instruct (free) or GLM 4.5 Air (free)?

GLM 4.5 Air (free) has the larger context window at 131k tokens versus 66k tokens for Llama 3.3 70B Instruct (free). That means GLM 4.5 Air (free) can ingest about 2.0x as much text per request.

What is the difference between Llama 3.3 70B Instruct (free) and GLM 4.5 Air (free)?

Llama 3.3 70B Instruct (free) comes from Meta; GLM 4.5 Air (free) comes from Z.ai. They differ in pricing, context window, and supported capabilities β€” see the side-by-side table on this page for the exact figures, refreshed nightly.

Keyboard shortcuts

?
Show this overlay
/
Focus the first form field
g h
Go to / (home)
g b
Go to /best-llm-for
g c
Go to /cost
g s
Go to /self-hosted
g x
Go to /compliance
Esc
Close any overlay

Inspired by Linear and GitHub conventions. The two-key sequences (g then h) work within ~1 second.