Google Vertex AI vs Meta Llama Pricing
Meta Llama is cheaper than Google Vertex AI at the entry level: Llama 3.2 3B Instruct costs $0.020 per 1M output tokens against $0.040 for Gemma 3n 4B — 2.0× the price. On current flagships, Google Vertex AI's Nano Banana 2 Lite costs $30.00/1M output against $4.00/1M for Meta Llama's Llama 3.1 405B (base). Prices are USD, current as of August 11, 2026.
Per-million-token pricing for Google Vertex AI and Meta Llama, with side-by-side flagship models, cheapest tiers, and context windows. Pricing data syncs weekly from a continuously-updated model catalog — last updated August 11, 2026.
Who wins on what
Cheapest input tokens
$0.02/1MGoogle Vertex AI
Gemma 3 4B — $0.02/1M input
Cheapest output tokens
$0.02/1MMeta Llama
Llama 3.2 3B Instruct — $0.02/1M output
Longest context window
2.0MGoogle Vertex AI
Gemini 3/3.1 (> 200k context) — 2.0M input tokens
Lowest average output cost
$0.67/1MMeta Llama
Provider-wide average across 22 models
Largest model catalog
59 modelsGoogle Vertex AI
More options to match cost vs capability
Most reasoning models
18 modelsGoogle Vertex AI
Models with dedicated reasoning / thinking support
Most vision models
18 modelsGoogle Vertex AI
Models that accept image input
Side-by-side
Google Vertex AI
Full Google Vertex AI pricing →Cheapest input
$0.017
Gemma 3 4B
Cheapest output
$0.040
Gemma 3n 4B
Longest context
2.0M
Gemini 3/3.1 (> 200k context)
Avg output / 1M
$9.45
Across catalog
Cheapest cached input
$0.025
Gemini 3.1 Flash Lite
| Model | In/1M | Out/1M | Ctx |
|---|---|---|---|
| Gemini 3.6 Flash VisionReasoningToolsCache | $1.50 | $7.50 | 1.0M |
| Gemini 3.5 Flash Lite VisionReasoningToolsCache | $0.300 | $2.50 | 1.0M |
| Nano Banana 2 Lite VisionReasoningTools | $0.250 | $30.00 | 66K |
| Gemini 3.5 Live Translate Preview | $3.50 | $21.00 | 16K |
| Gemini 3.5 Flash VisionReasoningToolsCache | $1.50 | $9.00 | 1.0M |
| Gemma 3n 4B | $0.020 | $0.040 | 33K |
Meta Llama
Full Meta Llama pricing →Cheapest input
$0.020
Llama 3.1 8B Instruct
Cheapest output
$0.020
Llama 3.2 3B Instruct
Longest context
1.0M
Llama 4 Maverick
Avg output / 1M
$0.674
Across catalog
| Model | In/1M | Out/1M | Ctx |
|---|---|---|---|
| Llama 3.1 405B (base) | $4.00 | $4.00 | 33K |
| Llama 3.1 405B Instruct | $3.50 | $3.50 | 131K |
| Llama 4 Maverick | $0.150 | $0.600 | 1.0M |
| Llama 3 70B Instruct | $0.300 | $0.400 | 8K |
| Llama 3.1 70B Instruct | $0.400 | $0.400 | 131K |
| Llama 3.2 3B Instruct | $0.020 | $0.020 | 131K |
All prices in USD per 1 million tokens. Showing top 6 models per provider, sorted by output cost.
Frequently asked questions
Is Google Vertex AI or Meta Llama cheaper?
Meta Llama has the cheaper entry point at $0.02/1M output (Llama 3.2 3B Instruct — $0.02/1M output). Provider-wide, Google Vertex AI averages $9.45/1M output against $0.674/1M for Meta Llama. Which is cheaper for you depends on which model tier your workload actually needs.
How much do Google Vertex AI and Meta Llama cost per 1M tokens?
Google Vertex AI starts at $0.040 per 1M output tokens (Gemma 3n 4B), with its current flagship Nano Banana 2 Lite at $30.00. Meta Llama starts at $0.020 (Llama 3.2 3B Instruct), with Llama 3.1 405B (base) at $4.00. Input tokens cost less than output on both.
Which has the larger context window, Google Vertex AI or Meta Llama?
Google Vertex AI — Gemini 3/3.1 (> 200k context) — 2.0M input tokens. For comparison, Google Vertex AI's largest is 2.0M tokens (Gemini 3/3.1 (> 200k context)) and Meta Llama's is 1.0M tokens (Llama 4 Maverick).
Which has more reasoning models, Google Vertex AI or Meta Llama?
Google Vertex AI lists 18 reasoning models and Meta Llama lists 0. Reasoning models bill their internal thinking as output tokens, so a reasoning call costs several times a standard completion of the same visible length — compare them on total tokens billed, not headline rate.
Should I switch from Google Vertex AI to Meta Llama to save money?
Only if the cheaper model still meets your quality bar. Token price is one input; the ones that decide your bill are prompt size, response length, retries and how much conversation history you resend each turn. Model the switch against your real traffic before committing. Prices here are current as of August 11, 2026.
Related comparisons
Run the numbers for your workload
Calcaas multiplies per-token costs by your real usage patterns — inputs, outputs, retries, and conversation history — across both providers in one model.