AllocateGemini 3.1 Pro vs Meta Llama 3.1 405B Instruct
On provider list prices, Meta Llama 3.1 405B Instruct costs $3.50 per million input tokens against $2 for Gemini 3.1 Pro: effectively level. Output is $3.50 against $12 (3.4x). On Allocate both bill at list plus the 7% transaction fee.
Gemini 3.1 Pro
Meta Llama 3.1 405B InstructSpecifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.
What the numbers say
Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $5,425 a month on Meta Llama 3.1 405B Instruct and $6,600 on Gemini 3.1 Pro at list: a gap of $1,175, or 1.2x.
Gemini 3.1 Pro reads 1M tokens per request against 4K for Meta Llama 3.1 405B Instruct, 244.1x the window. That decides which one can take whole documents without splitting them.
Choose Gemini 3.1 Pro for
- Judgment-heavy workflows
- Long-context analysis
- Escalation tier above Flash
Choose Meta Llama 3.1 405B Instruct for
- Open weights you can fine-tune and own
Common questions
Which is cheaper, Gemini 3.1 Pro or Meta Llama 3.1 405B Instruct?
Meta Llama 3.1 405B Instruct, on this workload shape. At list prices it is $3.50/$3.50 per million tokens in and out against $2/$12 for Gemini 3.1 Pro. Billed on Allocate: $3.75/$3.75 against $2.14/$12.84, list plus 7%.
Which has the bigger context window?
Gemini 3.1 Pro: 1,000,000 tokens (1M) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.
Can I fine-tune Gemini 3.1 Pro or Meta Llama 3.1 405B Instruct?
Meta Llama 3.1 405B Instruct publishes open weights (Llama community) and can be fine-tuned on your own data. Gemini 3.1 Pro is a closed model served over API; its weights are not available.
Related comparisons
Run the numbers on your workload
Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.