AllocateGPT-5.5 vs Meta Llama 3.3 70B Instruct Turbo
On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $5 for GPT-5.5: 4.8x apart. Output is $1.04 against $30 (28.8x). On Allocate both bill at list plus the 7% transaction fee.
GPT-5.5
Meta Llama 3.3 70B Instruct TurboSpecifications 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 $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $16,500 on GPT-5.5 at list: a gap of $14,888, or 10.2x.
GPT-5.5 reads 400K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 3.1x the window. That decides which one can take whole documents without splitting them.
Choose Meta Llama 3.3 70B Instruct Turbo for
- The lower list price ($1.04 in / $1.04 out per M tokens)
- Open weights you can fine-tune and own
Common questions
Which is cheaper, GPT-5.5 or Meta Llama 3.3 70B Instruct Turbo?
Meta Llama 3.3 70B Instruct Turbo, on this workload shape. At list prices it is $1.04/$1.04 per million tokens in and out against $5/$30 for GPT-5.5. Billed on Allocate: $1.11/$1.11 against $5.35/$32.10, list plus 7%.
Which has the bigger context window?
GPT-5.5: 400,000 tokens (400K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.
Can I fine-tune GPT-5.5 or Meta Llama 3.3 70B Instruct Turbo?
Meta Llama 3.3 70B Instruct Turbo publishes open weights (Llama community) and can be fine-tuned on your own data. GPT-5.5 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.