AllocateLlama 4 70B vs Inkling FP4
Llama 4 70B is not currently in the Allocate serving catalog, so this page lists no prices for it: every price on this site comes from the live catalog.
Llama 4 70B
Inkling FP4LabMetaThinking Machines
AccessNot served on AllocateOpen weights
Context windown/a512K tokens
List price, inputNot served$1 / M tokens
List price, outputNot served$4.05 / M tokens
Cached inputn/a$0.17 / M tokens
LicenseNot listedApache 2.0
Fine-tunableYesYes
Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.
Choose Llama 4 70B for
- First private fine-tunes
- Classification and extraction
- On-boundary deployments
Choose Inkling FP4 for
- Fine-tuning under a permissive license (Apache 2.0)
- Published cached-input pricing ($0.17 per M tokens)
Common questions
Which has the bigger context window?
Inkling FP4: 524,288 tokens (512K) against an unlisted window for Llama 4 70B.
Can I fine-tune Llama 4 70B or Inkling FP4?
Both publish open weights (Llama 4 70B: Not listed; Inkling FP4: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.
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.