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Comparisons

Meta Llama 3.1 405B Instruct vs Inkling

On provider list prices, Inkling costs $1.87 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 1.9x apart. Output is $4.68 against $3.50. On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3.1 405B Instruct Inkling
LabMetaThinking Machines
AccessOpen weightsOpen weights
Context window4K tokens1M tokens
List price, input$3.5 / M tokens$1.87 / M tokens
List price, output$3.5 / M tokens$4.68 / M tokens
Cached inputn/a$0.374 / M tokens
LicenseLlama communityApache 2.0
Fine-tunableYesYes

Specifications 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 $3,882 a month on Inkling and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $1,543, or 1.4x.

Inkling 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.

Inkling$1.87$4.68
Meta Llama 3.1 405B Instruct$3.50$3.50
InputOutput

Choose Meta Llama 3.1 405B Instruct for

  • Training toward a model you own
Meta Llama 3.1 405B Instruct details

Choose Inkling for

  • The lower list price ($1.87 in / $4.68 out per M tokens)
  • The longer context window (1M vs 4K tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Inkling details

Common questions

Which is cheaper, Meta Llama 3.1 405B Instruct or Inkling?

Inkling, on this workload shape. At list prices it is $1.87/$4.68 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $2.00/$5.01 against $3.75/$3.75, list plus 7%.

Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

Can I fine-tune Meta Llama 3.1 405B Instruct or Inkling?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; Inkling: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

Meta Llama 3.1 405B Instruct vs Meta Llama 3.3 70B Instruct TurboInkling vs Meta Llama 3.3 70B Instruct TurboMeta Llama 3.1 405B Instruct vs Llama 4 Scout Instruct (17Bx16E)Inkling vs Llama 4 Scout Instruct (17Bx16E)Meta Llama 3.1 405B Instruct vs GLM 5.2Inkling vs GLM 5.2

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.1 405B InstructInkling

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.

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