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Comparisons

Meta Llama 3 8B Instruct Reference vs Ministral 3 14B Instruct 2512

On provider list prices, Meta Llama 3 8B Instruct Reference costs $0.20 per million input tokens against $0.20 for Ministral 3 14B Instruct 2512: effectively level. Output is $0.20 against $0.20. On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3 8B Instruct Reference Ministral 3 14B Instruct 2512
LabMetaMistralai
AccessOpen weightsOpen weights
Context window8K tokens256K tokens
List price, input$0.2 / M tokens$0.2 / M tokens
List price, output$0.2 / M tokens$0.2 / M tokens
Cached inputn/an/a
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 $310 a month on Meta Llama 3 8B Instruct Reference and $310 on Ministral 3 14B Instruct 2512 at list: a gap of $0.

Ministral 3 14B Instruct 2512 reads 256K tokens per request against 8K for Meta Llama 3 8B Instruct Reference, 32.0x the window. That decides which one can take whole documents without splitting them.

Meta Llama 3 8B Instruct Reference$0.20$0.20
Ministral 3 14B Instruct 2512$0.20$0.20
InputOutput

Choose Meta Llama 3 8B Instruct Reference for

  • Training toward a model you own
Meta Llama 3 8B Instruct Reference details

Choose Ministral 3 14B Instruct 2512 for

  • The longer context window (256K vs 8K tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Ministral 3 14B Instruct 2512 details

Common questions

Which is cheaper, Meta Llama 3 8B Instruct Reference or Ministral 3 14B Instruct 2512?

Meta Llama 3 8B Instruct Reference, on this workload shape. At list prices it is $0.20/$0.20 per million tokens in and out against $0.20/$0.20 for Ministral 3 14B Instruct 2512. Billed on Allocate: $0.21/$0.21 against $0.21/$0.21, list plus 7%.

Which has the bigger context window?

Ministral 3 14B Instruct 2512: 262,144 tokens (256K) against 8,192 (8K) for Meta Llama 3 8B Instruct Reference.

Can I fine-tune Meta Llama 3 8B Instruct Reference or Ministral 3 14B Instruct 2512?

Both publish open weights (Meta Llama 3 8B Instruct Reference: Llama community; Ministral 3 14B Instruct 2512: 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 8B Instruct Reference vs Llama Guard 4 12BMeta Llama 3 8B Instruct Reference vs Meta Llama 3.1 8BMeta Llama 3 8B Instruct Reference vs Meta Llama 3 8B InstructMinistral 3 14B Instruct 2512 vs Llama Guard 4 12BMeta Llama 3 8B Instruct Reference vs Meta Llama 3.1 8B Instruct TurboMeta Llama 3 8B Instruct Reference vs Mistral (7B) Instruct v0.1

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3 8B Instruct ReferenceMinistral 3 14B Instruct 2512

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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