# Llama Guard 4 12B vs Mistral (7B) Instruct v0.3

On provider list prices, Llama Guard 4 12B costs $0.20 per million input tokens against $0.20 for Mistral (7B) Instruct v0.3: effectively level. Output is $0.20 against $0.20. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Llama Guard 4 12B | Mistral (7B) Instruct v0.3 |
| --- | --- | --- |
| Lab | Meta | mistralai |
| Access | Open weights | Open weights |
| Context window | 1M tokens | 32K tokens |
| List price, input | $0.20 / M tokens | $0.20 / M tokens |
| List price, output | $0.20 / M tokens | $0.20 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Apache 2.0 |
| Fine-tunable | Yes | Yes |

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 Llama Guard 4 12B and $310 on Mistral (7B) Instruct v0.3 at list: a gap of $0.

Llama Guard 4 12B reads 1M tokens per request against 32K for Mistral (7B) Instruct v0.3, 32.0x the window. That decides which one can take whole documents without splitting them.

## Choose Llama Guard 4 12B for

- The longer context window (1M vs 32K tokens)

## Choose Mistral (7B) Instruct v0.3 for

- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Llama Guard 4 12B or Mistral (7B) Instruct v0.3?

Llama Guard 4 12B, 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 Mistral (7B) Instruct v0.3. Billed on Allocate: $0.21/$0.21 against $0.21/$0.21, list plus 7%.

### Which has the bigger context window?

Llama Guard 4 12B: 1,048,576 tokens (1M) against 32,768 (32K) for Mistral (7B) Instruct v0.3.

### Can I fine-tune Llama Guard 4 12B or Mistral (7B) Instruct v0.3?

Both publish open weights (Llama Guard 4 12B: Llama community; Mistral (7B) Instruct v0.3: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/meta-llama-guard-4-12b-vs-mistral-mistral-7b-instruct-v0-3) · [Llama Guard 4 12B](https://allocate.network/models/meta-llama-guard-4-12b.md) · [Mistral (7B) Instruct v0.3](https://allocate.network/models/mistral-mistral-7b-instruct-v0-3.md) · [Machine-readable catalog](https://allocate.network/catalog.json)