# DeepSeek V4 Flash vs Meta Llama 3 8B Instruct Reference

On provider list prices, Meta Llama 3 8B Instruct Reference costs $0.20 per million input tokens against $0.14 for DeepSeek V4 Flash: effectively level. Output is $0.20 against $0.28 (1.4x).

## Specifications

| | DeepSeek V4 Flash | Meta Llama 3 8B Instruct Reference |
| --- | --- | --- |
| Lab | Deepseek | Meta |
| Access | Open weights | Open weights |
| Context window | 1M tokens | 8K tokens |
| List price, input | $0.14 / M tokens | $0.20 / M tokens |
| List price, output | $0.28 / M tokens | $0.20 / M tokens |
| Cached input | $0.028 / M tokens | n/a |
| License | Not listed | Llama community |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $266 a month on DeepSeek V4 Flash and $310 on Meta Llama 3 8B Instruct Reference at list: a gap of $44.00, or 1.2x.

DeepSeek V4 Flash reads 1M tokens per request against 8K for Meta Llama 3 8B Instruct Reference, 128.0x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek V4 Flash for

- The lower list price ($0.14 in / $0.28 out per M tokens)
- The longer context window (1M vs 8K tokens)
- Published cached-input pricing ($0.028 per M tokens)

## Choose Meta Llama 3 8B Instruct Reference for

- Training toward a model you own

## Common questions

### Which is cheaper, DeepSeek V4 Flash or Meta Llama 3 8B Instruct Reference?

DeepSeek V4 Flash, on this workload shape. At list prices it is $0.14/$0.28 per million tokens in and out against $0.20/$0.20 for Meta Llama 3 8B Instruct Reference. Billed on Allocate: $0.15/$0.30 against $0.21/$0.21.

### Which has the bigger context window?

DeepSeek V4 Flash: 1,048,576 tokens (1M) against 8,192 (8K) for Meta Llama 3 8B Instruct Reference.

### Can I fine-tune DeepSeek V4 Flash or Meta Llama 3 8B Instruct Reference?

Both publish open weights (DeepSeek V4 Flash: Not listed; Meta Llama 3 8B Instruct Reference: Llama community), 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/deepseek-deepseek-v4-flash-vs-meta-llama-3-8b-chat-hf) · [DeepSeek V4 Flash](https://allocate.network/models/deepseek-deepseek-v4-flash.md) · [Meta Llama 3 8B Instruct Reference](https://allocate.network/models/meta-llama-3-8b-chat-hf.md) · [Machine-readable catalog](https://allocate.network/catalog.json)