# DeepSeek R1 Distill Qwen 1.5B vs Meta Llama 3 8B Instruct Reference

On provider list prices, DeepSeek R1 Distill Qwen 1.5B costs $0.18 per million input tokens against $0.20 for Meta Llama 3 8B Instruct Reference: 1.1x apart. Output is $0.18 against $0.20 (1.1x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 Distill Qwen 1.5B | Meta Llama 3 8B Instruct Reference |
| --- | --- | --- |
| Lab | DeepSeek | Meta |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 8K tokens |
| List price, input | $0.18 / M tokens | $0.20 / M tokens |
| List price, output | $0.18 / M tokens | $0.20 / M tokens |
| Cached input | n/a | n/a |
| License | MIT | Llama community |
| 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 $279 a month on DeepSeek R1 Distill Qwen 1.5B and $310 on Meta Llama 3 8B Instruct Reference at list: a gap of $31.

DeepSeek R1 Distill Qwen 1.5B reads 128K tokens per request against 8K for Meta Llama 3 8B Instruct Reference, 16.0x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek R1 Distill Qwen 1.5B for

- The lower list price ($0.18 in / $0.18 out per M tokens)
- The longer context window (128K vs 8K tokens)
- Fine-tuning under a permissive license (MIT)

## Choose Meta Llama 3 8B Instruct Reference for

- Training toward a model you own

## Common questions

### Which is cheaper, DeepSeek R1 Distill Qwen 1.5B or Meta Llama 3 8B Instruct Reference?

DeepSeek R1 Distill Qwen 1.5B, on this workload shape. At list prices it is $0.18/$0.18 per million tokens in and out against $0.20/$0.20 for Meta Llama 3 8B Instruct Reference. Billed on Allocate: $0.19/$0.19 against $0.21/$0.21, list plus 7%.

### Which has the bigger context window?

DeepSeek R1 Distill Qwen 1.5B: 131,072 tokens (128K) against 8,192 (8K) for Meta Llama 3 8B Instruct Reference.

### Can I fine-tune DeepSeek R1 Distill Qwen 1.5B or Meta Llama 3 8B Instruct Reference?

Both publish open weights (DeepSeek R1 Distill Qwen 1.5B: MIT; 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-r1-distill-qwen-1-5b-vs-meta-llama-3-8b-chat-hf) · [DeepSeek R1 Distill Qwen 1.5B](https://allocate.network/models/deepseek-deepseek-r1-distill-qwen-1-5b.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)