AllocateDeepSeek 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.
DeepSeek R1 Distill Qwen 1.5B
Meta Llama 3 8B Instruct ReferenceSpecifications 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.
Related comparisons
Run the numbers on your workload
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.