AllocateDeepSeek R1 Distill Qwen 1.5B vs Meta Llama 3.1 8B Instruct Turbo
On provider list prices, DeepSeek R1 Distill Qwen 1.5B costs $0.18 per million input tokens against $0.18 for Meta Llama 3.1 8B Instruct Turbo: effectively level. Output is $0.18 against $0.18. On Allocate both bill at list plus the 7% transaction fee.
DeepSeek R1 Distill Qwen 1.5B
Meta Llama 3.1 8B Instruct TurboSpecifications 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 $279 on Meta Llama 3.1 8B Instruct Turbo at list: a gap of $0.
Choose DeepSeek R1 Distill Qwen 1.5B for
- Fine-tuning under a permissive license (MIT)
Choose Meta Llama 3.1 8B Instruct Turbo for
- Training toward a model you own
Common questions
Which is cheaper, DeepSeek R1 Distill Qwen 1.5B or Meta Llama 3.1 8B Instruct Turbo?
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 $0.18/$0.18 for Meta Llama 3.1 8B Instruct Turbo. Billed on Allocate: $0.19/$0.19 against $0.19/$0.19, list plus 7%.
Which has the bigger context window?
They match: both read 131,072 tokens (128K) per request.
Can I fine-tune DeepSeek R1 Distill Qwen 1.5B or Meta Llama 3.1 8B Instruct Turbo?
Both publish open weights (DeepSeek R1 Distill Qwen 1.5B: MIT; Meta Llama 3.1 8B Instruct Turbo: 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.