DeepSeek R1 Distill Llama 70B vs Qwen3 Coder 480B A35B Instruct Fp8

On provider list prices, DeepSeek R1 Distill Llama 70B costs $2 per million input tokens against $2 for Qwen3 Coder 480B A35B Instruct Fp8: effectively level. Output is $2 against $2. On Allocate both bill at list plus the 7% transaction fee.

DeepSeek R1 Distill Llama 70B

Qwen3 Coder 480B A35B Instruct Fp8

LabDeepSeekQwen

AccessOpen weightsOpen weights

Context window128K tokens256K tokens

List price, input$2 / M tokens$2 / M tokens

List price, output$2 / M tokens$2 / M tokens

Cached inputn/an/a

LicenseMITApache 2.0

Fine-tunableYesYes

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 $3,100 a month on DeepSeek R1 Distill Llama 70B and $3,100 on Qwen3 Coder 480B A35B Instruct Fp8 at list: a gap of $0.

Qwen3 Coder 480B A35B Instruct Fp8 reads 256K tokens per request against 128K for DeepSeek R1 Distill Llama 70B, 2.0x the window. That decides which one can take whole documents without splitting them.

DeepSeek R1 Distill Llama 70B$2$2

Qwen3 Coder 480B A35B Instruct Fp8$2$2

InputOutput

Choose DeepSeek R1 Distill Llama 70B for

Fine-tuning under a permissive license (MIT)

DeepSeek R1 Distill Llama 70B details

Choose Qwen3 Coder 480B A35B Instruct Fp8 for

The longer context window (256K vs 128K tokens)
Fine-tuning under a permissive license (Apache 2.0)

Qwen3 Coder 480B A35B Instruct Fp8 details

Common questions

Which is cheaper, DeepSeek R1 Distill Llama 70B or Qwen3 Coder 480B A35B Instruct Fp8?

DeepSeek R1 Distill Llama 70B, on this workload shape. At list prices it is $2/$2 per million tokens in and out against $2/$2 for Qwen3 Coder 480B A35B Instruct Fp8. Billed on Allocate: $2.14/$2.14 against $2.14/$2.14, list plus 7%.

Which has the bigger context window?

Qwen3 Coder 480B A35B Instruct Fp8: 262,144 tokens (256K) against 131,072 (128K) for DeepSeek R1 Distill Llama 70B.

Can I fine-tune DeepSeek R1 Distill Llama 70B or Qwen3 Coder 480B A35B Instruct Fp8?

Both publish open weights (DeepSeek R1 Distill Llama 70B: MIT; Qwen3 Coder 480B A35B Instruct Fp8: Apache 2.0), 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.

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