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Comparisons

Meta Llama 3.1 405B Instruct vs Qwen3 235B A22B Instruct 2507 FP8 Throughput

On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 17.5x apart. Output is $0.60 against $3.50 (5.8x). On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3.1 405B Instruct Qwen3 235B A22B Instruct 2507 FP8 Throughput
LabMetaQwen
AccessOpen weightsOpen weights
Context window4K tokens256K tokens
List price, input$3.5 / M tokens$0.2 / M tokens
List price, output$3.5 / M tokens$0.6 / M tokens
Cached inputn/an/a
LicenseLlama communityApache 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 $450 a month on Qwen3 235B A22B Instruct 2507 FP8 Throughput and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $4,975, or 12.1x.

Qwen3 235B A22B Instruct 2507 FP8 Throughput reads 256K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 64.0x the window. That decides which one can take whole documents without splitting them.

Qwen3 235B A22B Instruct 2507 FP8 Throughput$0.20$0.60
Meta Llama 3.1 405B Instruct$3.50$3.50
InputOutput

Choose Meta Llama 3.1 405B Instruct for

  • Training toward a model you own
Meta Llama 3.1 405B Instruct details

Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for

  • The lower list price ($0.20 in / $0.60 out per M tokens)
  • The longer context window (256K vs 4K tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Qwen3 235B A22B Instruct 2507 FP8 Throughput details

Common questions

Which is cheaper, Meta Llama 3.1 405B Instruct or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Qwen3 235B A22B Instruct 2507 FP8 Throughput, on this workload shape. At list prices it is $0.20/$0.60 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.21/$0.64 against $3.75/$3.75, list plus 7%.

Which has the bigger context window?

Qwen3 235B A22B Instruct 2507 FP8 Throughput: 262,144 tokens (256K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

Can I fine-tune Meta Llama 3.1 405B Instruct or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

Meta Llama 3.1 405B Instruct vs Qwen3.5 397B A17bQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.5 397B A17bMeta Llama 3.1 405B Instruct vs Qwen3.7 MaxQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.7 MaxMeta Llama 3.1 405B Instruct vs Meta Llama 3.3 70B Instruct TurboQwen3 235B A22B Instruct 2507 FP8 Throughput vs Meta Llama 3.3 70B Instruct Turbo

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.1 405B InstructQwen3 235B A22B Instruct 2507 FP8 Throughput

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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