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Comparisons

Meta Llama 3.3 70B Instruct Turbo vs Qwen 3.5

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.60 for Qwen 3.5: effectively level. Output is $1.04 against $3.60 (3.5x). On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3.3 70B Instruct Turbo Qwen 3.5
LabMetaQwen
AccessOpen weightsOpen weights
Context window128K tokens256K tokens
List price, input$1.04 / M tokens$0.6 / M tokens
List price, output$1.04 / M tokens$3.6 / M tokens
Cached inputn/a$0.35 / M tokens
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 $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $1,980 on Qwen 3.5 at list: a gap of $368, or 1.2x.

Qwen 3.5 reads 256K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 2.0x the window. That decides which one can take whole documents without splitting them.

Qwen 3.5$0.60$3.60
Meta Llama 3.3 70B Instruct Turbo$1.04$1.04
InputOutput

Choose Meta Llama 3.3 70B Instruct Turbo for

  • Training toward a model you own
Meta Llama 3.3 70B Instruct Turbo details

Choose Qwen 3.5 for

  • Multilingual support agents
  • Translation-adjacent workflows
  • Fine-tuning under Apache 2.0
Qwen 3.5 details

Common questions

Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or Qwen 3.5?

Meta Llama 3.3 70B Instruct Turbo, on this workload shape. At list prices it is $1.04/$1.04 per million tokens in and out against $0.60/$3.60 for Qwen 3.5. Billed on Allocate: $1.11/$1.11 against $0.64/$3.85, list plus 7%.

Which has the bigger context window?

Qwen 3.5: 262,144 tokens (256K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or Qwen 3.5?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; Qwen 3.5: 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 3.3 70B Instruct Turbo vs Qwen3-VL-32B-InstructMeta Llama 3.3 70B Instruct Turbo vs Qwen3.7 MaxQwen 3.5 vs Qwen3.7 MaxMeta Llama 3.3 70B Instruct Turbo vs Qwen3 235B A22B Instruct 2507 FP8 ThroughputQwen 3.5 vs Qwen3 235B A22B Instruct 2507 FP8 ThroughputMeta Llama 3.3 70B Instruct Turbo vs Llama 4 Scout Instruct (17Bx16E)

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.3 70B Instruct TurboQwen 3.5

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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