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Comparisons

Meta Llama 3.3 70B Instruct Turbo vs Qwen3-VL-32B-Instruct

On provider list prices, Qwen3-VL-32B-Instruct costs $0.50 per million input tokens against $1.04 for Meta Llama 3.3 70B Instruct Turbo: 2.1x apart. Output is $1.50 against $1.04. On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3.3 70B Instruct Turbo Qwen3-VL-32B-Instruct
LabMetaQwen
AccessOpen weightsOpen weights
Context window128K tokens256K tokens
List price, input$1.04 / M tokens$0.5 / M tokens
List price, output$1.04 / M tokens$1.5 / 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 $1,125 a month on Qwen3-VL-32B-Instruct and $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $487, or 1.4x.

Qwen3-VL-32B-Instruct 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.

Qwen3-VL-32B-Instruct$0.50$1.50
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 Qwen3-VL-32B-Instruct for

  • The lower list price ($0.50 in / $1.50 out per M tokens)
  • The longer context window (256K vs 128K tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Qwen3-VL-32B-Instruct details

Common questions

Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or Qwen3-VL-32B-Instruct?

Qwen3-VL-32B-Instruct, on this workload shape. At list prices it is $0.50/$1.50 per million tokens in and out against $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.54/$1.60 against $1.11/$1.11, list plus 7%.

Which has the bigger context window?

Qwen3-VL-32B-Instruct: 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 Qwen3-VL-32B-Instruct?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; Qwen3-VL-32B-Instruct: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

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Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.3 70B Instruct TurboQwen3-VL-32B-Instruct

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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