# 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.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | Qwen 3.5 |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.04 / M tokens | $0.60 / M tokens |
| List price, output | $1.04 / M tokens | $3.60 / M tokens |
| Cached input | n/a | $0.35 / M tokens |
| License | Llama community | Apache 2.0 |
| Fine-tunable | Yes | Yes |

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.

## Choose Meta Llama 3.3 70B Instruct Turbo for

- Training toward a model you own

## Choose Qwen 3.5 for

- Multilingual support agents
- Translation-adjacent workflows
- Fine-tuning under Apache 2.0

## 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.

---

[HTML page](https://allocate.network/compare/meta-llama-3-3-70b-instruct-turbo-vs-qwen-3-5) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Qwen 3.5](https://allocate.network/models/qwen-3-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)