# Meta Llama 3.3 70B Instruct Turbo 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 $1.04 for Meta Llama 3.3 70B Instruct Turbo: 5.2x apart. Output is $0.60 against $1.04 (1.7x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | Qwen3 235B A22B Instruct 2507 FP8 Throughput |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.04 / M tokens | $0.20 / M tokens |
| List price, output | $1.04 / M tokens | $0.60 / M tokens |
| Cached input | n/a | n/a |
| 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 $450 a month on Qwen3 235B A22B Instruct 2507 FP8 Throughput and $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $1,162, or 3.6x.

Qwen3 235B A22B Instruct 2507 FP8 Throughput 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 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 128K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo 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 $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.21/$0.64 against $1.11/$1.11, list plus 7%.

### Which has the bigger context window?

Qwen3 235B A22B Instruct 2507 FP8 Throughput: 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 235B A22B Instruct 2507 FP8 Throughput?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: 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.

---

[HTML page](https://allocate.network/compare/meta-llama-3-3-70b-instruct-turbo-vs-qwen-qwen3-235b-a22b-instruct-2507-tput) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Qwen3 235B A22B Instruct 2507 FP8 Throughput](https://allocate.network/models/qwen-qwen3-235b-a22b-instruct-2507-tput.md) · [Machine-readable catalog](https://allocate.network/catalog.json)