# Llama 4 70B vs Qwen3 235B A22B Instruct 2507 FP8 Throughput

Llama 4 70B is not currently in the Allocate serving catalog, so this page lists no prices for it: every price on this site comes from the live catalog.

## Specifications

| | Llama 4 70B | Qwen3 235B A22B Instruct 2507 FP8 Throughput |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Not served on Allocate | Open weights |
| Context window | n/a | 256K tokens |
| List price, input | Not served | $0.20 / M tokens |
| List price, output | Not served | $0.60 / M tokens |
| Cached input | n/a | n/a |
| License | Not listed | 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.

## Choose Llama 4 70B for

- First private fine-tunes
- Classification and extraction
- On-boundary deployments

## Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for

- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which has the bigger context window?

Qwen3 235B A22B Instruct 2507 FP8 Throughput: 262,144 tokens (256K) against an unlisted window for Llama 4 70B.

### Can I fine-tune Llama 4 70B or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Both publish open weights (Llama 4 70B: Not listed; 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.

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[HTML page](https://allocate.network/compare/llama-4-70b-vs-qwen-qwen3-235b-a22b-instruct-2507-tput) · [Llama 4 70B](https://allocate.network/models/llama-4-70b.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)