# Qwen 3.5 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 $0.60 for Qwen 3.5: 3.0x apart. Output is $0.60 against $3.60 (6.0x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Qwen 3.5 | Qwen3 235B A22B Instruct 2507 FP8 Throughput |
| --- | --- | --- |
| Lab | Qwen | Qwen |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 256K tokens |
| List price, input | $0.60 / M tokens | $0.20 / M tokens |
| List price, output | $3.60 / M tokens | $0.60 / M tokens |
| Cached input | $0.35 / M tokens | n/a |
| License | Apache 2.0 | 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 $1,980 on Qwen 3.5 at list: a gap of $1,530, or 4.4x.

## Choose Qwen 3.5 for

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

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

- The lower list price ($0.20 in / $0.60 out per M tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Qwen 3.5 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 $0.60/$3.60 for Qwen 3.5. Billed on Allocate: $0.21/$0.64 against $0.64/$3.85, list plus 7%.

### Which has the bigger context window?

They match: both read 262,144 tokens (256K) per request.

### Can I fine-tune Qwen 3.5 or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Both publish open weights (Qwen 3.5: Apache 2.0; 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/qwen-3-5-vs-qwen-qwen3-235b-a22b-instruct-2507-tput) · [Qwen 3.5](https://allocate.network/models/qwen-3-5.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)