# Qwen3 235B A22B Instruct 2507 FP8 Throughput vs GLM 5.2

On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $1.40 for GLM 5.2: 7.0x apart. Output is $0.60 against $4.40 (7.3x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Qwen3 235B A22B Instruct 2507 FP8 Throughput | GLM 5.2 |
| --- | --- | --- |
| Lab | Qwen | Zai Org |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 256K tokens |
| List price, input | $0.20 / M tokens | $1.40 / M tokens |
| List price, output | $0.60 / M tokens | $4.40 / M tokens |
| Cached input | n/a | $0.26 / M tokens |
| License | Apache 2.0 | Not listed |
| 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 $3,220 on GLM 5.2 at list: a gap of $2,770, or 7.2x.

## 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)

## Choose GLM 5.2 for

- Agents on open weights
- Code and structured outputs
- Fine-tuning toward an owned model

## Common questions

### Which is cheaper, Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 5.2?

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 $1.40/$4.40 for GLM 5.2. Billed on Allocate: $0.21/$0.64 against $1.50/$4.71, list plus 7%.

### Which has the bigger context window?

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

### Can I fine-tune Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 5.2?

Both publish open weights (Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0; GLM 5.2: Not listed), 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-qwen3-235b-a22b-instruct-2507-tput-vs-z-ai-glm-5-2) · [Qwen3 235B A22B Instruct 2507 FP8 Throughput](https://allocate.network/models/qwen-qwen3-235b-a22b-instruct-2507-tput.md) · [GLM 5.2](https://allocate.network/models/z-ai-glm-5-2.md) · [Machine-readable catalog](https://allocate.network/catalog.json)