# Qwen3 235B A22B Instruct 2507 FP8 Throughput vs GLM 4.7 FP8

On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $0.45 for GLM 4.7 FP8: 2.3x apart. Output is $0.60 against $2 (3.3x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Qwen3 235B A22B Instruct 2507 FP8 Throughput | GLM 4.7 FP8 |
| --- | --- | --- |
| Lab | Qwen | Zai Org |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 198K tokens |
| List price, input | $0.20 / M tokens | $0.45 / M tokens |
| List price, output | $0.60 / M tokens | $2 / M tokens |
| Cached input | n/a | n/a |
| License | Apache 2.0 | MIT |
| 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 $1,240 on GLM 4.7 FP8 at list: a gap of $790, or 2.8x.

Qwen3 235B A22B Instruct 2507 FP8 Throughput reads 256K tokens per request against 198K for GLM 4.7 FP8, 1.3x the window. That decides which one can take whole documents without splitting them.

## 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 198K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Choose GLM 4.7 FP8 for

- Fine-tuning under a permissive license (MIT)

## Common questions

### Which is cheaper, Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 4.7 FP8?

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 $0.45/$2 for GLM 4.7 FP8. Billed on Allocate: $0.21/$0.64 against $0.48/$2.14, list plus 7%.

### Which has the bigger context window?

Qwen3 235B A22B Instruct 2507 FP8 Throughput: 262,144 tokens (256K) against 202,752 (198K) for GLM 4.7 FP8.

### Can I fine-tune Qwen3 235B A22B Instruct 2507 FP8 Throughput or GLM 4.7 FP8?

Both publish open weights (Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0; GLM 4.7 FP8: MIT), 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/qwen-qwen3-235b-a22b-instruct-2507-tput-vs-z-ai-glm-4-7) · [Qwen3 235B A22B Instruct 2507 FP8 Throughput](https://allocate.network/models/qwen-qwen3-235b-a22b-instruct-2507-tput.md) · [GLM 4.7 FP8](https://allocate.network/models/z-ai-glm-4-7.md) · [Machine-readable catalog](https://allocate.network/catalog.json)