gpt-oss-120b vs qwen3.8-flash
Cerebras against Alibaba Qwen, priced on the same four workloads with price book 2026-10-22.
On real workloads
| Workload | gpt-oss-120b per call | qwen3.8-flash per call | Monthly difference |
|---|---|---|---|
| Chat reply 1,500 input and 400 output tokens, 100,000 calls a month |
$0.000825 | $0.000413 | −$41.20 |
| RAG answer, cached context 2,000 fresh + 4,000 cached input, 500 output, 100,000 calls a month |
$0.00248 | $0.000599 | −$187.60 |
| Agent step 4,000 fresh + 8,000 cached input, 800 output, 250,000 calls a month |
$0.0048 | $0.0011 | −$924.00 |
| Batch summary 3,000 input and 300 output tokens via the Batch API, 1,000,000 calls a month |
— | — | — |
Monthly difference is qwen3.8-flash minus gpt-oss-120b. Batch rows use each provider's Batch API rates where published. Price both on your own workload.
Rates
| Model | Input | Output | Cache read | Cache write | Context |
|---|---|---|---|---|---|
| cerebras/gpt-oss-120b | $0.35 | $0.75 | — | — | 131.1K |
| qwen/qwen3.8-flash | $0.15 | $0.47 | $0.016 | $0.20 | 991.8K |
Price is one input. Before switching, run your own prompts through both and compare quality; then veqen diff prices the change at your production volume.