GPT-OSS 20B vs Qwen3.8 Flash
Two open-weight chat models on the same endpoint and API key, so switching between them is a one-word change. Below: what each costs for three realistic workloads, how they differ, and measured speed.
Inference APIs prices are read live from the price list.
- Cost: GPT-OSS 20B is cheaper in all three workloads, by 63–71%.
Side by side
| GPT-OSS 20B | Qwen3.8 Flash | |
|---|---|---|
| Served by | Inference APIs | Inference APIs |
| Model author | OpenAI | Alibaba Qwen |
| Model id | openai/gpt-oss-20b | Qwen/Qwen3.8-Flash |
| Input price | $0.04 / 1M tokens | $0.15 / 1M tokens |
| Output price | $0.19 / 1M tokens | $0.50 / 1M tokens |
| Context window | 131K tokens | 1M tokens |
| Capabilities | Chat, Reasoning, Tool calling, JSON mode | Chat, Reasoning, Tool calling, JSON mode, Cached input |
| Weights | Open | Open |
| Status | Available | Available |
| Limits | No per-minute or per-day request or token caps; usage draws on a prepaid balance | No per-minute or per-day request or token caps; usage draws on a prepaid balance |
What three workloads cost
List prices applied to the same work. The cheaper side of each row is in bold.
| Workload | GPT-OSS 20B | Qwen3.8 Flash | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $0.89 | $2.70 | 67% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $4.15 | $14.50 | 71% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $4.00 | $10.75 | 63% |
GPT-OSS 20B is cheaper in all three workloads, by 63–71%. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.
When to choose which
- Cost is the deciding factor; it is cheaper in every workload above
- You are replacing llama-3.1-8b-instant on Groq and want the small model Groq itself recommends
- Classification, extraction and short assistant replies where cost per call matters more than depth
- You need vision; use Qwen3-VL 235B
- You need the strongest coding model; GLM 5.3 and DeepSeek V4 Pro rank higher
- You need more than 131K tokens of context (it takes 1M)
- You want a fast, inexpensive model with a 1M-token window and prompt caching
- Long-document summarisation and extraction where the whole file goes in one request
- Multi-step coding or agent work; use GPT-OSS 120B or DeepSeek V4 Flash
- You need more than 131K tokens of context
Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each in the playground.
Trying both
Same endpoint, same key. Change one string:
for MODEL in "openai/gpt-oss-20b" "Qwen/Qwen3.8-Flash"; do
curl -s https://api.inferenceapis.com/v1/chat/completions \
-H "Authorization: Bearer $INFERENCE_API_KEY" -H "Content-Type: application/json" \
-d "{\"model\": \"$MODEL\", \"max_tokens\": 500, \"messages\": [{\"role\": \"user\", \"content\": \"Summarise the plot of Hamlet in two sentences.\"}]}" \
| jq -r '.model, .choices[0].message.content, .usage'
doneFAQ
Which is cheaper, GPT-OSS 20B or Qwen3.8 Flash?
GPT-OSS 20B is cheaper in all three workloads, by 63–71%. The table above uses list prices: GPT-OSS 20B $0.04 / 1M tokens input, $0.19 / 1M tokens output; Qwen3.8 Flash $0.15 / 1M tokens input, $0.50 / 1M tokens output.
How do I switch between them?
They are on the same endpoint and key. Change the model field from openai/gpt-oss-20b to Qwen/Qwen3.8-Flash and nothing else.
Why does GPT-OSS 20B use more output tokens than the visible answer?
It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.