Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
Workload (per month)
OpenAI: gpt-oss-120b (batch)
Qwen: Qwen3 8B
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$4.94
$17.88
OpenAI: gpt-oss-120b (batch) saves $12.93
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$4.91
$18.59
OpenAI: gpt-oss-120b (batch) saves $13.68
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$5.18
$19.50
OpenAI: gpt-oss-120b (batch) saves $14.32
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$3.18
$12.09
OpenAI: gpt-oss-120b (batch) saves $8.91
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$1.62
$6.30
OpenAI: gpt-oss-120b (batch) saves $4.69
Capabilities side by side
Capability
OpenAI: gpt-oss-120b (batch)
Qwen: Qwen3 8B
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
✓ Yes
spend extra tokens thinking before answering hard problems
Image input
—
—
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
—
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
—
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
✓ Yes
repeat a generation for tests and evals
Log-probabilities
—
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
cost matters at scale — it is 72% cheaper on a typical 3:1 input/output mix ($4.94 vs $17.88 a month for 50,000 support chats)
you generate long outputs in one go — up to 118K tokens (about 88,500 words) per response
you want the newer model — released 2025-08-05, 3 months after the other
Choose Qwen: Qwen3 8B if…
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Frequently asked questions
Is OpenAI: gpt-oss-120b (batch) better than Qwen: Qwen3 8B?
OpenAI: gpt-oss-120b (batch) comes out ahead on the data we track: 3 advantages and none for Qwen: Qwen3 8B.
Which is cheaper, OpenAI: gpt-oss-120b (batch) or Qwen: Qwen3 8B?
OpenAI: gpt-oss-120b (batch) is cheaper: $0.06 vs $0.20 per 1M tokens at a typical 3:1 input/output mix, 72% less. For 50,000 support conversations a month that is $4.94 against $17.88.
How much do OpenAI: gpt-oss-120b (batch) and Qwen: Qwen3 8B cost per million tokens?
OpenAI: gpt-oss-120b (batch) costs $0.03 per 1M input tokens and $0.14 per 1M output tokens; Qwen: Qwen3 8B costs $0.12 input and $0.46 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, OpenAI: gpt-oss-120b (batch) or Qwen: Qwen3 8B?
Both take 131K tokens per request — roughly 197 pages of text.
Do OpenAI: gpt-oss-120b (batch) and Qwen: Qwen3 8B support function calling and JSON output?
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...
Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math,...