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-5 Nano
Qwen: Qwen3.5-9B (batch)
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$11.75
$17.75
OpenAI: GPT-5 Nano saves $6.00
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$10.00
$22.90
OpenAI: GPT-5 Nano saves $12.90
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)
$10.80
$23.40
OpenAI: GPT-5 Nano saves $12.60
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$6.40
$15.10
OpenAI: GPT-5 Nano saves $8.70
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$2.90
$8.75
OpenAI: GPT-5 Nano saves $5.85
Capabilities side by side
Capability
OpenAI: GPT-5 Nano
Qwen: Qwen3.5-9B (batch)
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
✓ Yes
✓ Yes
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
✓ Yes
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
✓ Yes
—
answer from live web results
Seed (reproducible sampling)
✓ 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.
Price history
OpenAI: GPT-5 Nano: price unchanged since we started tracking it on 2026-03-10.
Qwen: Qwen3.5-9B (batch): price unchanged since we started tracking it on 2026-08-29.
Which should you choose?
Choose OpenAI: GPT-5 Nano if…
cost matters at scale — it is 28% cheaper on a typical 3:1 input/output mix ($11.75 vs $17.75 a month for 50,000 support chats)
you need to fit long inputs in one request — 400K tokens is roughly 600 pages, against 393
you need file / PDF input — to accept documents without your own parsing step; Qwen: Qwen3.5-9B (batch) does not advertise it
you need built-in web search — to answer from live web results; Qwen: Qwen3.5-9B (batch) does not advertise it
you need seed (reproducible sampling) — to repeat a generation for tests and evals; Qwen: Qwen3.5-9B (batch) does not advertise it
Choose Qwen: Qwen3.5-9B (batch) if…
you generate long outputs in one go — up to 236K tokens (about 176,900 words) per response
you want the newer model — released 2026-03-10, 7 months after the other
Frequently asked questions
Is OpenAI: GPT-5 Nano better than Qwen: Qwen3.5-9B (batch)?
Neither wins outright: OpenAI: GPT-5 Nano is the pick when cost matters at scale, Qwen: Qwen3.5-9B (batch) when you generate long outputs in one go. In short: OpenAI: GPT-5 Nano when cost matters at scale, you need to fit long inputs in one request, you need file / PDF input, you need built-in web search, you need seed (reproducible sampling); Qwen: Qwen3.5-9B (batch) when you generate long outputs in one go, you want the newer model.
Which is cheaper, OpenAI: GPT-5 Nano or Qwen: Qwen3.5-9B (batch)?
OpenAI: GPT-5 Nano is cheaper: $0.14 vs $0.19 per 1M tokens at a typical 3:1 input/output mix, 28% less. For 50,000 support conversations a month that is $11.75 against $17.75.
How much do OpenAI: GPT-5 Nano and Qwen: Qwen3.5-9B (batch) cost per million tokens?
OpenAI: GPT-5 Nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens; Qwen: Qwen3.5-9B (batch) costs $0.17 input and $0.25 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, OpenAI: GPT-5 Nano or Qwen: Qwen3.5-9B (batch)?
OpenAI: GPT-5 Nano: 400K tokens, about 600 pages, against 262K (about 393 pages) for Qwen: Qwen3.5-9B (batch).
Do OpenAI: GPT-5 Nano and Qwen: Qwen3.5-9B (batch) support function calling and JSON output?
GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger...
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...