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)
IBM: Granite 4.2 8B
Qwen: Qwen3 235B A22B
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$9.50
$70.53
IBM: Granite 4.2 8B saves $61.03
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$9.70
$72.80
IBM: Granite 4.2 8B saves $63.10
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)
$10.20
$76.44
IBM: Granite 4.2 8B saves $66.24
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$6.30
$47.32
IBM: Granite 4.2 8B saves $41.02
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$3.25
$24.57
IBM: Granite 4.2 8B saves $21.32
Capabilities side by side
Capability
IBM: Granite 4.2 8B
Qwen: Qwen3 235B A22B
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
✓ Yes
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Qwen: Qwen3 235B A22B: price unchanged since we started tracking it on 2026-03-10.
Date
Model
Input / 1M
Output / 1M
2026-09-22
IBM: Granite 4.2 8B
$0.06
$0.25
2026-09-21
IBM: Granite 4.2 8B
$0.10
$0.15
2026-09-09
IBM: Granite 4.2 8B
$0.06
$0.25
Which should you choose?
Choose IBM: Granite 4.2 8B if…
cost matters at scale — it is 86% cheaper on a typical 3:1 input/output mix ($9.50 vs $70.53 a month for 50,000 support chats)
you need log-probabilities — to read token confidence for classification and scoring; Qwen: Qwen3 235B A22B does not advertise it
you generate long outputs in one go — up to 118K tokens (about 88,500 words) per response
you want the newer model — released 2026-08-31, 16 months after the other
Choose Qwen: Qwen3 235B A22B if…
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Frequently asked questions
Is IBM: Granite 4.2 8B better than Qwen: Qwen3 235B A22B?
IBM: Granite 4.2 8B comes out ahead on the data we track: 4 advantages and none for Qwen: Qwen3 235B A22B.
Which is cheaper, IBM: Granite 4.2 8B or Qwen: Qwen3 235B A22B?
IBM: Granite 4.2 8B is cheaper: $0.11 vs $0.80 per 1M tokens at a typical 3:1 input/output mix, 86% less. For 50,000 support conversations a month that is $9.50 against $70.53.
How much do IBM: Granite 4.2 8B and Qwen: Qwen3 235B A22B cost per million tokens?
IBM: Granite 4.2 8B costs $0.06 per 1M input tokens and $0.25 per 1M output tokens; Qwen: Qwen3 235B A22B costs $0.46 input and $1.82 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, IBM: Granite 4.2 8B or Qwen: Qwen3 235B A22B?
Both take 131K tokens per request — roughly 197 pages of text.
Do IBM: Granite 4.2 8B and Qwen: Qwen3 235B A22B support function calling and JSON output?
Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It supports full, low-effort,...
Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...