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
OpenAI: gpt-oss-120b
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$9.50
$23.25
IBM: Granite 4.2 8B saves $13.75
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$9.70
$24.00
IBM: Granite 4.2 8B saves $14.30
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
$25.20
IBM: Granite 4.2 8B saves $15.00
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$6.30
$15.60
IBM: Granite 4.2 8B saves $9.30
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
$8.10
IBM: Granite 4.2 8B saves $4.85
Capabilities side by side
Capability
IBM: Granite 4.2 8B
OpenAI: gpt-oss-120b
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
✓ Yes
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 59% cheaper on a typical 3:1 input/output mix ($9.50 vs $23.25 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 2026-08-31, 13 months after the other
Choose OpenAI: gpt-oss-120b if…
you want to prototype at zero cost — a free variant is listed
Frequently asked questions
Is IBM: Granite 4.2 8B better than OpenAI: gpt-oss-120b?
Neither wins outright: IBM: Granite 4.2 8B is the pick when cost matters at scale, OpenAI: gpt-oss-120b when you want to prototype at zero cost. In short: IBM: Granite 4.2 8B when cost matters at scale, you generate long outputs in one go, you want the newer model; OpenAI: gpt-oss-120b when you want to prototype at zero cost.
Which is cheaper, IBM: Granite 4.2 8B or OpenAI: gpt-oss-120b?
IBM: Granite 4.2 8B is cheaper: $0.11 vs $0.26 per 1M tokens at a typical 3:1 input/output mix, 59% less. For 50,000 support conversations a month that is $9.50 against $23.25.
How much do IBM: Granite 4.2 8B and OpenAI: gpt-oss-120b cost per million tokens?
IBM: Granite 4.2 8B costs $0.06 per 1M input tokens and $0.25 per 1M output tokens; OpenAI: gpt-oss-120b costs $0.15 input and $0.60 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, IBM: Granite 4.2 8B or OpenAI: gpt-oss-120b?
Both take 131K tokens per request — roughly 197 pages of text.
Do IBM: Granite 4.2 8B and OpenAI: gpt-oss-120b 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,...
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...