OpenAI's open-source 120B model, and a workhorse on Kyma — strong-tier quality at cheap-tier pricing, steady across high volume. Reach for it when you want dependable general intelligence and writing without flagship cost.
Modalities
Text → Text
Weights
Published
openai/gpt-oss-120b
Where it sits in the catalogue Kyma measures
Every number against every text model Kyma prices per token — a stated rule, not a chosen line-up.
$0.0494
output $0.2406
median $0.977 · best $0.0494
$0.2406
per 1M tokens generated
median $3.531 · best $0.2164
92.43%
700 observations since 2026-05-02
median 96.2% · best 100.0%
34.2 tok/s
probe median, one fixed prompt
median 40.8 tok/s · best 308.3 tok/s
3.58 s
probe median, to a complete answer
median 2.50 s · best 0.52 s
128K
max output 8,192
published by OpenAI, not measured here
Tick above each rail is this model, below it the other 69. Dashed rule is the field median, solid is its best. Better is left; the axis stops at the 90th percentile, so a few models sit past its right edge.
Usage
How much this model is actually called here.
Rank
#5
of 113 active models
Tokens served
125.6M
all-time
Platform share
3.9%
of all tokens
How it behaves under real clients
The same model answering different prompt shapes — measured, not benchmarked.
| Client | Requests | Tokens | Throughput | To first token | p95 total | Completed |
|---|---|---|---|---|---|---|
| Python App | 181 | 711.6K | 48 tok/s | 6.74 s | 132 s | 100% |
Grouped by the client that sent the request. Not a benchmark: the same model on different prompt shapes. Throughput and time-to-first-token are medians; p95 is the slowest response in twenty. 2 clients under 100 requests not shown.
Two clocks, and why they disagree
Kyma measures this model twice. Both are real; they answer different questions.
Probe · every 6h · 30 days
3.58s to answer
One fixed prompt, on a schedule, to every model. Comparable, because the model is the only thing that changes.
- Observations
- 700
- Answered by a substitute
- 49
Real traffic · last 7 days
30.26s to answer
Your requests, at the lengths clients actually send. Not comparable between models, but it is what running this one feels like.
- Requests
- 61
- Completed
- 100%
- p95
- 182.8 s
The gap is prompt length, not the model degrading. Use the probe figure to choose between models, the traffic figure to budget for your own.
Pricing
Pay per token. Cached input bills at this model’s own cached rate, listed below.
+ Estimate your workload− Estimate your workload
Estimated monthly cost
$6.57
$0.2191 / day on GPT-OSS 120B
Same workload on:
Estimates use list pricing. Actual bills depend on real token counts, and every response includes its exact cost.
When to use GPT-OSS 120B
Where this model earns its cost — and where it doesn't.
GPT-OSS 120B is OpenAI's open-source model at 120B parameters, built for general intelligence and writing. On Kyma it sits in the strong quality tier with cheap-tier pricing — the combination teams pick when they need broadly capable output across many requests and the bill matters.
It has earned that role in production, mostly under coding agents and direct SDK integrations. Every call gets Kyma's automatic failover, and prompt caching is fully supported — repeated prompt prefixes bill at this model's cached input rate.
The 128K-token context window handles long documents and conversation history, while function calling and structured outputs make it usable inside agent pipelines, not just chat. It's text-in, text-out, with generations capped at 8K output tokens.
Writing and drafting
Its core strength — articles, summaries, rewrites, and editorial work at a price that holds up across high volume.
General assistants
A broadly capable default for chatbots and Q&A where you want strong-tier answers without flagship spend.
Agent pipelines
Function calling plus structured outputs keep it reliable inside multi-step agents — OpenClaw is already its top app by tokens on Kyma.
High-volume batch work
Cheap-tier pricing with prompt caching makes it economical for classification, extraction, and bulk text processing.
Long-context tasks
The 128K window fits substantial documents and long-running conversations in a single request.
Not ideal for: Image inputs (it's text-only), extended step-by-step reasoning modes (it has none), or single very long generations — output is capped at 8K tokens. Its medium speed also rules it out for latency-critical UX.
How it compares
Against the peers people actually weigh it against.
| Spec | GPT-OSS 120B | GPT-5.6 Sol Pro | GPT-5.6 Sol |
|---|---|---|---|
| Input /1M | $0.0494 | $2.70 | $2.70 |
| Output /1M | $0.2406 | $13.50 | $13.50 |
| Context | 128K | 1M | 1M |
| Tools | Yes | Yes | Yes |
| Reasoning | No | Yes | Yes |
| Throughput | 34.2 tok/s | 51.8 tok/s | 28.1 tok/s |
Quick start
Up and running in under two minutes.
- 1
Create an API key
Sign up and grab a key from the dashboard — $0.50 free credit, no card required.
Get API key → - 2
Make your first request
Drop in your key and send a chat completion — fully OpenAI-compatible.
curl https://kymaapi.com/v1/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-oss-120b", "messages": [ {"role": "user", "content": "Explain prompt caching in one paragraph."} ] }' - 3
Stream responses
Add
"stream": trueto receive tokens as they arrive.curl https://kymaapi.com/v1/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-oss-120b", "stream": true, "messages": [{"role": "user", "content": "Hello!"}] }'
FAQ
Common questions about this model.
What is the context window of GPT-OSS 120B?
How much does the GPT-OSS 120B API cost?
Does GPT-OSS 120B support function calling?
Are the weights for GPT-OSS 120B publicly available?
How do I use GPT-OSS 120B?
Is GPT-OSS 120B good for writing?
What throughput should I expect?
Why use GPT-OSS 120B through Kyma?
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