GPT OSS 120B
Up to 30%GPT OSS 120B is available on LLM.API as an OpenAI-compatible chat endpoint—route OpenAI quality through one key with transparent token pricing.
What is GPT OSS 120B?
On LLM.API, GPT OSS 120B (gpt-oss-120b) serves as a OpenAI conversational model for products that need 131K tokens context windows and predictable token pricing. Open-source 120B parameter model with reasoning capabilities.
Providers
LLM.API routes GPT OSS 120B to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
 aws-bedrock30% off | in $150; out $600 per 1M tokens | 131K tokens | vision, tools, streaming, JSON, structured |
| in $350; out $750 per 1M tokens | 131K tokens | vision, tools, streaming, reasoning, JSON, structured | |
| in $150; out $600 per 1M tokens | 131K tokens | vision, tools, streaming, reasoning, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test GPT OSS 120B right here — free to start.
Suggestions for your first prompt
Code snippet
Call GPT OSS 120B through the OpenAI-compatible API — POST /v1/chat/completions.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="gpt-oss-120b",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "gpt-oss-120b",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Multilingual drafting
Drafts and translates professional content across major business languages. Grounded in the model's chat role rather than generic chat claims.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Relevant for `gpt-oss-120b` workloads on LLM.API.
Code assistance
Helps write, explain, refactor, and debug application code across common languages. Grounded in the model's chat role rather than generic chat claims.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Reflects OpenAI positioning for this endpoint.
6 Most Valuable Use Cases
- Data extraction into JSON for downstream systems with GPT OSS 120B
- Sales and success email drafting with CRM context
- Product analytics narration and anomaly explanations with GPT OSS 120B
- Internal knowledge assistants grounded with your retrieval layer
- Multilingual localization drafts for UX copy with GPT OSS 120B
- Research synthesis across long documents and tickets
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run GPT OSS 120B on LLM.API?
Unified AI Routing
Practical: Reach GPT OSS 120B and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale GPT OSS 120B.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Practical: Trace prompts, tokens, and errors for GPT OSS 120B alongside the rest of your stack.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Practical: Swap GPT OSS 120B for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You need a general-purpose text model for assistants, agents, or content workflows (GPT OSS 120B)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (GPT OSS 120B)
- You want OpenAI-compatible chat completions through a single LLM.API key (GPT OSS 120B)
- You need provider failover options exposed for this model id (GPT OSS 120B)
Avoid if...
- Your use case depends on unpublished proprietary benchmarks not listed here
- You need pure embedding, OCR, or media generation instead of chat
- You require on-prem only deployment with no cloud inference
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
BENCHMARKS
GPT OSS 120B benchmark scores
Intelligence index
Scale: 0-100 index points
Output speed
Scale: 0-400 tokens per second
Reference price per 1M tokens
Bars compare input and output list prices for this model.
Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.
COMMUNITY
What developers say about OpenAI models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GPT OSS 120B.
- Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
- The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
- Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.
SOURCES
Frequently Asked Questions
What is the context length for GPT OSS 120B?
Reported context for GPT OSS 120B is 131K tokens. Always verify the active provider row if multiple providers are listed.
Which providers serve GPT OSS 120B?
LLM.API currently lists: aws-bedrock, cerebras, togetherai. Availability can vary by region and account.
What is GPT OSS 120B?
Open-source 120B parameter model with reasoning capabilities. On LLM.API it is addressed as `gpt-oss-120b`.
When should I choose GPT OSS 120B?
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need GPT OSS 120B.
How is GPT OSS 120B priced on LLM.API?
Listed pricing metadata shows: In $0.15 / 1M tokens · Out $0.6 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Can I use tools or structured outputs with GPT OSS 120B?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What are limitations of GPT OSS 120B?
Like other API models, GPT OSS 120B can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Is GPT OSS 120B a chat model?
Yes—GPT OSS 120B is exposed as a chat/completions-style endpoint on LLM.API.
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