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.

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.

Which endpoint do I call for GPT OSS 120B?

POST https://api.llmapi.ai/v1/chat/completions with model "gpt-oss-120b" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to GPT OSS 120B on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does GPT OSS 120B support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format. Reasoning effort accepts none, low, medium, high (default none).

GPT OSS 120B: Self-Hosting & Deployment Guide

GPT OSS 120B is available on LLM.API as an OpenAI-compatible chat endpoint—route OpenAI quality through one key with transparent token pricing.

Start Using API

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.

How to run GPT OSS 120B yourself

Open weights (apache-2.0), 116.8B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

GPT OSS 120B VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	233.7 GB	Estimate	116.8B params × 2 bytes
MLX 4-bit	65.8 GB	File size	mlx-community/gpt-oss-120b-4bit

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy GPT OSS 120B on your hardware

Datacenter GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	MLX 4-bit
NVIDIA H200141 GB	2×	1×
NVIDIA H10080 GB	4×	1×
NVIDIA A100 80GB80 GB	4×	1×
NVIDIA L40S48 GB	6×	2×

GPUs needed per variant.

Hardware	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits
Mac, 192 GBM2 Ultra	Fits
Mac, 128 GBM4 Max / M3 Max	Fits
Mac, 64 GBM4 Pro / Max	—

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	MLX 4-bit
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	—	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangMLX (Mac)

pip install -U vllm
vllm serve openai/gpt-oss-120b

pip install 'sglang[all]'
python -m sglang.launch_server --model-path openai/gpt-oss-120b

pip install -U mlx-lm
mlx_lm.generate --model mlx-community/gpt-oss-120b-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

LLM.API routes GPT OSS 120B to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

aws-bedrock$0.15 in

$0.6 out

cerebras$0.35 in

$0.75 out

togetherai$0.15 in

$0.6 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
aws-bedrock	in $0.15; out $0.6 per 1M tokens	131K tokens	tools, streaming, JSON, structured
cerebras	in $0.35; out $0.75 per 1M tokens	131K tokens	tools, streaming, reasoning, JSON, structured
togetherai	in $0.15; out $0.6 per 1M tokens	131K tokens	tools, streaming, reasoning, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test GPT OSS 120B right here — free to start.

GPT OSS 120B

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call GPT OSS 120B through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gpt-oss-120b and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-oss-120b",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "none"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_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."},
    ],
    reasoning_effort="none",
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gpt-oss-120b",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="gpt-oss-120b",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "gpt-oss-120b",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "none"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gpt-oss-120b · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`
Reasoning effort	none · low · medium · high	`reasoning_effort: "none"`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

GPT OSS 120B in production

Best workloadsLLM.API features

What the model is good at.

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.

What you get when you call GPT OSS 120B through LLM.API.

Unified AI Routing
Practical: Reach GPT OSS 120B and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale GPT OSS 120B.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Practical: Trace prompts, tokens, and errors for GPT OSS 120B alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Practical: Swap GPT OSS 120B for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

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

LLM API rating for GPT OSS 120B

★ 3.0/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

3.2Production adoptionModerate open-weight adoption

2.3Benchmark qualityArtificial Analysis Intelligence Index 11.6 · LMArena text rank #185, Elo 1365

3.7Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.0Coding 3.1Vision & multimodal 3.3Tool & function calling 3.7

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

BENCHMARKS

GPT OSS 120B benchmark scores

Intelligence index

This model12.

Tracked median8.

Scale: 0-100 index points

Output speed

This model181 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$0.15

Output$0.59

Bars compare input and output list prices for this model.

Intelligence index+50% vs median

GPT OSS 120B: Self-Hosting & Deployment Guide12

Median tracked model8

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed

181tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens

Input$0.15

Output$0.59

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

RELIABILITY

GPT OSS 120B uptime, last 30 days

30-Day Uptime

96.70%

Past Incidents (30d)

Error rate (24h)

9.27%

Last 30 days

26/30 days operational | 96.70% uptime

Availability tracked for the OpenAI GPT API. Full history on the LLM Uptime Status page or the status hub. LLM.API routes around provider outages automatically.

COMMUNITY

What developers say about OpenAI models

LMArena#185 · Elo 1365Arena coding#185 · Elo 1380Speed181 tokens/sSources: LMArena, Artificial Analysis

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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GPT OSS 120B: Self-Hosting & Deployment Guide

What is GPT OSS 120B?

How to run GPT OSS 120B yourself

GPT OSS 120B VRAM requirements

GPU requirements: deploy GPT OSS 120B on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

GPT OSS 120B in production

Multilingual drafting

Long-context synthesis

Analytical writing

Code assistance

Multi-step reasoning

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for GPT OSS 120B

GPT OSS 120B benchmark scores

Intelligence index

Output speed

Reference price per 1M tokens

GPT OSS 120B uptime, last 30 days

Last 30 days

What developers say about OpenAI models

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