Gpt oss 120b
8912°
<oss-120b>
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Guides for this model
How to Install Ollama and Run a Local AI (Windows, Mac, Linux)
Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs
Best local LLM by use case (October 2026): the right open model for every job
Open weights vs open source vs API-only: what you are actually allowed to do
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
How much VRAM do I actually need for local AI?
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
OpenAI | 131,072 token context | $0.15/M input, $0.60/M output. 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 to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Captain's notes
Notes from 2026-10-13
gpt-oss-120b is OpenAI's first open-weight model release: a 117B-parameter Mixture-of-Experts built for reasoning-heavy and agentic work, licensed under Apache 2.0. Its MoE architecture keeps active parameters at 5.1B per forward pass, letting it fit on a single 80GB H100 with MXFP4 quantization. If you want a serious reasoning model you can actually run locally, fine-tune, and deploy commercially without royalty headaches, this is the one to watch.
Known for
- Apache 2.0 licence: fine-tune and deploy commercially without restrictions.
- Configurable reasoning depth: low, medium or high effort to trade latency for quality.
- Full chain-of-thought output exposed, useful for debugging pipelines.
- Native tool use: function calling, browsing, Python execution, structured outputs.
- Fits a single 80GB H100 or AMD MI300X via MXFP4 quantization.
Watch out for
- Requires the harmony response format; using it outside that format will produce broken output.
- A single H100 is still a serious hardware ask — not a consumer-GPU local model.
- Chain-of-thought output is for developers; OpenAI says not to surface it directly to end users.
Prompts to try
Multi-step agentic plan
You are building a Python service that monitors a public REST API for price changes and sends a Slack notification when a threshold is crossed. Break the problem into components, write the core polling loop with error handling, and describe how you would structure the function calls for the Slack webhook integration.
Hard reasoning puzzle
Five engineers each own a different programming language, drink a different beverage, and sit in a different seat. Use the following clues to determine who drinks tea: the Rust engineer sits next to the coffee drinker; the Python engineer sits at seat 1; the Go engineer drinks water; the TypeScript engineer sits between the Rust and Python engineers; the engineer in seat 5 drinks juice.
Debug a subtle bug
Here is a Python function that is supposed to return the longest substring without repeating characters, but it returns wrong results for some inputs. Walk through your reasoning step by step, identify the bug, and produce a corrected version:
def longest_unique(s):
seen = {}
start = 0
best = ''
for i, c in enumerate(s):
if c in seen:
start = seen[c]
seen[c] = i
if i - start + 1 > len(best):
best = s[start:i+1]
return best
Compare with
- GPT-5.6 SolAPI-only model1,050,000 token context · $4.00/M input, $20.00/M output
- GPT-6.1 SolAPI-only model1,050,000 token context · $2.00/M input, $10.00/M output
- GPT-6 SolAPI-only model1,050,000 token context · $2.00/M input, $10.00/M output
- GPT-6 AstraAPI-only model1,050,000 token context · $10.00/M input, $50.00/M output
More text models from openai
Questions people ask
- Can I run gpt-oss-120b locally?
- Yes — the weights are open under Apache 2.0 and available on Hugging Face. You need a single 80GB GPU such as an NVIDIA H100 or AMD MI300X; the model uses MXFP4 quantization to fit that constraint.
- What does gpt-oss-120b cost via API?
- OpenAI prices it at $0.15 per million input tokens and $0.60 per million output tokens, with a 131,072-token context window.
- How does gpt-oss-120b differ from gpt-oss-20b?
- gpt-oss-120b (117B parameters, 5.1B active) targets production and high-reasoning workloads on a single 80GB GPU; gpt-oss-20b (21B parameters, 3.6B active) is the smaller sibling aimed at lower latency, local or specialised use cases within 16GB of memory.
Captain's verdictOpenAI's first real open-weight release is a capable reasoning model with a clean licence — the hardware bar is just genuinely high.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #36 Hottest Text / LLM model · #36 Hottest Text / LLM text model
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gpt oss 120b Popularity Trend
Heat score, last 12 months
Heat score
8,912°
This week
▲
32.5%
8,875° then, 8,912° now
This month
▲
245%
8,747° then, 8,912° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,613°
OpenRouter tokens
2,379,488,165,808 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 237,948 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+299°
Adoption rate
About 9,681 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,912°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.237,948 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,613°8,613°running total
- 3Add the adoption rate.About 9,681 downloads a day right now, measured between two readings, at a quarter of the weight.+299°8,912°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,912°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Gpt oss 120b is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models, check its heat score to see how it is trending, and open it on PirateDiffusion or BitVector to try it.
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