GPT OSS 120B Overview

Description:

OpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:

gpt-oss-120b — for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)
gpt-oss-20b — for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).

The gpt-oss-120b model is architecturally designed as a Mixture-of-Experts (MoE) model. This model features SwiGLU activations and learned attention sinks within its architecture. It functions as a reasoning model, supporting capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. This model is text-only for both input and output modalities, enabling enterprises and governments to deploy it on-premises or in private cloud environments for enhanced data security and privacy.

Model Highlights:

Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
Full chain-of-thought: Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.
Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
Agentic capabilities: Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA gpt-oss-120b model card.

License and Terms of Use:

GOVERNING TERMS: The use of this model is governed by the NVIDIA Open Model License Agreement.

Additional Information: Apache 2.0.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws

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Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

NVIDIA NIM Documentation

Visit the NIM Container LLM page for release documentation, deployment guides and more.

Deployment Geography:

Global

Use Case:

Intended for use as a reasoning model with features like chain-of-thought and adjustable reasoning effort levels. It supports instruction following and tool use, offering transparency, customization, and deployment flexibility for developers, researchers, and startups. Additionally, it enables enterprises and governments to deploy on-premises or in private clouds to ensure data security and privacy.

Release Date:

Build.NVIDIA.com - 08/05/2025 via link
\nHugging Face - 08/05/2025 via link

Reference(s):

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Mixture-of-Experts (MoE)
\nTotal Parameters: 117B
\nActive Parameters: 5.7B
\nVocabulary Size: 201,088

Input:

Input Type(s): Text
\nInput Format(s): String
\nInput Parameters: One Dimensional (1D)
\nOther Properties Related to Input: Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000

Output:

Output Type(s): Text
\nOutput Format: String
\nOutput Parameters: One Dimensional (1D)
\nOther Properties Related to Output: The model is designed to be compatible with the OpenAI Responses API and supports Structured Output.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems [or name equivalent hardware preference]. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

NeMo Framework (based on 25.07)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell: B200
NVIDIA Hopper: H200

Operating System(s): Linux

Model Version(s):

gpt-oss-120b v1.0 (August 5, 2025)

Training, Testing, and Evaluation Datasets:

Training Dataset:

Training Data Collection: Undisclosed
Training Labeling: Undisclosed
Training Properties: The model has approximately 117 billion parameters. Weights for all layers are in BF16, except for MoE projection weights, which are in MXFP4. The reference implementation currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.

Testing Dataset:

Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: The model is tested against benchmarks such as MMLU and GPQA, among others including LiveCodeBench, AIME 2024, and MATH-500.

Evaluation Dataset:

Evaluation Data Collection: Undisclosed
Evaluation Labeling: Undisclosed
Evaluation Benchmark Score:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	gpt-oss-120b	gpt-oss-20b
AIME 2024 (no tools)	95.8	92.1
AIME 2024 (with tools)	96.6	96.0
AIME 2025 (no tools)	92.5	91.7
AIME 2025 (with tools)	97.9	98.7
GPQA Diamond (no tools)	80.1	71.5
GPQA Diamond (with tools)	80.9	74.2
HLE (no tools)	14.9	10.9
HLE (with tools)	19.0	17.3
MMLU	90.0	85.3
SWE-Bench Verified	62.4	60.7
Tau-Bench Retail	67.8	54.4
Tau-Bench Airline	49.2	38.0
Aider Polyglot	44.4	34.2
MMMLU (Average)	81.3	75.6
HealthBench	57.6	42.5
HealthBench Hard	30.0	10.8
HealthBench Consensus	89.9	82.6
Codeforces (no tools) [elo]	2463	2230
Codeforces (with tools) [elo]	2622	2516

Above scores were measured for the high reasoning level.

Safety Results:

The following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI’s safety policies, including hateful content or illicit advice.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Category	gpt-oss-120b	gpt-oss-20b
hate (aggregate)	0.996	0.996
self-harm/intent and selfharm/instructions	0.995	0.984
personal data/semi restrictive	0.967	0.947
sexual/exploitative	1.000	0.980
sexual/minors	1.000	0.971
illicit/non-violent	1.000	0.983
illicit/violent	1.000	1.000
personal data/restricted	0.996	0.978

Inference:

Acceleration Engine: vLLM
\nTest Hardware: NVIDIA Hopper: B200

Additional Details

The model is released with the native quantization support. Specifically, MXFP4 is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:

tensor.blocks stores the actual fp4 values. Every two values are packed in one uint8 value.
tensor.scales stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.

All other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\nOpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:\n- `gpt-oss-120b` — for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)\n- `gpt-oss-20b` — for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).\n\nThe `gpt-oss-120b` model is architecturally designed as a Mixture-of-Experts (MoE) model. This model features SwiGLU activations and learned attention sinks within its architecture. It functions as a reasoning model, supporting capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. This model is text-only for both input and output modalities, enabling enterprises and governments to deploy it on-premises or in private cloud environments for enhanced data security and privacy.\n\nModel Highlights: \n- **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.\n- **Full chain-of-thought:** Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.\n- **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.\n- **Agentic capabilities:** Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.\n\nThis model is ready for commercial/non-commercial use.\n\n\n## Third-Party Community Consideration
\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [gpt-oss-120b model card](https://huggingface.co/openai/gpt-oss-120b).\n\n\n### License and Terms of Use:
\nGOVERNING TERMS: The use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws**\n\n## Get Help\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides and more.\n\n### Deployment Geography:\nGlobal\n\n### Use Case:
\nIntended for use as a reasoning model with features like chain-of-thought and adjustable reasoning effort levels. It supports instruction following and tool use, offering transparency, customization, and deployment flexibility for developers, researchers, and startups. Additionally, it enables enterprises and governments to deploy on-premises or in private clouds to ensure data security and privacy.\n\n### Release Date:
\nBuild.NVIDIA.com - 08/05/2025 via [link](https://build.nvidia.com/openai/gpt-oss-120b)
\nHugging Face - 08/05/2025 via [link](https://huggingface.co/openai/gpt-oss-120b)
\n\n## Reference(s):\n- [OpenAI Cookbook](https://cookbook.openai.com/)\n- [Open AI Coobkbook -- Serving Model with TensorRT-LLM](https://cookbook.openai.com/articles/gpt-oss/run-nvidia)\n\n\n## Model Architecture:
\n**Architecture Type:** Transformer
\n**Network Architecture:** Mixture-of-Experts (MoE)
\n**Total Parameters:** 117B
\n**Active Parameters:** 5.7B
\n**Vocabulary Size:** 201,088
\n\n\n## Input:
\n**Input Type(s):** Text
\n**Input Format(s):** String
\n**Input Parameters:** One Dimensional (1D)
\n**Other Properties Related to Input:** Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000
\n\n## Output:
\n**Output Type(s):** Text
\n**Output Format:** String
\n**Output Parameters:** One Dimensional (1D)
\n**Other Properties Related to Output:** The model is designed to be compatible with the OpenAI Responses API and supports Structured Output.
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems [or name equivalent hardware preference]. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
\n\n## Software Integration:
\n**Runtime Engine(s):**
\n* NeMo Framework (based on 25.07)
\n\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Blackwell: B200
\n* NVIDIA Hopper: H200\n\n\n**Operating System(s):** Linux \n\n## Model Version(s): \n`gpt-oss-120b` v1.0 (August 5, 2025)\n\n\n## Training, Testing, and Evaluation Datasets:
\n### Training Dataset:\n\n* **Training Data Collection:** Undisclosed
\n* **Training Labeling:** Undisclosed
\n* **Training Properties:** The model has approximately 117 billion parameters. Weights for all layers are in BF16, except for MoE projection weights, which are in MXFP4. The reference implementation currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.\n\n\n### Testing Dataset:\n* **Testing Data Collection:** Undisclosed
\n* **Testing Labeling:** Undisclosed
\n* **Testing Properties:** The model is tested against benchmarks such as MMLU and GPQA, among others including LiveCodeBench, AIME 2024, and MATH-500. \n\n### Evaluation Dataset:\n\n* **Evaluation Data Collection:** Undisclosed
\n* **Evaluation Labeling:** Undisclosed
\n* **Evaluation Benchmark Score:** \n\n| Benchmark | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| AIME 2024 (no tools) | 95.8 | 92.1 |\n| AIME 2024 (with tools) | 96.6 | 96.0 |\n| AIME 2025 (no tools) | 92.5 | 91.7 |\n| AIME 2025 (with tools) | 97.9 | 98.7 |\n| GPQA Diamond (no tools) | 80.1 | 71.5 |\n| GPQA Diamond (with tools) | 80.9 | 74.2 |\n| HLE (no tools) | 14.9 | 10.9 |\n| HLE (with tools) | 19.0 | 17.3 |\n| MMLU | 90.0 | 85.3 |\n| SWE-Bench Verified | 62.4 | 60.7 |\n| Tau-Bench Retail | 67.8 | 54.4 |\n| Tau-Bench Airline | 49.2 | 38.0 |\n| Aider Polyglot | 44.4 | 34.2 |\n| MMMLU (Average) | 81.3 | 75.6 |\n| HealthBench | 57.6 | 42.5 |\n| HealthBench Hard | 30.0 | 10.8 |\n| HealthBench Consensus | 89.9 | 82.6 |\n| Codeforces (no tools) [elo] | 2463 | 2230 |\n| Codeforces (with tools) [elo] | 2622 | 2516 |\n\nAbove scores were measured for the high reasoning level.\n\n### Safety Results:\n\nThe following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI’s safety policies, including hateful content or illicit advice.\n\n| Category | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| hate (aggregate) | 0.996 | 0.996 |\n| self-harm/intent and selfharm/instructions | 0.995 | 0.984 |\n| personal data/semi restrictive | 0.967 | 0.947 |\n| sexual/exploitative | 1.000 | 0.980 |\n| sexual/minors | 1.000 | 0.971 |\n| illicit/non-violent | 1.000 | 0.983 |\n| illicit/violent | 1.000 | 1.000 |\n| personal data/restricted | 0.996 | 0.978 |\n\n## Inference:\n**Acceleration Engine:** vLLM
\n**Test Hardware:** NVIDIA Hopper: B200
\n\n\n## Additional Details\nThe model is released with the native quantization support. Specifically, [MXFP4](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:\n\n- `tensor.blocks` stores the actual fp4 values. Every two values are packed in one `uint8` value.\n- `tensor.scales` stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.\n\nAll other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","GPT-OSS-120B",["D",1779899595178],"publisher","OpenAI","latestVersion","collectionCounts","imageUrl","/api/image/nim/openai/models/gpt-oss-120b","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/gpt-oss-120b.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_284":285,"_286":287,"_288":289,"_290":35},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[275,276],"displayLabels",[261,262,263],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_228":273,"_265":273,"_266":274,"_268":269},{"_228":271,"_265":271,"_266":272,"_268":269},{"_228":264,"_265":264,"_266":267,"_268":269},"Signed Models","value","filter",{"_270":269,"_265":264},"category","label","field","NSPECT-EEZS-7JBM",{"_270":269,"_265":271},"False",{"_270":269,"_265":273},{"_228":282,"_265":282,"_266":283,"_268":280},{"_228":277,"_265":278,"_266":279,"_268":280},"NVIDIA NIM","nimmcro_nvidia_nim",{"_270":281,"_265":278},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_270":269,"_265":282},"governingTerms","

GOVERNING DOWNLOAD TERMS

The use of this model is governed by the NVIDIA Open Model License Agreement (https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

Additional Information: Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0).

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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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