GPT OSS 120B
OpenAI's largest open-weight reasoning model — 117B MoE parameters, Apache 2.0 licensed, with web search and full chain-of-thought.
Overview
What is GPT OSS 120B
GPT OSS 120B is OpenAI's 117B-parameter open-weight reasoning model released in August 2025 under the Apache 2.0 license. It supports configurable reasoning effort, full chain-of-thought, native web search, and efficient deployment on a single 80GB GPU for data sovereignty.
Running it privately on Venice
On Venice, GPT OSS 120B runs inside a trusted execution environment with end-to-end encryption and zero retention — your prompts are never stored, profiled, or used for training. You get the same open-weight model with reasoning and web-search capabilities, but with full data sovereignty and no Big-Tech surveillance.
Assessment
Strengths and limitations
- Open weights under Apache 2.0: permissionless self-hosting, fine-tuning, and commercial use without copyleft or patent risk.
- Configurable reasoning effort (low, medium, high) with complete chain-of-thought visibility for debugging and trust.
- Native web search and reasoning on Venice, with broader agentic capabilities available when self-hosted.
- Efficient deployment: fits on a single 80GB GPU (H100 or AMD MI300X) despite 117B parameters thanks to MoE architecture and MXFP4 quantization.
- Strong instruction following designed for agentic workflows and structured outputs.
- Text-only: no vision, image, or audio support.
- Requires the harmony response format; using other formats will cause incorrect behavior.
- Open-weight release means determined attackers can fine-tune away safety refusals, so downstream systems need extra guardrails.
- Not uncensored: follows OpenAI safety policies by default, which may refuse certain requests.
- The smaller GPT OSS 20B can be more efficient for latency-sensitive tasks and sometimes matches or exceeds this model on select benchmarks.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- OpenAI
- Released
- August 5, 2025
- Architecture
- Mixture of Experts (MoE)
- Parameters
- 117B total / 5.1B active
- Open weights
- Yes — Apache 2.0
- Context window
- 128K tokens
- Max output
- 32.768K tokens
- Capabilities
- Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Mar 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-gpt-oss-120b-p",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.13 per 1M input tokens and $0.65 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context window | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| GPT OSS 120B | 128K tokens | Yes | $0.13 in · $0.65 out / 1M | Open-weight reasoning & web search |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M | Long-context open MoE |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M | Long-context reasoning |
| Claude Sonnet 4.6 | 1M tokens | No | $3.60 in · $18 out / 1M | Closed frontier agent |
OpenAI's most powerful open model — full CoT, Apache 2.0, and efficient on a single GPU.
Use cases
What it is good for
- 01Self-hosted or on-premise reasoning for enterprises that need data sovereignty and zero retention.
- 02Agentic workflows with web search, structured outputs, and chain-of-thought debugging.
- 03Fine-tuning for domain-specific instruction following or compliance requirements.
- 04Cost-efficient large-scale inference where open-weight control and permissionless deployment matter.
- 05Privacy-sensitive research and development via Venice's encrypted, zero-retention inference.
Prompting
Getting better results
Always use the harmony response format — the model is trained exclusively for it and will malfunction with other formats.
Explicitly set reasoning effort (low, medium, high) to balance latency against answer depth.
Request structured outputs when you need machine-parseable results.
Inspect chain-of-thought for debugging, but avoid exposing raw reasoning traces to end users.
Version history
Smaller sibling — 21B parameters, faster, edge-device friendly.
Current — largest open-weight model in the series.
FAQ
Frequently asked questions
GPT OSS 120B is OpenAI's largest open-weight language model, released in August 2025 under the Apache 2.0 license. It has 117B total parameters with 5.1B active per token, supports reasoning, web search, and full chain-of-thought, and fits on a single 80GB GPU.
Venice bills per token at $0.13 per 1M input tokens and $0.65 per 1M output tokens. There is no subscription required — you only pay for what you use.
It is open weights under the Apache 2.0 license, so you can download, self-host, and fine-tune the model. However, the training data and full training code are not open source.
Yes. On Venice it exposes native web search and reasoning. Because the weights are open, you can also self-host to enable function calling, Python execution, and structured outputs via the OpenAI-compatible API.
On Venice, GPT OSS 120B runs in a trusted execution environment with end-to-end encryption and zero retention. Your prompts are not stored, profiled, or used for training.
Choose GPT OSS 120B for cheaper input pricing, full chain-of-thought transparency, and OpenAI-compatible agentic tooling. Choose DeepSeek V3.2 if you need a larger 160K context window or lower output pricing.
Thanks to MoE architecture and MXFP4 quantization, it runs on a single 80GB GPU such as an NVIDIA H100 or AMD MI300X.
Yes. You can set low, medium, or high reasoning effort to trade latency against depth of thinking, and you get full access to the model's chain-of-thought for debugging.
Run GPT OSS 120B privately
No prompt logging. No data used for training.