LangMart: OpenAI: gpt-oss-120b
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/openai/gpt-oss-120b |
| Name | OpenAI: gpt-oss-120b |
| Provider | openai |
| Released | 2025-08-05 |
Description
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.
Description
LangMart: OpenAI: gpt-oss-120b is a language model provided by openai. This model offers advanced capabilities for natural language processing tasks.
Provider
openai
Specifications
| Spec | Value |
|---|---|
| Context Window | 131,072 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.04 per 1M tokens |
| Output | $0.19 per 1M tokens |
Capabilities
- Frequency penalty
- Include reasoning
- Logit bias
- Logprobs
- Max tokens
- Min p
- Presence penalty
- Reasoning
- Reasoning effort
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top logprobs
- Top p
Detailed Analysis
GPT-OSS-120b is OpenAI's large open-weight model released under Apache 2.0 license with 117B total parameters using mixture-of-experts (MoE) architecture. Activates only 5.1B parameters per token, running efficiently within 80GB memory on a single GPU. Features 128 expert sub-networks (4 active per token), 128K native context length with RoPE positional encoding, and grouped multi-query attention. Achieves near-parity with o4-mini on reasoning benchmarks. Natively quantized in MXFP4. Priced at $0.04/$0.40 per 1M tokens. Best for: self-hosted deployments requiring strong performance, on-premise applications with data privacy requirements, cost-sensitive large-scale deployments, research requiring model inspection and modification.