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Model details

GPT OSS 20B (Consensus Protocol)

gpt-oss-20b is the smaller sibling in OpenAI's gpt-oss family, released on August 5, 2025 as an open-weight language model available under the Apache 2.0 license. According to OpenAI's release notes, the 20B variant was trained using a mix of reinforcement learning and techniques informed by the company's most advanced internal models, including o3 and other frontier systems, giving it a clear lineage from OpenAI's reasoning-focused research rather than being a generic pretrained release. The family as a whole was described as pushing the frontier of open-weight reasoning models while outperforming similarly sized open models on reasoning tasks and demonstrating strong tool-use capabilities on evaluations such as Tau-Bench.

OpenAI positions gpt-oss-20b as delivering results comparable to OpenAI o3-mini on common reasoning benchmarks, making it attractive for developers who want o3-mini-class reasoning in a model small enough to run on a single 16 GB device. This makes it well suited to on-device assistants, local inference, and rapid prototyping without standing up costly GPU infrastructure, while still benefiting from chain-of-thought reasoning and tool calling for agentic workflows. In the broader market context, CNBC framed the gpt-oss launch as a lower-cost open-weight alternative aimed at competing with offerings from Meta, Mistral, and DeepSeek, so practitioners choosing this variant are getting a small, developer-friendly model that pairs lightweight deployment with reasoning and function-calling strengths inherited from OpenAI's larger systems.

LLM Gatewayconsensusprotocol/gpt-oss-20bgpt-oss

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Model key
consensusprotocol/gpt-oss-20b
Release date
Aug 5, 2025
Last updated
Aug 5, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.04
Output token cost
$0.19

Limits

Output tokens
32,768 tokens
Context window
128,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GPT OSS 20B (Consensus Protocol)

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CoverageBenchmark

The Inferbase catalog page documents core specifications for OpenAI's GPT-OSS-20B: a chat model with a 131,072-token context window for both input and output, 20.9B parameters, text input and output modalities, an Apache-2.0 license, an August 4, 2025 release date, and a June 1, 2024 training cutoff. The model is posit An Artificial Analysis benchmark bundle reports scores of 5.3% on HLE, 31.0% on AA-LCR, 61.1% on GPQA, 50.3% on Tau2-Bench, 62.3% on AIME 2025, 57.8% on IFBench, 71.8% on MMLU Pro, 65.2% on Math, 10.0% on LiveCodeBench, and 4.5% on Terminal-Bench Hard, with speed measured at 8.5 tokens per second, a TTFA of 0.486s, and

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Coverage

News coverage from DailySynapse reports that OpenAI, in a partnership with NVIDIA, introduced open-source gpt-oss language models tailored for deployment on NVIDIA RTX and RTX PRO GPUs. The gpt-oss-20b and gpt-oss-120b models are designed for reasoning tasks including web search, coding assistance, and document analysi The models leverage a Mixture-of-Experts architecture and use the MXFP4 precision format to boost efficiency and reduce resource demands, with training performed on NVIDIA H100 GPUs. Developers can run the models locally or in the cloud via frameworks including Ollama, llama.cpp, and Microsoft AI Foundry Local, with NV

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CoverageAnalysis

A third-party deployment study on arXiv (2508.16700) evaluates OpenAI's GPT-OSS-20B on a single H100 GPU in bf16 precision, providing detailed architectural and performance characteristics relevant to the underlying model. The paper confirms GPT-OSS-20B is a Mixture-of-Experts checkpoint with 20.9B total parameters and At a 2,048-token context with 64-token decode, GPT-OSS-20B delivers roughly 31.8% higher decode throughput and approximately 25.8% lower energy per 1,000 generated tokens than the dense Qwen3-32B baseline, while using about 31.7% less peak VRAM. The model shows a higher TTFT due to MoE routing overhead but markedly str

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