Every OpenAI Open Source Model Ranked and Explained for 2026

Most folks assume that OpenAI has a huge open-source lineup. After all, they dominate the AI space. Truth is, they do not share their work openly. OpenAI has historically kept its most capable models closed and shared only a handful of open-weight releases on purpose.

The big shift to OpenAI’s approach to open source came in 2025 with the release of gpt-oss models. This guide walks you through every open-weight model OpenAI has ever released. It covers what each model does, comparisons, and their real value in 2026.

Here is the full list of OpenAI’s open-source models:

• gpt-oss 120b
• gpt-oss 20b
• Whisper
• CLIP
• GPT-2
• Jukebox, Point-E, Shap-E (older niche releases)

OpenAI and Open Source: A Brief History

To understand why this list is so short, you have to go back to the beginning. OpenAI launched in 2015 as a nonprofit with a mission to “advance digital intelligence” for the benefit of humanity. In those early years, they strongly promoted an open research approach. The company open-sources most of their early work, including GPT-2 (after some initial hesitation).

In 2019, it transitioned to a “capped-model” company, a combination of for-profit and nonprofit. By the time GPT-3 arrived, OpenAI limited itself to closed, API-only releases. They did not release model weights for GPT-4, GPT-5, and subsequent models, citing safety concerns.

That changed last year with the release of gpt-oss models. This was likely due to the massive success of open-source models like Qwen, Mistral, Llama series, and DeepSeek.

Today, OpenAI releases open-weight models instead of fully open-source ones. It means you get the final trained weights to run locally, but not the full training code or data. On the other hand, open source means getting the full training code, architecture, and datasets.

gpt-oss 120B

The gpt-oss 120b is OpenAI’s major open-weight release so far. It uses a sparse MoE architecture that only activates 5.1B parameters per token. Despite its large size, this efficiency allows it to run on a single 80GB GPU (H100/AMD MI300X).

• Parameters: 117B parameters (128 experts), 5.1B active (4 experts)
• Context Window: 128K
• Quantization: MXFP4
• License: Apache 2.0
• Release: August 2025

Performance benchmarks: It matches OpenAI’s o4-mini on competition math (AIME 2024/2025) and core reasoning tasks. The 120b variant scores 97.9% on AIME 25, nearly equal to o4-mini’s 99.5%. It exceeds o4-mini on health reasoning tasks by scoring 57.6% on HealthBench (compared to o4-mini’s 50.1%) and 30% on HealthBench Hard (versus o4-mini’s 17.5%). Plus, gpt-oss attains 90% and 80.1% on MMLU and GPQA Diamond (without tools), respectively.

Strengths

• Adjustable reasoning efforts (low/medium/high) depending on the use case
• Full Chain-of-Thought (CoT) outputs for easier debugging and increased trust
• Native tool use capabilities (web search, function calling, Python execution, structured output)
• Fully customizable through parameter fine-tuning
• Commercial-friendly Apache 2.0 license

Limitations: gpt-oss 120b is noticeably weaker on agentic coding. Plus, it is not as capable as GPT-5 on cutting-edge reasoning tasks. It is text-only and does not accept image or audio inputs. The model requires substantial hardware (~ 240GB) to run at full precision.

Availability on Okara: Yes, run it privately without any setup.

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gpt-oss 20b

It is the smaller but mighty sibling in the gpt-oss family. At 21b parameters, the model is built to fit on consumer hardware (RTX 4090 with 4-bit quant). gpt-oss 20b matches and outperforms OpenAI’s o3-mini on many benchmarks. It is best for developers building local AI features and testing ideas without enterprise-grade infrastructure.

• Parameters: 21B parameters (32 experts), 3.6 active (4 experts)
• Context Window: 128K
• Quantization: MXFP4
• License: Apache 2.0
• Release: August 2025

Performance benchmarks: It scores 85.3% on MMLU, just shy of o4-mini's 87%. Like gpt-oss 120b, it also outperforms its competitors on health-related reasoning. The 20b variant achieves 42.5% on HealthBench (vs. o3-mini's 37.8%) and 10.8% on HealthBench Hard (compared to o4-mini's 4%). It records 2516 (with tools) and 2230 (without tools) on Codeforces.

Strength

• Edge deployment and on-device applications
• Same adjustable reasoning and tool use capabilities as the 120b variant
• Best for prototyping and testing ideas
• Low latency for simple tasks when set to “low” reasoning effort
• Apache 2.0 license for commercial use and customization

Limitations: gpt-oss 20b is less capable for highly complex reasoning or niche coding than its bigger sibling. Similar to 120b, it also has a text-only modality. The model requires careful prompt engineering to get the best results.

Availability on Okara: Yes, it is privately hosted by the platform.

Try gpt-oss 20b for Free!

Whisper

Whisper is the go-to open model for turning speech into text. Unlike OpenAI’s text models, it has always been open. It was released in 2022 with multiple sizes, the largest model with 1.55B parameters. Whisper worked with noisy audio and accents far better than most models did at launch.

Undoubtedly, this well-documented Automatic Speech Recognition (ASR) model is a true gift from OpenAI to the developer community.

• Parameters: Tiny (39M), Base (74M), Small (244M), Medium (769M), Large (1.55B), Turbo (809M)
• Context Window: 30-second audio chunks
• Quantization: Various community options (GGUF, INT8)
• License: MIT
• Release: September 2022

Performance benchmarks: Benchmarks for Whisper models show 5-10% lower WER (Word Error Rate) than competitors on noisy data. It also outperforms other models on alphanumeric recognition and formatting.

Strengths

• Trained on 680,000 hours of supervised data covering multiple languages and tasks
• Translates speech to English from 99 languages
• Transcribes audio and video content into text with near-human accuracy
• Processes noisy and accented speech
• Timestamp generation for subtitles
• MIT license means no limits on commercial application

Limitations: Whisper is not optimized for speaker diarization (identifying who spoke when). Also, it struggles with highly technical jargon without fine-tuning. Real-time transcription requires smaller, less accurate Whisper models.

Availability on Okara: No, Whisper is not currently supported by the platform.

CLIP

CLIP (Contrastive Language–Image Pretraining) matches images to text descriptions in a single model. Released in 2021, it uses vision transformers and text encoders to understand both. You can use it for zero-shot image classification and image search.

• Parameters: Various ViT-based and ResNet-based versions
• Context Window: 77 text tokens
• Quantization: Community-supported INT8/FP16
• License: MIT
• Release: January 2021

Performance benchmarks: CLIP achieves top-1 accuracy 59.2% on “in the wild” celebrity image classification from 100 options and 43.3% on 1000 options. The model reaches ResNet-50’s 76.2% accuracy on standard ImageNet. It scores 60.2% on ImageNet Sketch and 77.1% on adversarial examples.

Strengths

• Trained on 400M image-text pairs
• No task-specific training needed for many use cases
• Simple to use for visual search and content moderation
• Strong zero-shot performance on ImageNet
• MIT license allows commercial deployment

Limitations: It is not a generative model; therefore does not create images or text. Newer multimodal models perform considerably better for many tasks.

Availability on Okara: Not available on Okara at the moment.

GPT-2

Back in 2019, GPT-2 made headlines as the model “too dangerous to release.” Initially, OpenAI withheld the full version, citing misuse concerns. GPT-2 started the modern AI boom. Today, it is outdated for most production tasks and falls behind most tiny, modern models.

• Parameters: 117M to 1.5B
• Context Window: 1,024 tokens
• Quantization: widely supported (ONNX, GGML, etc.)
• License: MIT
• Release: February 2019

Performance benchmarks: GPT-2 scored 70.70% accuracy on the Winograd Schema Challenge. It achieved a BLUE score of 5 on the WMT-14 English-French test.

Strengths

• Tiny footprint; runs on modest hardware
• Perfect for lightweight text generation tasks
• Academic research on early transformer architecture
• MIT license for maximum flexibility

Limitations: It hallucinates frequently, has a tiny context window, and produces outdated text outputs compared to modern models.

Availability on Okara: Not available.

Other Worth Noting Models

OpenAI also open-sourced a few creative tools, but they are not practical for most 2026 use cases:

• Jukebox (2020): Jukebox was truly groundbreaking for its time. It generates original music in different genres and styles. The model is incredibly slow, hard to use, and requires enormous compute. In 2026, newer models like Lyria have largely taken over this space.
• Point-E (2022): Point-E turns text prompts into 3D point clouds. It is fun for early 3D experiments but requires significant post-processing for use in games and films. Modern 3D generation systems like Meshy and DreamFusion create better meshes.
• Shap-E (2023): Shap-E is similar to Point-E, but slightly more advanced. It produces textured 3D objects using text or image input. Shap-E is still useful in niche research, but consumer-grade 3D generators are more useful today.

Are GPT-4, GPT-4o, or GPT-5 Open Source?

No, GPT-4, GPT-4o, and the recently released GPT-5 are not open source. Their weights are proprietary and have never been publicly released. This means you can not download, self-host, or modify them directly.

These models are accessible through OpenAI’s official API, ChatGPT, and approved partner platforms. Open-weight access allows you to run the model on your own hardware with full control over data and cost. API access means you send the data to OpenAI’s servers and pay per use.

What the GPT-oss Release Actually Means

The release of gpt-oss duo represents a genuine shift in OpenAI’s response to open models. Here is what changed:

• Training approach: Both models were trained using reinforcement learning techniques and insights from other OpenAI models, including o3. These were the first open-weight LLMs they have shipped since GPT-2.
• Adjustable reasoning effort: This feature allows models to spend more or less “thinking time” on problems. You can adjust the reasoning effort to low, medium, or high depending on the queries. Low effort = faster responses for simple tasks. High effort = deeper Chain-of-Thought for complex problems. This flexibility was not available in earlier open releases.
• MXFP4 quantization: This 4-bit microscopic floating-point format (MXFP4) makes the 120b version fit on a single 80 GB GPU, and the 20b variant runs on a laptop.
• Apache 2.0 License: Some open models (looking at you llama) allow restrictive commercial use. gpt-oss uses a permissive license, Apache 2.0. This means you can use it commercially, modify it, and distribute your work without paying royalties to OpenAI. Businesses can build products on top of these models without open-sourcing their code.

How OpenAI's Open Models Compare to the Rest of the Field

Here's how OpenAI competes against leading open models in 2026

vs. Llama 4: Meta’s Llama 4 (Scout/Maverick) has large context windows and multimodal support. This is slightly more performant on general-purpose reasoning and long-context understanding. It also uses fewer resources and has a large ecosystem of community fine-tunes. In contrast, gpt-oss matches or beats many similarly sized models on code, math, and health-related tasks.

vs. DeepSeek V3.2: DeepSeek has a cost-efficient design and surpasses OpenAI's offerings in math and code. That said, DeepSeek’s licensing is more restrictive for commercial use. On the other hand, gpt-oss has better instruction following and safety alignment.

vs. Mistral Large 3: Mistral Large 3 competes closely with gpt-oss 120b on many benchmarks. It focuses on enterprise features and long-context handling. Plus, Mistral may have better European language support. On the contrary, OpenAI’s models show better English performance and coding capabilities. gpt-oss’s adjustable reasoning efforts make it well-suited for adaptive workflows.

vs. Qwen 3: Alibaba’s Qwen 3 excels in multilingual and Asian language tasks. Its 235B model competes with 120b on several benchmarks at similar sizes. However, got-oss has better documentation and safety features for Western developers.

Which OpenAI Open Source Model Should You Actually Use

Choosing the best open-source OpenAI models depends on the use case:

Choose gpt-oss-120b when:

• You need complex reasoning or multi-step tool use
• You have access to an 80 GB GPU (or use Okara)
• You plan to fine-tune the base model for specialized tasks

Choose gpt-oss-20b when:

• You are building applications for edge devices
• You are deploying a model in a resource-constrained environment
• You are prototyping or want fast iteration
• Your tasks do not demand deep reasoning

Choose Whisper when:

• You need a reliable speech-to-text in multiple languages
• You are building voice interfaces or transcription tools
• You need to transcribe audio and videos

Choose CLIP when:

• You are prototyping multimodal applications
• You need zero-shot visual understanding
• You are building image search or classification systems

Choose GPT-2 when:

• You are teaching or learning about transformer architecture
• You are researching old model behaviors for comparison
• You need a tiny model for an embedded system

Using the Top OpenAI Open-Source Models at Okara.ai

You do not have to configure GPU, quantization, and updates alone. At Okara.ai, you can use OpenAI's open-weight models, gpt-oss 120b and gpt-oss 20b, on secure, private infrastructure.

Chat with more than 30 AI models without leaving the platform. Switch between open models mid-conversation to compare their outputs. Above all, your data stays encrypted and is never used to train external systems.

Try Okara today for flexible, secure access to open models.

Frequently Asked Questions

Does OpenAI have open-source models?

Yes, but only a few. Their open-weight catalog includes gpt-oss (120b and 20b), Whisper, CLIP, GPT-2, and several specialized models like Jukebox, Point-E, and Shap-E.

What is the difference between gpt-oss-120b and gpt-oss-20b?

gpt-oss 120b has 117B total parameters (5.1b active) for handling complex reasoning, advanced coding, and difficult problems. On the other hand, gpt-oss-20b has 21B total parameters (3.6B active) for edge deployment. The duo shares the same architecture, context window, license, and features like adjustable reasoning.

Is gpt-oss-120b as powerful as GPT-4?

No, GPT-4 and GPT-5 are more capable models for multimodal tasks and creative writing. gpt-oss’s performance is comparable to GPT-4 on certain reasoning tasks.

Is GPT-2 still worth using in 2026?

It is only useful for learning, research, or ultra-lightweight demos. Modern models like gpt-oss are vastly superior and outperform it on every practical task.

What license do OpenAI's open source models use?

gpt-oss models use Apache 2.0, a permissive license for commercial use. Older models like Whisper, CLIP, and GPT-2 use a similarly permissive license, MIT.

Can I run OpenAI open-source models locally?

Yes, gpt-oss 120b needs ~80GB GPU memory, and the 20b version can run on 16GB systems. Whisper and CLIP have smaller variants for consumer hardware.

How do OpenAI's open models compare to Llama and DeepSeek?

gpt-oss excels in instruction following, English reasoning, and tool use. In contrast, DeepSeek wins on coding and cost-efficiency. Llama 4 has broad community support and more fine-tuning resources.