A highly intelligent and exceptionally fast open-weight model with a massive context window, balanced by moderate pricing and high verbosity.
gpt-oss-120B (high) emerges as a formidable player in the open-weight large language model landscape, distinguishing itself through a potent combination of elite intelligence and remarkable processing speed. Positioned at the pinnacle of performance benchmarks, it directly challenges not only other open models but also established proprietary systems, offering a compelling alternative for developers seeking top-tier capabilities without being locked into a single ecosystem. Its profile is that of a specialist: a powerful engine for complex thought, delivered at a pace suitable for real-time applications.
On the Artificial Analysis Intelligence Index, gpt-oss-120B (high) achieves a score of 61, placing it at the very top of the 44 models benchmarked and far surpassing the class average of 26. This demonstrates its profound capacity for reasoning, instruction-following, and knowledge retrieval. This intellectual prowess is complemented by its speed. With an average output of 327 tokens per second, it ranks as the fastest model in its class, ensuring that its powerful insights are delivered with minimal latency. This combination makes it uniquely suited for tasks that require both deep thinking and rapid responses.
The model's economic profile is nuanced. The input token price of $0.15 per million is moderate and competitive, sitting comfortably below the average. However, the output token price of $0.60 per million is slightly more expensive than its peers. This pricing structure is amplified by the model's most notable quirk: its extreme verbosity. During testing, it generated 110 million tokens, a staggering figure compared to the 13 million average. This tendency means that without careful prompt engineering to encourage conciseness, output costs can escalate quickly. The total cost to run the intelligence benchmark, $75.96, serves as a practical indicator of its operational expense at scale.
Technically, gpt-oss-120B (high) is equipped with a state-of-the-art 131,000-token context window and knowledge updated to May 2024. This massive context capacity unlocks sophisticated use cases, such as analyzing entire codebases, summarizing lengthy reports, or maintaining long, coherent conversations. It empowers the model to draw connections and maintain context across vast amounts of information, a critical feature for high-stakes, knowledge-intensive applications.
61 (1 / 44)
326.8 tokens/s
$0.15 / 1M tokens
$0.60 / 1M tokens
110M tokens
0.14 seconds
| Spec | Details |
|---|---|
| Model Name | gpt-oss-120B (high) |
| Owner | OpenAI |
| License | Open |
| Parameters | ~120 Billion |
| Context Window | 131,000 tokens |
| Knowledge Cutoff | May 2024 |
| Input Modalities | Text |
| Output Modalities | Text |
| Architecture | Transformer-based |
| Typical Use Cases | Complex reasoning, RAG, summarization, creative writing |
| Fine-tuning Support | Varies by API provider |
Choosing the right API provider for gpt-oss-120B (high) is a critical decision that directly impacts your application's performance and cost. The ideal choice depends on whether you prioritize raw throughput for batch jobs, low latency for interactive use, or the absolute best price for budget-sensitive applications.
The provider market for this model is diverse, with clear leaders in each category. We've analyzed the benchmark data to offer recommendations tailored to different priorities, helping you navigate the trade-offs between speed, responsiveness, and cost.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Blended Cost | Deepinfra | Offers the lowest blended price at $0.10/M tokens, making it the most economical choice for general-purpose or cost-sensitive workloads. | Not the fastest provider for either latency or throughput. |
| Max Throughput | Cerebras | Delivers an astonishing 2942 tokens/second, an order of magnitude faster than most. Ideal for large-scale, offline batch processing. | Significantly higher cost and latency compared to other providers. |
| Lowest Latency | Fireworks | Achieves the best time-to-first-token at just 0.14 seconds, perfect for chatbots and other real-time interactive applications. | Not the cheapest option; throughput is good but not class-leading. |
| Balanced Performance | Together.ai | Provides a great balance with the second-highest throughput (892 t/s) and competitive pricing. A strong all-around choice for many use cases. | Latency is not as low as specialized providers like Fireworks or Groq. |
| Best All-Rounder | Clarifai | Ranks in the top 3 for speed, top 5 for latency, and top 3 for blended price. An excellent, well-rounded option with no major weaknesses. | Not the absolute #1 in any single category, but consistently strong across the board. |
*Provider benchmarks reflect a snapshot in time and can be influenced by factors like server load, geographic location, and specific API configurations. Prices are based on a blend of input and output costs per million tokens. Your own testing is recommended for production workloads.
To understand the real-world cost implications of using gpt-oss-120B (high), let's examine a few common scenarios. These estimates are based on the model's average pricing of $0.15 per 1M input tokens and $0.60 per 1M output tokens. Remember that the model's high verbosity can significantly influence output costs, so these figures assume a reasonably controlled output length.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Summarization | 2,000 tokens | 200 tokens | Summarizing a long email thread for a daily digest. | ~$0.00042 |
| Customer Support Chatbot | 1,500 tokens | 500 tokens | A medium-length support conversation with context. | ~$0.00053 |
| RAG Document Q&A | 20,000 tokens | 300 tokens | Providing a large document snippet for context and asking a question. | ~$0.00318 |
| Blog Post Generation | 100 tokens | 1,500 tokens | Generating a draft article from a brief outline. | ~$0.00092 |
| Code Generation & Refactoring | 4,000 tokens | 4,000 tokens | Providing a block of code and asking for improvements or additions. | ~$0.00300 |
The cost per individual task is low, but the key insight is the cost sensitivity to output length. Due to the higher output price and natural verbosity, tasks that generate extensive text (like blog posts or code) can become more expensive than tasks that process large inputs but produce concise outputs (like RAG Q&A). Managing output token count is the most important factor for cost control.
Given gpt-oss-120B (high)'s specific profile of high intelligence, high speed, and high verbosity, a strategic approach is needed to maximize its value. Implementing a few key practices can help you harness its power for complex tasks while keeping operational costs predictable and under control.
The single most important cost-control measure is managing the model's verbosity. Because output tokens are 4x more expensive than input tokens and the model tends to be wordy, reining in its output is crucial.
Don't stick to a single provider for all tasks. The provider ecosystem is diverse, and you can optimize costs by routing different types of jobs to the most suitable endpoint.
The 131k context window is a powerful tool, but it's also a cost driver. Filling the context window is expensive in terms of input tokens. Use it strategically, not by default.
Many applications receive a high volume of repetitive requests. Instead of calling the API for the same query multiple times, implement a caching layer (e.g., using Redis or a simple in-memory cache).
gpt-oss-120B (high) is a large, 120-billion-parameter, open-weight language model. It is distinguished by its ranking as one of the most intelligent and fastest models available, making it a top choice for demanding AI applications. It was released by OpenAI and has a knowledge cutoff of May 2024.
It is a very strong open-weight competitor to leading proprietary models like GPT-4. It often matches or exceeds them in raw intelligence benchmarks and can be significantly faster. However, proprietary models may have an edge in areas like polish, reduced verbosity, or access to multimodal features. The primary advantage of gpt-oss-120B is its open nature, which provides more transparency, hosting flexibility, and a competitive provider market.
'Open-weight' (or 'open source' in this context) means that the model's parameters, the 'weights' that store its learned knowledge, are publicly released. This is in contrast to closed models, where the weights are a trade secret. Open-weight models can be downloaded, inspected, and run by anyone with the necessary computational resources, fostering greater transparency and innovation.
A model's verbosity is often a byproduct of its training data and the reinforcement learning process (RLHF) used to fine-tune it. The model may have been rewarded during training for providing comprehensive, detailed, and thorough answers. While this can be helpful for explanation, it leads to higher token counts. This behavior can be managed and mitigated by providing explicit instructions for conciseness in your prompts.
The 131k context window is a specialized feature, not a universal benefit. It is incredibly useful for tasks that require the model to process and reason over very long texts, such as legal contract analysis, scientific paper summarization, or maintaining context in a very long conversation. For short, stateless queries (e.g., 'What is the capital of France?'), it provides no advantage and can increase costs if you unnecessarily fill it with data.
The ideal user is a developer, researcher, or business that requires state-of-the-art reasoning and generation capabilities and values high-speed performance. They are building applications like advanced Retrieval-Augmented Generation (RAG) systems, sophisticated creative writing tools, or in-depth data analysis agents. They should be prepared to manage the model's verbosity through careful prompting and to choose their API provider strategically to balance cost and performance.