An open-weight model from ServiceNow that redefines expectations with top-tier intelligence, blazing-fast performance, and an exceptionally large context window, all at a groundbreaking price point.
Emerging from the enterprise AI labs at ServiceNow, Apriel-v1.5-15B-Thinker is a formidable open-weight model that has quickly distinguished itself in a crowded field. It strikes a rare and powerful balance between raw intellectual capability, processing speed, and cost efficiency. With a parameter count of 15 billion, it occupies a sweet spot, delivering sophisticated reasoning without the heavyweight infrastructure demands of much larger models.
What truly sets Apriel-v1.5-Thinker apart is its exceptional performance profile. It ranks among the top echelon of models in our Artificial Analysis Intelligence Index, scoring a 52—double the average of its peers. This intelligence is paired with remarkable speed, clocking in at over 146 tokens per second, ensuring that its powerful insights are delivered without delay. Furthermore, its ability to process both text and images, combined with a massive 128,000-token context window, unlocks new possibilities for complex, long-form analysis and multimodal applications.
Perhaps most strikingly, this model is currently offered at a price of zero dollars per million tokens for both input and output on select platforms. This disruptive pricing makes it an incredibly attractive option for developers, researchers, and businesses looking to experiment with or deploy advanced AI without the typical cost barriers. However, its tendency towards high verbosity is a key characteristic that users must learn to manage to harness its full potential effectively.
52 (5 / 84)
146.4 tokens/sec
0.0 $/1M tokens
0.0 $/1M tokens
110 M tokens
0.16 seconds
| Spec | Details |
|---|---|
| Model Parameter Size | 15 Billion (15B) |
| Context Window | 128,000 tokens |
| Creator / Owner | ServiceNow |
| License | Open License (Specific terms may apply) |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Primary Architecture | Transformer-based |
| Typical Use Cases | Complex reasoning, document analysis, multimodal understanding, conversational AI |
The performance and cost of running Apriel-v1.5-15B-Thinker can vary significantly depending on the API provider you choose. Factors like hardware allocation, server load, and pricing models all play a role. Below is a comparison based on our benchmarks and hypothetical scenarios to illustrate these differences.
| Priority | Pick | Why | Tradeoff to accept | ||
|---|---|---|---|---|---|
| Together.ai | $0.00 / 1M | $0.00 / 1M | 146.4 tokens/s | 0.16s | Excellent balance of speed and cost, making it the top choice based on current data. |
| Hypothetical Budget Provider | $0.10 / 1M | $0.10 / 1M | ~90 tokens/s | ~0.45s | A lower-cost paid option that may offer slower performance and higher latency. |
| Hypothetical Performance Provider | $0.30 / 1M | $0.30 / 1M | ~180 tokens/s | ~0.12s | A premium option focused on maximizing speed and minimizing latency for mission-critical applications. |
| Self-Hosted (On-Prem) | N/A (Upfront Hardware Cost) | N/A (Ongoing Operational Cost) | Variable | Variable | Offers maximum control and privacy but comes with high initial investment and maintenance overhead. |
Note: Performance metrics and pricing are subject to change. The data for Together.ai is based on recent benchmarks. Hypothetical providers are included for illustrative purposes. Always check the latest pricing and terms directly with the provider.
Theoretical benchmarks are useful, but a model's true value is revealed when applied to real-world tasks. We've evaluated Apriel-v1.5-15B-Thinker across several common workloads to assess its practical strengths and weaknesses.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Real-Time Customer Support Chatbot | A | Excellent fit. Low latency ensures instant replies, and high intelligence provides accurate answers. Verbosity needs to be managed with careful prompting to keep responses chatbot-friendly. | ||
| Legal Document Review & Summarization | A+ | Ideal use case. The massive 128k context window can ingest entire contracts or depositions, while its high intelligence can accurately identify key clauses and summarize complex information. | ||
| Code Generation & Debugging | A- | Very strong. Its intelligence helps in understanding complex logic and generating accurate code. High verbosity can sometimes lead to overly commented or unnecessarily long code snippets. | ||
| Marketing Copy & Blog Post Drafting | B+ | Highly capable of generating creative and well-structured content. Its verbosity means first drafts might be long and require editing to achieve a punchy, concise tone. | ||
| Visual Q&A (Image Analysis) | A | Performs well due to its multimodal input capabilities. It can analyze an image and provide detailed, text-based answers and descriptions based on its content. | ||
| RFP/Grant Proposal Analysis | A+ | A perfect match. The model can process the entirety of a long Request for Proposal document within its context and help draft comprehensive, relevant responses by cross-referencing all sections. | ||
| Scientific Research Paper Analysis | A | Strong performance in parsing dense, technical language. The large context window is a major asset for understanding the full scope of a research paper and its citations. |
Apriel-v1.5-15B-Thinker excels in any task that demands a combination of deep understanding and a large information capacity. It is a top-tier choice for professional applications involving long, complex documents, such as legal, financial, and academic analysis. Its speed and low latency also make it a premier candidate for sophisticated, real-time conversational agents, provided its verbose nature is properly managed through prompt design.
While Apriel-v1.5-Thinker is currently very affordable, smart usage strategies can help you maximize its value and prepare for any future pricing changes. Here are several tactics to keep your costs low and your efficiency high.
The model's biggest hidden cost is its verbosity. Mitigate this by adding explicit instructions to your prompts. Use phrases like 'Be concise,' 'Answer in one paragraph,' 'Use bullet points,' or 'Limit the response to 100 words.' This simple step can dramatically reduce output token count.
Take full advantage of the current $0.00 pricing on providers like Together.ai for development, testing, and even production workloads. Be sure to read the provider's fair use policy and understand any rate limits or usage caps associated with the free offering.
Instead of making multiple API calls with small pieces of information, consolidate tasks. Use the 128k context window to provide all necessary documents, history, and instructions in a single call. This is often more efficient than a 'chain' of smaller prompts.
For frequently asked questions or repeated requests, implement a caching layer. If a new prompt is identical or very similar to a previous one, you can serve the cached response instead of making a new API call, saving both time and potential cost.
For non-interactive tasks like document summarization or data analysis, group requests together and send them as a batch. This can be more efficient from a processing standpoint and simplifies cost tracking.
The AI provider landscape is dynamic. Keep an eye on the pricing for Apriel-v1.5-Thinker across different platforms. A provider that is free today may not be the cheapest tomorrow. Be prepared to migrate workloads if a more cost-effective option appears.
Before sending data to the model, especially in automated workflows, clean and condense it. Remove boilerplate text, irrelevant formatting, or redundant information to reduce the number of input tokens you need to send, which can save costs if pricing ever changes.
Apriel-v1.5-15B-Thinker is a 15-billion parameter, open-weight large language model developed by ServiceNow. It is known for its high intelligence, fast processing speed, large context window, and multimodal (text and image) capabilities.
On certain API providers benchmarked by Artificial Analysis, such as Together.ai, the model is currently available at a price of $0.00 per million input and output tokens. This may be a promotional offer or part of a free tier, and pricing is subject to change.
The '15B' stands for 15 billion, which is the number of parameters in the model. Parameters are the variables the model learns during training and are a rough indicator of its complexity and potential capability. 15B is considered a medium-sized model.
A 128,000-token context window allows the model to process and 'remember' a very large amount of text in a single prompt—roughly equivalent to a 250-page book. This is incredibly useful for analyzing long documents, maintaining extended conversations, or performing complex tasks that require a lot of background information.
Its primary strengths are its elite-level intelligence (ranking #5 of 84 models), high output speed (146.4 tokens/sec), massive 128k context window, and exceptional cost-effectiveness on current platforms. Its ability to process images is also a key advantage.
High verbosity means the model tends to provide very long, detailed, and comprehensive answers by default. While this can be good for in-depth explanations, it may require users to write more specific prompts to request shorter, more concise responses for certain applications.
Yes. The model is multimodal, meaning it can accept both text and images as input. You can provide it with an image and ask questions about it, request descriptions, or perform other forms of visual analysis.
Apriel-v1.5-Thinker is highly competitive. It often outperforms other open-source models in its size class on intelligence benchmarks and offers a significantly larger context window than many alternatives. Its combination of speed, intelligence, and a massive context window makes it a standout choice.
