2 May 2025
A Breakdown of OpenAI, Anthropic, Google, and Groq Models Thus Far Available in Power Flow
The University of Nicosia is proud to offer faculty and staff access to some of the most powerful AI models available today through its advanced Powerflow tool.
(The information in this article is accurate as of 2/05/2025)
The University of Nicosia is proud to offer faculty and staff access to some of the most powerful AI models available today through its advanced Powerflow tool. This platform grants users direct access to cutting-edge large language models (LLMs), ensuring they can leverage AI for research, education, and professional applications. More models will be added over time, always keeping up with the latest advancements in AI technology.
This post explores the latest models from OpenAI, Anthropic, Google, Grok, Groq-based and Bedrock (AWS) —highlighting their capabilities, use cases, and what makes them stand out in the field of AI.
Data for the benchmarks in this post is sourced from Artificial Analysis.
Below is a concise summary of the five strongest models across OpenAI, Anthropic, Google, Groq, Grok, and Amazon Bedrock, selected for their intelligence, performance, and versatility. Task requirements should guide your choice: for complex reasoning and research workflows, opt for the most powerful models; for routine daily tasks—such as summarization or email drafting—a faster, more cost-efficient model is often preferable.
Most Powerful
| Model Name | Company | Context Window | Price (Approx.) | Key Strengths |
|---|---|---|---|---|
| o3-pro | OpenAI | 200K tokens | Input: 20.00; Output: 80.00 | The most powerful model. Designed to tackle tough problems. The o3-pro model uses more compute to think harder and provide consistently better answers. |
| o4-mini | OpenAI | 200K tokens | Input: 1.10; Output: 4.40 | Optimized for fast, cost-efficient reasoning; excels in math, coding, and visual tasks. |
| Gemini 2.5 Pro | 1M tokens | Input: 1.25; Output: 10.00 | Excels in reasoning, coding, and multimodal tasks; supports text, audio, images, video, and code. | |
| o3 | OpenAI | 128K tokens | Input: 10; Output: 40.00 | Powerful reasoning model that pushes the frontier across coding, math, science, visual perception |
| Grok 3 beta | xAI | 1M tokens | Input 3.00; Output 15.00 | Advanced reasoning, STEM tasks, real-time research, large document processing with excellent scores on benchmarks |
Most Efficient for daily use (sorted from most intelligent)
| Model Name | Company | Context Window | Price (Approx.) | Key Strengths |
|---|---|---|---|---|
| Grok 3 Mini Beta | xAI | 1M tokens | Input 0.30; Output 0.50 | Optimized for speed and efficiency; suitable for applications requiring quick, logical responses with lower computational costs. |
| Gemini 2.0 Flash | 1M tokens | Input: 0.10; Output: 0.40 | Very fast, highly intelligent and very cheap. Optimized for real-time AI interactions, chatbots, and automation. | |
| GPT-4.1 mini | OpenAI | 1M tokens | Input: 0.40; Output: 1.60 | Budget-friendly for educational tools, basic automation, and medium-complexity writing tasks. |
| Nova Lite | Amazon Bedrock | 300k tokens | Input: 0.06; Output: 0.24 | Real-time interactions, document analysis, and visual question answering; optimized for speed and efficiency. |
| GPT-4.1 nano | OpenAI | 1M tokens | Input: 0.10; Output: 0.40 | Ultra-cheap for simple classification, data tagging, and light summarization. |
This section provides a detailed evaluation of the leading AI offerings from OpenAI, Anthropic, Google, and other major providers. Each model is analyzed in terms of its context capacity, cost efficiency, and benchmark performance, helping you select the optimal tool for your specific workflow needs. Continuous updates ensure you’re always working with the latest capabilities and pricing information.
OpenAI
OpenAI Models (Ranked by Power)
| Model Name | Context Window | Cost (USD per 1M tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| o3-pro | 200k tokens | Input: 20; Output: 80 | Best for enterprise-scale research synthesis, entire-codebase engineering, and long-horizon strategic planning | The most powerful model. Designed to tackle tough problems. The o3-pro model uses more compute to think harder and provide consistently better answers. |
| o4-Mini | 200k tokens | Input: 1.10; Output: 4.40 | Best for deep scientific reasoning, advanced coding, and high-end math problem solving. | Highest scores from OpenAI on MMLU-Pro, HumanEval, SciCode, and AIME 2024; best OpenAI model currently for general intelligence. |
| o3 | 128k tokens | Input: 10; Output: 40 | Best for deep scientific reasoning, advanced coding, and high-end math problem solving. | Powerful reasoning model that pushes the frontier across coding, math, science, visual perception |
| o3-Mini | 200K tokens | Input: 1.10; Output: 4.40 | Best for deep reasoning, advanced problem-solving, complex coding, and AI research. | High intelligence ranking (63); strong in logic-heavy and analytical tasks. |
| GPT-o1 | 200K tokens | Input: 15.00; Output 60.00 | High-level scientific research, complex problem-solving, and in-depth coding applications. | Strong logical reasoning, but lower intelligence than o3-mini. |
| o1-Mini | 128K tokens | Input: 1.90; Output: 7.60 | Best mix of speed, reasoning, and affordability; ideal for STEM work, writing, and AI-driven projects. | Faster and cheaper than GPT-4o while offering superior intelligence. |
| GPT-4.1 | 1.00M tokens | Input: 2.00; Output: 8.00 | Suitable for coding workflows, general QA bots, and academic tasks with long context needs. | Middle-tier performance in reasoning and coding tasks |
| GPT-4.5 Preview | 130k tokens | Input: 75; Output: 150 | Rarely recommended due to high cost with minimal benefit over GPT-4.1 | Performs nearly identically to GPT-4.1 across reasoning and coding tasks but at a significantly higher price. Offers no meaningful advantage and is not optimized for production use. |
| GPT-4.1 mini | 1.00M tokens | Input: 0.40; Output: 1.60 | Budget-friendly for educational tools, basic automation, and medium-complexity writing tasks. | Middle-tier performance in reasoning and coding tasks. Faster than GPT-4.1 |
| GPT-4o | 128K tokens | Input: 3.00; Output: 12.00 | Best for multimodal tasks (text, images, and audio); useful for language translation and chatbots. | Good general-purpose model but not the best in raw intelligence or reasoning. Pricey as well. Second most expensive model after GPT-o1 |
| GPT-4.1 nano | 1M tokens | Input: 0.10; Output: 0.40 | Ultra-cheap for simple classification, data tagging, and light summarization. | Lower performance across all benchmarks; designed for low-cost, high-speed tasks. |
| GPT-4o Mini | 128K tokens | Input: 0.15; Output: 0.60 | Budget-friendly AI for customer support, simple chatbots, and content generation. | More affordable than GPT-4o but significantly weaker in intelligence and reasoning. |
Which OpenAI model should you use?
- Use o4-mini for scientific research, advanced coding, and complex problem-solving** — it’s the strongest reasoning model currently available.
- Opt for GPT-4.1 if you need strong coding assistance, academic support, and long context handling at a lower cost.
- Pick GPT-4o if you want a versatile multimodal AI for chatbots, image processing, and translation tasks — at a more affordable rate.
- Use GPT-4.1 nano for the fastest response for simple tasks.
- Use o3-pro for the toughest, largest jobs—it’s the smartest model but also the most expensive, so save it for tasks that truly need its extra power.
Reasoning Parameter in Powerflow
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For o3-mini GPT-o1 and GPT-o3 Pro you can specify a reasoning level of low, medium, or high.
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low: Maximizes speed and conserves tokens, but produces less comprehensive reasoning.
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medium: The default, providing a balance between speed and reasoning accuracy.
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high: Focuses on the most thorough line of reasoning, at the cost of extra tokens and slower responses.
Anthropic
Anthropic Models (Ranked by Power)
| Model Name | Context Window | Cost (USD per 1M Tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| Claude 4 Opus | 200K tokens | Input: 15.00; Output: 75.00 | Excels at coding, with sustained performance on complex, long-running tasks and agent workflows. Use cases include advanced coding work, autonomous AI agents, agentic search and research, tasks that require complex problem solving | Currently the most intelligent anthropic mode. Very expensive. Leading on SWE-bench (72.5%) and Terminal-bench (43.2%) |
| Claude 4 Sonnet | 200K tokens | Input: 3.00; Output: 15.00 | Claude Sonnet 4 significantly improves on Sonnet 3.7's industry-leading capabilities, excelling in coding with a state-of-the-art 72.7% on SWE-bench. | Faster and cheaper than Opus. Only just behind it in benchmarks but by a tiny fraction. |
| Claude 3.7 Sonnet | 200K tokens | Input: 3.00; Output: 15.00 | The latest top-tier Claude model; excels in advanced coding, reasoning, and tasks. | Outperforms previous 3.5 Sonnet in intelligence/coding benchmarks (per new data). |
| Claude 3.5 Sonnet (NEW) | 200K tokens | Input: 3.00; Output: 15.00 | Top-performing Claude model; excels in advanced coding, reasoning, and complex tasks. | Formerly best in code generation and MMLU; now slightly behind 3.7. |
| Claude 3 Opus | 200K tokens | Input: 15.00; Output: 75.00 | Deep analytical reasoning, high-level research, advanced problem-solving. | Previously the most capable Claude, now slightly behind 3.5 Sonnet (New). |
| Claude 3.5 Sonnet | 200K tokens | Input: 3.00; Output: 15.00 | Balanced AI for content creation, translation, and general tasks. | Faster than Opus but less capable than Sonnet (New) in advanced tasks. |
| Claude 3.5 Haiku | 200K tokens | Input: 0.25; Output: 1.25 | Cost-effective, lightweight AI ideal for chatbots and summarization. | Lower power but highly affordable and efficient for simple tasks. |
Which Claude model should you use?
- Claude 4 Opus: For the absolute best coding, math, and complex reasoning tasks based on new data.
- Claude 4 Sonnet: Excels in advanced coding and workflows.
- Claude 3.5 Haiku: Perfect for quick, cost-conscious tasks like chatbots and summarization.
Google Models (Ranked by Power)
| Model Name | Context Window | Cost (USD per 1M Tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| Gemini 2.5 Pro Exp | 1M tokens | Input: 1.25; Output: 10.00 | Best for complex reasoning, coding, and multimodal tasks (text + image). Strong in creative writing and logic-heavy tasks. | Google's most capable public model |
| Gemini 2.0 Pro Exp | 2M tokens | Free for devs (not for production) | Best for enterprise AI, large-scale research, and deep AI applications. | Google’s advanced Gemini model with cutting-edge capabilities. |
| Gemini 2.0 Flash | 1M tokens | Input: 0.10; Output: 0.40 | Optimized for real-time AI interactions, chatbots, and automation. | Lower power but highly efficient for fast-response applications. |
| Gemini 1.5 Pro | 2M tokens | Input: 1.25; Output: 5.00 | Strong for legal, medical, and creative writing applications. | Strong in technical fields. |
| Gemini 2.0 Flash Lite Preview | 1M tokens | Input: 0.07; Output: 0.30 | Mobile AI assistants, compact AI models for small-scale applications. | Optimized for efficiency over power. |
| Gemini Exp 1206 | 2M tokens | Free for devs (experimental) | Designed for AI model testing and internal research applications. | Limited public benchmark data available. |
| Gemini 1.5 Flash | 1M tokens | Input: 0.07; Output: 0.30 | Best for fast, cost-effective summarization, chatbots, and automation. | High-speed performance for efficient processing. |
| LearnLM 1.5 Pro Experimental | Not available | Free for developers (experimental) | Education & learning AI, potentially optimized for tutoring and adaptive learning applications. | No public benchmark data available. |
Which Gemini model should you use?
- Gemini 2.5 Pro: best for complex reasoning, creative writing and high-end applications.
- Gemini 2.0 Pro Exp: great for enterprise AI development, cutting-edge research, and large-scale deep learning projects.
- Gemini Flash variants: ideal for real-time AI interactions, fast automation, high-speed summarization, and cost-effective deployments.
xAI
| Model Name | Context Window | Cost (USD per 1M Tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| Grok 3 Beta | 1M tokens | Input 3.00; Output 15.00 | Advanced reasoning, STEM tasks, real-time research, large document processing | Achieved 93.3% on AIME 2025 and 84.6% on GPQA; Elo score of 1402 on LMArena; trained with 10x compute over Grok 2; excels in long-context reasoning and complex problem-solving. |
| Grok 3 Mini Beta | 1M tokens | Input 0.3; Output 0.5 | Cost-effective reasoning, logic-based tasks, faster response times | Optimized for speed and efficiency; suitable for applications requiring quick, logical responses with lower computational costs. |
| Grok 2 1212 Vision | 1M tokens | Input 2.00 | Visual comprehension, multilingual support | Designed for advanced image understanding, including object recognition and style analysis; enhances visually aware applications. |
| Grok 2 1212 | 128K tokens | Input 2.00; Output 10.00 | Suitable for advanced reasoning & creativity | No official benchmark reported |
Which Grok model should you use?
- Grok 3 Beta: ideal for deep reasoning, research-heavy workflows, complex STEM tasks, and long document understanding.
- Grok 3 Mini Beta: great for cost-effective automation, quick logic-based tasks, and high-speed chatbot applications.
Groq-Based Models
| Model Name | Context Window | Cost (USD per 1M Tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| LLama 3 (8B) | 8K tokens | Input: 0.05; Output: 0.1 | Basic chat, summarization, simpler coding | Similar performance range to GPT-4o Mini or Claude 3.5 Haiku. Smaller context means it’s best for shorter documents. |
| LLama 3 (70B) | 8K tokens | Input: 0.59; Output: 0.79 | More advanced coding & reasoning for mid-level projects | Rival to GPT-4o or Claude 3.5 Sonnet in many tasks, but still capped at 8K tokens. |
| LLama 3.3 (70B, 128K) | 128K tokens | Input: 0.59; Output: 0.79 | Reading lengthy academic papers, detailed Q&A | Comparable in context size to OpenAI o1-mini or Gemini Pro (up to 128K). Slightly behind top OpenAI/Anthropic models in raw intelligence. |
| LLama 3.1 (8B) | 8K tokens | Input: 0.05; Output: 0.08 | Lightweight tutoring or instruction-based chat | Similar to GPT-4o Mini or Claude Haiku but not suitable for large or highly complex tasks. |
| LLama Guard 3 (8B) | 8K tokens | Input: 0.20; Output: 0.20 | Specialized content moderation | Used alongside other models to filter harmful or biased content. |
Which Groq-based model should you use?
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LLaMA 3 (8B) & 3.1 (8B): Ideal for basic chat and tutoring tasks; similar to GPT-4o Mini or Claude Haiku.
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LLaMA 3 (70B): More advanced reasoning/coding but still limited by an 8K token window.
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LLaMA 3.3 (70B, 128K): Best for long documents (128K tokens) if you need a moderate level of coding/logic.
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LLaMA Guard 3 (8B): Use this for content moderation only—complements a primary model to ensure safe outputs.
Bedrock Models
| Model Name | Context Window | Cost (USD per 1M Tokens) | Usefulness (Examples) | Benchmark Notes |
|---|---|---|---|---|
| Nova Pro | 300k tokens | Input: 0.8; Output: 3.2 | Advanced multimodal tasks, including text, image, and video processing; suitable for complex agentic workflows and document analysis. | Achieved competitive performance on key benchmarks, offering a balance between cost and capability. |
| Nova Lite | 300k tokens | Input: 0.06; Output: 0.24 | Real-time interactions, document analysis, and visual question answering; optimized for speed and efficiency. | Demonstrated faster output speeds and lower latency compared to average, with a context window of 300K tokens. |
| Nova Micro | 128k tokens | Input: 0.04; Output: 0.14 | Text-only tasks such as summarization, translation, and interactive chat; excels in low-latency applications. | Offers the lowest latency responses in the Nova family, with a context window of 128K tokens. |
| Titan Text G1 – Lite | 4K tokens | Input: 0.15; Output: 0.20 | Quick writing, summaries, generating simple documents or standard forms | Similar to GPT-4o Mini or Claude 3.5 Haiku. Note the 4K context is much smaller than many OpenAI/Anthropic/Gemini options (up to 200K). |
| Titan Text G1 – Express | 8K tokens | Input: 0.20; Output: 0.60 | Mid-range tasks: summarizing reports, assisting enterprise communication | Closer Gemini Flash in complexity. The 8K context is still smaller than top models’ 128K/200K capacities. |
Which bedrock model should you use?
- Nova Pro is Amazon’s flagship model, offering advanced multimodal capabilities suitable for complex tasks requiring integration of text, image, and video inputs. GPT-4o demonstrated a slight advantage in accuracy but Nova Pro outperforms GPT-4o in efficiency, operating 97% faster while being 65.26% more cost-effective.
- Nova Lite provides a cost-effective solution for tasks requiring real-time processing and document analysis, with a balance between performance and affordability.
- Nova Micro is optimized for speed and low-latency applications, making it ideal for tasks like summarization and translation where quick responses are essential.
- Titan Text G1 – Lite and Express are designed for simpler tasks with smaller context windows, suitable for generating standard documents and assisting in enterprise communications.
Conclusion
With access to these powerful models through the University of Nicosia’s Powerflow tool, faculty and staff can explore new frontiers in AI. Whether for research, writing, or automation, these models provide robust AI solutions tailored to a variety of needs.
Model descriptions compiled by Konstantinos Vassos