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This document provides a detailed comparison of the Large Language Models available within the CometAI platform, including those developed by Anthropic, OpenAI, Mistral, and Meta. Each model is analyzed based on its design purpose, technical specifications, pricing, and optimal use cases to help users make informed decisions based on their unique needs.
For step-by-step instructions on how to choose and switch between models, refer to Model Selection in CometAI.
Claude 3 Haiku
Design Purpose: Claude 3 Haiku generates 65.2 tokens per second and has a latency of 0.70 seconds, making it the fastest model in Claude's lineup, designed for high-throughput applications requiring quick responses.
Key Specifications:
- Parameters: ~7-8B (estimated)
- Context Window: 200,000 tokens
- Pricing: $0.25 per million input tokens, $1.25 per million output tokens
- Speed: 123.1 tokens per second and a latency of 0.71 seconds
Optimal Use Cases: Real-time customer support, content moderation, simple text classification, and high-volume processing tasks where speed is prioritized over complex reasoning.
Claude 3 Opus
Design Purpose: Claude 3 Opus is the most intelligent member of the Claude 3 family. It performs well on highly complex tasks, designed for the most demanding cognitive tasks requiring deep analysis.
Key Specifications:
- Parameters: ~175B (estimated)
- Context Window: 200,000 tokens
- Pricing: $15.00 per million input tokens and $75.00 per one million output tokens
- Speed: Slower but highest quality output
Optimal Use Cases: Complex research analysis, advanced creative writing, sophisticated reasoning tasks, legal document analysis, and strategic planning.
Claude 3 Sonnet
Design Purpose: Mid-tier balance between intelligence and efficiency.
Key Specifications:
- Parameters: ~65B (estimated)
- Context Window: 200,000 tokens
- Pricing: $3.00 per million input tokens, $15.00 per million output tokens
- Speed: 66.9 tokens per second and a latency of 0.72 seconds
Optimal Use Cases: Educational content creation, business analysis, coding assistance, and general-purpose applications requiring balanced performance.
Claude 3.5 Haiku
Design Purpose: Enhanced version of Haiku with improved capabilities while maintaining speed advantages.
Key Specifications:
- Parameters: ~8-10B (estimated)
- Context Window: 200,000 tokens
- Pricing: $0.80 per million input tokens, $4.00 per million output tokens
- Performance: scores 40.6% on SWE-bench Verified [1], outperforming many agents using publicly available state-of-the-art models
Optimal Use Cases: Enhanced customer support, improved content generation, coding assistance for simpler tasks.
Claude 3.5 Sonnet
Design Purpose: Claude 3.5 Sonnet operates at twice the speed of Claude 3 Opus while delivering superior performance across multiple benchmarks.
Key Specifications:
- Parameters: over 175 billion parameters
- Context Window: 200,000 tokens
- Pricing: $3.00 per million input tokens, $15.00 per million output tokens
- Benchmarks: Claude scored 59.1% on the GPQA (Graduate Problem Solving and Question Answering) benchmark, 92.6% on the MGSM (Multilingual Grade School Math Benchmark) benchmark, and 78.4% on the MMLU Pro (Massive Multitask Language Understanding) benchmark [2].
Optimal Use Cases: Advanced coding projects, research assistance, complex analytical tasks, educational tutoring.
Claude 3.5 Sonnet v2
Design Purpose: the upgraded Claude 3.5 Sonnet model (which we can consider as v2) is superior to its predecessor (v1) in terms of features and performance, while maintaining the same cost.
Key Specifications:
- New Features: Computer use capabilities (in public beta): This allows Claude to perceive and interact with computer interfaces, including viewing screens, moving cursors, clicking buttons, and typing text. Improved coding abilities
- Performance: Enhanced across all benchmarks
- Pricing: Same as v1
Optimal Use Cases: Automated workflows, advanced coding assistance, computer-based task automation.
Claude 3.7 Sonnet
Design Purpose: Claude 3.7 Sonnet is both an ordinary LLM and a reasoning model in one: you can pick when you want the model to answer normally and when you want it to think longer before answering.
Key Specifications:
- Dual Mode: Standard and extended thinking modes
- Benchmarks: achieves state-of-the-art performance on SWE-bench Verified [1], which evaluates AI models' ability to solve real-world software issues
- Pricing: $3.00 per million input tokens, $15.00 per million output tokens
- Extended Thinking: Up to 128K reasoning tokens
Optimal Use Cases: Complex problem-solving, advanced mathematics, competitive programming, detailed research analysis.
GPT-4.1
Design Purpose: GPT4.1 is a structured, API-only workhorse built for developers: Great at tight instruction following and long context memory.
Key Specifications:
- Context Window: supports an extended context window up to 1 million tokens
- Output Limit: can generate up to 32,768 tokens per request
- Performance: scoring 54.6% on SWE-Bench Verified [1], and demonstrates superior instruction following with a 10.5% improvement over GPT-4o on MultiChallenge
Optimal Use Cases: Large document processing, detailed instruction following, enterprise applications requiring precise control.
OpenAI o3
Design Purpose: OpenAI o3 and o4-mini are the most intelligent models we have ever released, designed for complex reasoning and agentic tool use.
Key Specifications:
- Tool Integration: our reasoning models can agentically use and combine every tool within ChatGPT—this includes searching the web, analyzing uploaded files and other data with Python, reasoning deeply about visual inputs, and even generating images
- Mathematics: o3 scored 85.3% accuracy on the American Invitational Mathematics Examination (AIME) 2025 competition in math [3].
- Science Performance: On GPQA Diamond, a benchmark testing Ph.D.-level science questions, o3 scored 83.6% [3].
Optimal Use Cases: Complex mathematical problems, scientific research, multi-step reasoning tasks, agentic applications.
o3-mini
Design Purpose: Pushing the frontier of cost-effective reasoning, optimized for STEM tasks with three reasoning levels.
Key Specifications:
- Reasoning Levels: Low, medium, and high effort options
- Performance: supports key developer features including function calling, structured outputs, and developer messages
- Cost-Effectiveness: reducing per-token pricing by 95% since launching GPT4 [4]
Optimal Use Cases: Educational STEM applications, cost-sensitive reasoning tasks, bulk processing with reasoning requirements.
o4-mini
Design Purpose: OpenAI o4-mini is a smaller model optimized for fast, cost-efficient reasoning—it achieves remarkable performance for its size and cost, particularly in math, coding, and visual tasks.
Key Specifications:
- Mathematics Excellence: It is the best-performing benchmarked model on AIME 2024 and 2025 [5]
- Pricing: $1.10 per million input tokens, $4.40 per million output tokens
Optimal Use Cases: Mathematical computation, coding assistance, educational applications, cost-effective reasoning tasks.
Mistral 7B
Design Purpose: Mistral 7B works with around 7 billion parameters and serves the ideal blend between language understanding abilities and computational efficiency.
Key Specifications:
- Parameters: 7.3 billion
- Architecture: utilizes innovative techniques like Grouped-Query Attention (GQA) for improved inference speed and Sliding Window Attention (SWA) to manage lengthy sequences efficiently [6]
- License: Apache 2.0
- Performance: outperforms the 13 billion parameters Llama 2 model across all benchmarks [6]
Optimal Use Cases: answering questions, generating outlines, or interpreting text, resource-constrained environments, local deployment.
Mistral Large
Design Purpose: Enterprise-grade model for complex reasoning and multilingual tasks.
Key Specifications:
- Multilingual: Supports English, French, German, Spanish, and Italian
- Performance: Approaches GPT-4 level performance [7]
- Pricing: $4.00 per million input tokens, $12.00 per million output tokens
Optimal Use Cases: Enterprise applications, multilingual content generation, complex reasoning tasks, international business applications.
Mixtral 8x7B
Design Purpose: Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license [8].
Key Specifications:
- Architecture: Sparse Mixture of Experts (SMoE)
- Parameters: 47B parameters, but only 13B parameters are active at any given time
- Context Window: 32,000 tokens
- Languages: English, French, Italian, German and Spanish
Optimal Use Cases: classification, customer support, text generation, code generation, multilingual applications.
Llama 3.2 90B Instruct
Design Purpose: Meta designed this model to support image reasoning use cases, such as document-level understanding including charts and graphs, captioning of images, and visual grounding tasks.
Key Specifications:
- Parameters: 90 billion
- Multimodal: Text + Vision capabilities
- Context Window: 130k tokens
- Training Data: pre-trained on 6B image and text pairs
- MMLU Score: 0.671
- Pricing: $0.54 per 1M Tokens (blended 3:1)
Optimal Use Cases: document-level understanding including charts and graphs, captioning of images, and visual grounding tasks, educational content with visual elements, research involving visual data analysis.
|
Model
|
Provider
|
Parameters
|
Context Window
|
Input Price ($/1M tokens)
|
Output Price ($/1M tokens)
|
Key Strengths
|
Best For
|
|
Claude 3 Haiku
|
Anthropic
|
~8B
|
200K
|
$0.25
|
$1.25
|
Speed, efficiency
|
High-volume tasks
|
|
Claude 3 Opus
|
Anthropic
|
~175B
|
200K
|
$15
|
$75
|
Complex reasoning
|
Research, analysis
|
|
Claude 3 Sonnet
|
Anthropic
|
~65B
|
200K
|
$3
|
$15
|
Balanced performance
|
General purpose
|
|
Claude 3.5 Haiku
|
Anthropic
|
~10B
|
200K
|
$0.80
|
$4
|
Enhanced speed
|
Improved customer support
|
|
Claude 3.5 Sonnet
|
Anthropic
|
~175B
|
200K
|
$3
|
$15
|
Coding, speed
|
Development, education
|
|
Claude 3.5 Sonnet v2
|
Anthropic
|
~175B
|
200K
|
$3
|
$15
|
Computer use
|
Automation
|
|
Claude 3.7 Sonnet
|
Anthropic
|
~175B
|
200K
|
$3
|
$15
|
Highly intelligent
|
Complex problem-solving
|
|
GPT-4.1
|
OpenAI
|
Unknown
|
1M
|
$2.50
|
$10
|
Long context
|
Document processing
|
|
o3
|
OpenAI
|
Unknown
|
200K
|
$60
|
$240
|
Advanced reasoning
|
Research, mathematics
|
|
o3-mini
|
OpenAI
|
Unknown
|
200K
|
$1
|
$4
|
Cost-effective reasoning
|
STEM education
|
|
o4-mini
|
OpenAI
|
Unknown
|
200K
|
$1.10
|
$4.40
|
Math excellence
|
Mathematical computation
|
|
Mistral 7B
|
Mistral
|
7.3B
|
8192
|
$0.15
|
$0.15
|
Efficiency, open-source
|
Low-cost
|
|
Mistral Large
|
Mistral
|
Unknown
|
32K
|
$4.00
|
$12
|
Multilingual, enterprise
|
International business
|
|
Mixtral 8x7B
|
Mistral
|
47B (13B active)
|
32K
|
$0.45
|
$0.70
|
Open-source, efficiency
|
development, multilingual
|
|
Llama 3.2 90B
|
Meta
|
90B
|
128K
|
$0.54
|
$0.54
|
Vision + text
|
Visual analysis
|
|
Use Case
|
Description
|
Recommended Models
|
Reasoning
|
|
Research Paper Analysis
|
Analyzing academic papers, extracting insights, literature reviews
|
Claude 3.7 Sonnet, o3
|
Advanced reasoning capabilities and extended thinking modes excel at complex academic analysis
|
|
Code Education & Debugging
|
Teaching programming, code review, debugging assistance
|
Claude 3.5 Sonnet v2, o4-mini
|
Strong coding performance with computer use capabilities and mathematical reasoning
|
|
Essay Writing & Feedback
|
Student essay assistance, providing feedback, improving writing
|
Claude 3.5 Sonnet, GPT-4.1
|
Excellent writing capabilities with long context for handling full essays
|
|
STEM Problem Solving
|
Mathematics, physics, chemistry problem solving and tutoring
|
o4-mini, Claude 3.7 Sonnet
|
Exceptional mathematical performance and reasoning capabilities specifically designed for STEM
|
|
Language Learning
|
Multilingual conversation practice, translation, cultural context
|
Mixtral 8x7B, Mistral Large
|
Strong multilingual capabilities and cultural understanding across multiple languages
|
|
Data Analysis & Visualization
|
Analyzing research data, creating reports, statistical analysis
|
Claude 3.5 Sonnet, o3
|
Strong analytical capabilities with tool use for data processing and chart interpretation
|
|
Academic Writing Support
|
Grant proposals, academic papers, citation assistance
|
Claude 3 Opus, GPT-4.1
|
Highest quality writing with extensive context windows for large documents
|
|
Interactive Tutoring
|
Real-time Q&A, personalized learning assistance
|
Claude 3.5 Haiku, o3-mini
|
Fast response times with solid reasoning for immediate student support
|
|
Document Processing
|
Handling large syllabi, textbooks, policy documents
|
GPT-4.1, Claude 3.5 Sonnet
|
Million-token context window and strong comprehension for large document analysis
|
|
Visual Content Analysis
|
Analyzing charts, graphs, images in research materials
|
Llama 3.2 90B, Claude 3.5 Sonnet
|
Multimodal capabilities for understanding visual academic content
|
For Budget-Conscious Applications:
- Mistral 7B or Mixtral 8x7B: Open-source options that can be deployed locally, reducing ongoing costs
- o3-mini or o4-mini: Cost-effective reasoning for STEM applications
For High-Performance Research:
- Claude 3.7 Sonnet: Best balance of advanced reasoning and cost
- o3: When maximum reasoning capability is required regardless of cost
For General Educational Use:
- Claude 3.5 Sonnet: Excellent all-around performance for most academic tasks
- GPT-4.1: Best for applications requiring very long context processing
For Specialized Applications:
- Llama 3.2 90B: When visual analysis is crucial
- Mistral Large: For international programs requiring multilingual support
The current model offerings in CometAI provide a versatile foundation for academic, operational, and research use. Claude 3.7 Sonnet stands out for university workflows due to its high performance on writing and reasoning tasks. For cost-effective mathematical and STEM-related applications, OpenAI's o4-mini offers strong performance at a lower price point.
We recommend a diversified approach to model selection: choose Claude models for structured writing and internal documentation, OpenAI models for logic and planning, and Mistral or Meta models where budget or vision capabilities are key. As models continue to evolve rapidly, teams should maintain flexible workflows that adapt to updates within CometAI while ensuring consistency across institutional goals.
[1] SWE-bench. (2025). SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Retrieved from https://www.swebench.com/
[2] Vals AI. (2025). Anthropic Claude-3.5-Sonnet Model Performance Metrics. Retrieved from https://www.vals.ai/models/anthropic_claude-3-5-sonnet-20241022
[3] Vals AI. (2025). OpenAI o3 Model Performance Evaluation. Retrieved from https://www.vals.ai/models/openai_o3-2025-04-16
[4] OpenAI. (2025). OpenAI o3-mini. Retrieved from https://openai.com/index/openai-o3-mini/
[5] Vals AI. (2025). OpenAI o4-mini Model Performance Metrics. Retrieved from https://www.vals.ai/models/openai_o4-mini-2025-04-16
[6] Mistral AI. (2023). Announcing Mistral 7B. Retrieved from https://mistral.ai/news/announcing-mistral-7b
[7] Mistral AI. (2024). Mistral Large 2407. Retrieved from https://mistral.ai/news/mistral-large-2407
[8] Mistral AI. (2023). Mixtral of Experts. Retrieved from https://mistral.ai/news/mixtral-of-experts