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OpenAI
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3 models · Opus, Sonnet, Haiku
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5 models · Gemini 3.1, Veo 3
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xAI (Grok)
2 models · Grok-4-1, Grok-Imagine
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3 models · V3, V3.2, R1
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2 models · Maverick, Scout
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2 models · Qwen3-Max, Coder
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2 models · Devstral 2, Medium 3.1
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4 models · Nemotron Ultra, Nano
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3 models · GLM-5, 4.7, 4.6V
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Flagship Model Comparison
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Side-by-side view of the leading models across all major labs. Input/Output prices per 1M tokens.
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* Prices shown are approximate. Free self-hosted models exclude infrastructure costs. Beta pricing may change.
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🆕 Just Released
Anthropic
Claude Opus 4.6 & Sonnet 4.6
Adaptive Thinking and 1M token context (beta) mark a major leap in agent capability. Now the most intelligent Claude for coding and agentic tasks.
🔥 Hot
Google DeepMind
Gemini 3.1 Pro — Thought Signatures
Thought Signatures bring new transparency to deep reasoning. 5M context window makes it the go-to for ultra-long document analysis.
🤖 Agent Use
OpenAI
GPT-5.4 — Native agent use
GPT-5.4 introduces native agent use, letting it operate browsers, apps, and files autonomously with improved reasoning efficiency.
⚡ Real-Time
xAI
Grok-4-1 Fast — 4-Agent Architecture
Grok's 4-agent architecture with real-time X (Twitter) data access and 2M context makes it unique for real-time analysis tasks.
🔓 Open Source
Meta
Llama 4 Maverick — 400B MoE
Meta's 400B Mixture-of-Experts model with native multimodal understanding. Free to self-host with a full commercial licence.
💻 Coding
Mistral
Devstral 2 — Fastest Coding Agent
Mistral's coding agent with 256K context, multi-file edits, and codebase navigation. The fastest software engineering model available.
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Llama 4 Maverick, Llama 4 Scout, DeepSeek-V3, DeepSeek-R1 — self-host with zero API cost.
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Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, GPT-5.4, Qwen3-Max.
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Claude Opus 4.6, Sora 2 Pro, gpt-image-1.5 — top-tier quality for demanding workloads.
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Research Agent
Automates web research and generates structured reports.
GPT-5.4Web search
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Handles tickets, FAQs, and escalates complex issues.
GPT-5.4Ticketing
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Code Review Agent
Reviews PRs, flags bugs, and suggests improvements.
Claude Opus 4.6GitHub
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GeminiSheets
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Content Writer Agent
Creates blog posts and marketing copy with brand voice.
Claude Opus 4.6Marketing
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GPT-4o
by OpenAI · Multimodal flagship model
Live
Description
GPT-4o is OpenAI's flagship multimodal model combining text, vision, and audio in one unified architecture. It achieves state-of-the-art performance across language understanding, reasoning, and code generation tasks.
Input / Output
Input: Text, images, audio, PDFs Output: Text, code, structured data Context: 128K tokens Max output: 4,096 tokens Latency: ~1.2s avg
Use Cases
✍️
Content writing
💻
Code generation
🔍
Document analysis
🌐
Translation
🎓
Education
📊
Data analysis
Example Prompt → Output
User
"Summarize this research paper in 3 bullet points and suggest 2 follow-up questions."
GPT-4o
• The paper introduces a new attention mechanism reducing compute by 40% • Results on MMLU show 3.2% improvement over baseline • Authors release code and weights under MIT license
Follow-up questions: 1. How does this scale to 100B+ parameter models? 2. What are the trade-offs at inference time?
Benchmark Scores
87.2MMLU
90.2HumanEval
76.6MATH
4.7⭐Rating
How to Use This Model
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1
Get API Access
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Choose your integration method
Options: (a) NexusAI REST API — simple HTTP requests from any language, (b) Official SDK — Python, Node.js, Go packages available, (c) No-code — use the built-in Playground or connect via Zapier / Make.
This model accepts text, images, and PDFs as input. Outputs are text and structured JSON. The context window is 128K tokens — roughly 96,000 words. For long documents, consider chunking content into sections.
4
Set parameters for your use case
Key parameters: temperature (0 = deterministic, 1 = creative), max_tokens (controls output length), system (sets model persona and behaviour). Start with temperature 0.3–0.7 for most applications.
5
Test in the Playground first
Before writing code, iterate on your prompt in the built-in Playground. Test edge cases, adjust tone and format, then copy the final prompt into your integration.
Pro tip: Start with a small free-tier experiment. Build a minimal working version, measure quality and latency, then scale. Most production apps iterate through 3–5 prompt versions before going live.
Choose the plan that fits your usage. All plans include API access, documentation, and community support.
Pay-per-use
$5
per 1M input tokens
No monthly commitment
$15 per 1M output tokens
128K context window
Rate: 500 RPM
Standard support
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$49
per month
$3 per 1M input tokens
$9 per 1M output tokens
128K context window
Rate: 3,000 RPM
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Dedicated CSM
Free tier available: Get 100K tokens/month at no cost. Perfect for prototyping and exploration. No credit card required to get started.
Prompt Engineering for GPT-4o
Well-crafted prompts dramatically improve model output quality. Follow these principles to get the best results every time.
Principle 1 — Be explicit about format
Summarize the following text in exactly 3 bullet points.
Each bullet should be one sentence, under 20 words.
Text: {your_text_here}
Principle 2 — Assign a role
You are a senior software engineer specializing in Python.
Review the following code for bugs, performance issues,
and style violations. Be concise and actionable.
Code: {your_code_here}
Principle 3 — Chain-of-thought for complex tasks
Solve this step by step, showing your reasoning at each stage.
Problem: {your_problem_here}
Think through: assumptions → approach → calculation → answer
Principle 4 — Few-shot examples
Classify customer sentiment. Examples:
Input: "Shipping was fast!" → Output: positive
Input: "Product broke after a day." → Output: negative
Input: "It's okay, nothing special." → Output: neutral
Now classify: "{new_review_here}"
Pro tips: Always specify the desired output length. Use delimiters like triple backticks to separate instructions from content. For JSON output, include a sample structure in the prompt.
Create an Agent with GPT-4o
Follow these steps to build a powerful AI agent in under 10 minutes.
1
Define your agent's purpose
Clearly state what your agent should do. Example: "A customer support agent that answers product questions, escalates billing issues, and creates Jira tickets for bugs."
2
Write the system prompt
The system prompt defines the agent's persona, scope, and behaviour. Be explicit about what the agent should and shouldn't do. Include tone, response length, and escalation rules.
3
Connect tools & APIs
Equip your agent with tools: web search, database lookup, email sender, calendar API, Slack webhook. GPT-4o supports function calling — define your tools in JSON schema format.
4
Set up memory
Configure short-term (conversation history) and long-term memory (vector store). This lets the agent remember user preferences and important context across sessions.
5
Test & iterate
Run the agent through 20+ test scenarios covering edge cases. Refine the system prompt based on failures. Use our Agent Playground to debug and tune before deployment.
6
Deploy & monitor
Get a shareable endpoint or embed widget. Monitor performance in the NexusAI dashboard — track response quality, latency, token usage, and user satisfaction scores in real time.
4.7
★★★★★
2,847 reviews
5
72%
4
20%
3
6%
1-2
2%
Sarah K.
ML Engineer at Stripe
★★★★★
Mar 2025
GPT-4o has transformed our document processing pipeline. The vision capabilities are remarkably accurate — it handles complex financial statements and extracts structured data with minimal post-processing. Latency is excellent for our use case.
Tariq M.
Founder, EdTech Startup
★★★★☆
Feb 2025
Impressive reasoning and creative capabilities. We use it for personalised learning content and student feedback. The main downside is cost at scale — the Pro subscription helps but enterprise pricing is where it becomes truly cost-effective.
Priya N.
Senior Developer at Shopify
★★★★★
Feb 2025
Best coding model we've used. Code review, refactoring suggestions, and debugging explanations are outstanding. The function calling is reliable and JSON mode outputs are always well-structured. Highly recommend for developer tooling.
Available model versions and their deployment status.
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