Side-by-side comparison of 7 AI and LLM APIs. Pricing per token, context windows, speed benchmarks, and real code examples to help you choose.
Last updated: March 2026
An LLM (Large Language Model) API lets you integrate AI text generation, reasoning, and code completion into your applications via HTTP requests. Send a prompt, get back a completion. These APIs power chatbots, coding assistants, content generators, and AI agents.
Customer support bots, internal Q&A, conversational interfaces. Requires fast streaming and context management.
Autocomplete, code review, bug fixing, refactoring. Models need strong reasoning and language-specific knowledge.
Blog posts, marketing copy, product descriptions, social media. Requires creativity and brand voice consistency.
Retrieval-augmented generation for knowledge bases, document Q&A, semantic search. Needs large context windows.
Autonomous agents that use tools, browse the web, execute code. Requires function calling and long-context reasoning.
Structured extraction, summarization, classification, sentiment analysis. Benefits from JSON mode and schema enforcement.
Key differences between AI/LLM APIs at a glance.
| Provider | Top Model | Context Window | Streaming | Function Calling | Vision | JSON Mode | Fine-tuning |
|---|---|---|---|---|---|---|---|
| OpenAI | GPT-4o, o3 | 128K | Yes | Yes | Yes | Yes | Yes |
| Anthropic | Claude Opus 4.6 | 200K | Yes | Yes | Yes | Yes | No |
| Gemini 2.5 Pro | 1M | Yes | Yes | Yes | Yes | Yes | |
| Mistral | Mistral Large | 128K | Yes | Yes | Yes | Yes | Yes |
| Groq | Llama 3.3 70B | 128K | Yes | Yes | Yes | Yes | No |
| Cohere | Command R+ | 128K | Yes | Yes | No | Yes | Yes |
| Together.ai | Llama 3.1 405B | 128K | Yes | Yes | Yes | Yes | Yes |
Detailed breakdown of each AI/LLM API provider.
Make a chat completion request with each provider.
// OpenAI — Node.js import OpenAI from 'openai'; const client = new OpenAI(); const response = await client.chat.completions.create({ model: 'gpt-4o', messages: [ { role: 'system', content: 'You are a helpful assistant.' }, { role: 'user', content: 'Explain quantum computing in 3 sentences.' }, ], max_tokens: 200, }); console.log(response.choices[0].message.content); // "Quantum computing uses quantum bits (qubits)..."
// Anthropic — Node.js import Anthropic from '@anthropic-ai/sdk'; const client = new Anthropic(); const message = await client.messages.create({ model: 'claude-sonnet-4-6', max_tokens: 200, messages: [ { role: 'user', content: 'Explain quantum computing in 3 sentences.' }, ], }); console.log(message.content[0].text); // "Quantum computing harnesses quantum mechanics..."
// Google Gemini — Node.js import { GoogleGenAI } from '@google/genai'; const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY, }); const response = await ai.generateContent({ model: 'gemini-2.5-pro', contents: 'Explain quantum computing in 3 sentences.', }); console.log(response.text); // "Quantum computing leverages superposition..."
// Mistral — Node.js import { Mistral } from '@mistralai/mistralai'; const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY, }); const response = await client.chat.complete({ model: 'mistral-large-latest', messages: [ { role: 'user', content: 'Explain quantum computing in 3 sentences.' }, ], }); console.log(response.choices[0].message.content); // "Quantum computing operates on qubits..."
// Groq — Node.js (OpenAI-compatible) import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.GROQ_API_KEY, baseURL: 'https://api.groq.com/openai/v1', }); const response = await client.chat.completions.create({ model: 'llama-3.3-70b-versatile', messages: [ { role: 'user', content: 'Explain quantum computing in 3 sentences.' }, ], }); console.log(response.choices[0].message.content); // Response in ~0.3 seconds (500+ tok/s)
// Together.ai — Node.js (OpenAI-compatible) import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.TOGETHER_API_KEY, baseURL: 'https://api.together.xyz/v1', }); const response = await client.chat.completions.create({ model: 'meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo', messages: [ { role: 'user', content: 'Explain quantum computing in 3 sentences.' }, ], }); console.log(response.choices[0].message.content); // Llama 3.1 405B — largest open-source model
Cost per 1M tokens (input/output) for each provider's flagship model.
| Provider | Model | Input (per 1M) | Output (per 1M) | Free Tier |
|---|---|---|---|---|
| OpenAI | GPT-4o | $2.50 | $10.00 | $5 credit |
| OpenAI | GPT-4o-mini | $0.15 | $0.60 | $5 credit |
| Anthropic | Claude Sonnet 4.6 | $3.00 | $15.00 | None |
| Anthropic | Claude Haiku 4.5 | $0.80 | $4.00 | None |
| Gemini 2.5 Pro | $1.25 | $10.00 | Free tier | |
| Gemini 2.0 Flash | $0.075 | $0.30 | Free tier | |
| Mistral | Mistral Large | $2.00 | $6.00 | Free tier |
| Mistral | Mistral Small | $0.10 | $0.30 | Free tier |
| Groq | Llama 3.3 70B | $0.59 | $0.79 | Free tier |
| Cohere | Command R+ | $2.50 | $10.00 | Free tier |
| Together | Llama 3.1 405B | $3.50 | $3.50 | $5 credit |
Prices as of March 2026. Check provider websites for current pricing. Batch/cached pricing may be significantly lower.
Typical inference speed and latency benchmarks for each provider.
| Provider | Model | Tokens/sec (output) | Time to First Token | Throughput Rating |
|---|---|---|---|---|
| Groq | Llama 3.3 70B | 500+ | <100ms | Fastest |
| Gemini 2.0 Flash | 200+ | <200ms | Very Fast | |
| Mistral | Mistral Small | 150+ | <300ms | Fast |
| OpenAI | GPT-4o | 80-120 | 200-500ms | Good |
| Anthropic | Claude Sonnet | 80-100 | 300-600ms | Good |
| Together | Llama 3.1 405B | 40-80 | 500ms-1s | Moderate |
| Cohere | Command R+ | 40-60 | 500ms-1s | Moderate |
Speed varies by prompt length, model load, and region. Groq's LPU hardware provides consistently fast inference regardless of load.
Quick recommendations based on your use case.
Largest ecosystem, most tutorials, best third-party support. The safe default choice for most applications.
Best instruction-following, 200K context for large codebases, excellent at debugging and code review.
1M token context window handles entire books, codebases, or video transcripts in a single request.
500+ tokens/second on LPU hardware. Unmatched for real-time applications, chatbots, and interactive UIs.
Gemini Flash at $0.075/1M input tokens or Mistral Small at $0.10/1M. Great quality at a fraction of GPT-4 pricing.
Built-in RAG with citations, top-tier embeddings and reranking. Purpose-built for enterprise knowledge bases.
Frostbyte provides complementary APIs that work alongside any LLM: web scraping, screenshots, IP geolocation, DNS lookup, and more. Power your AI agents with real-world data.
Explore Frostbyte APIs