COMPARISON GUIDE

AI & LLM API Comparison 2026

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

What is an LLM API?

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.

1
User Prompt
2
API Request
3
Model Inference
4
Stream Response
5
Post-Process
💬

Chatbots & Assistants

Customer support bots, internal Q&A, conversational interfaces. Requires fast streaming and context management.

💻

Code Generation

Autocomplete, code review, bug fixing, refactoring. Models need strong reasoning and language-specific knowledge.

📝

Content Creation

Blog posts, marketing copy, product descriptions, social media. Requires creativity and brand voice consistency.

🔍

RAG & Search

Retrieval-augmented generation for knowledge bases, document Q&A, semantic search. Needs large context windows.

🤖

AI Agents

Autonomous agents that use tools, browse the web, execute code. Requires function calling and long-context reasoning.

📊

Data Analysis

Structured extraction, summarization, classification, sentiment analysis. Benefits from JSON mode and schema enforcement.

Feature Comparison

Key differences between AI/LLM APIs at a glance.

ProviderTop ModelContext WindowStreamingFunction CallingVisionJSON ModeFine-tuning
OpenAIGPT-4o, o3128KYesYesYesYesYes
AnthropicClaude Opus 4.6200KYesYesYesYesNo
GoogleGemini 2.5 Pro1MYesYesYesYesYes
MistralMistral Large128KYesYesYesYesYes
GroqLlama 3.3 70B128KYesYesYesYesNo
CohereCommand R+128KYesYesNoYesYes
Together.aiLlama 3.1 405B128KYesYesYesYesYes

Provider Deep-Dives

Detailed breakdown of each AI/LLM API provider.

OpenAI

The market leader. GPT-4o, o1/o3 reasoning models, DALL-E, Whisper, and the largest ecosystem.
GPT-4o: fast multimodal flagship
o1/o3: chain-of-thought reasoning
128K context window
Function calling & structured outputs
Vision, audio, and image generation
Fine-tuning on GPT-4o-mini
Batch API for 50% cost reduction
Assistants API with built-in RAG

Pros

  • Largest ecosystem and SDK support
  • Most third-party integrations
  • Strong multimodal capabilities
  • Reliable uptime and scalability

Cons

  • Most expensive per token
  • Data may be used for training (opt-out available)
  • Rate limits can be restrictive on free tier
  • Aggressive content filtering

Anthropic (Claude)

Safety-focused AI with the best instruction-following. 200K context, excellent for coding and analysis.
Claude Opus 4.6: most capable model
Claude Sonnet 4.6: best price/performance
200K token context window
Extended thinking for complex reasoning
Tool use & computer use
Vision (images and PDFs)
System prompts with caching
Prompt caching (90% cost reduction)

Pros

  • Best instruction-following accuracy
  • Largest standard context (200K)
  • Excellent at coding and analysis
  • Prompt caching saves money at scale

Cons

  • No fine-tuning available
  • No audio or image generation
  • Smaller model selection than OpenAI
  • Can be overly cautious on edge cases

Google (Gemini)

1M token context, multimodal native, deeply integrated with Google Cloud and Workspace.
Gemini 2.5 Pro: frontier reasoning
Gemini 2.0 Flash: fast and cheap
1M token context window
Native multimodal (text, image, video, audio)
Grounding with Google Search
Code execution in sandbox
Generous free tier
Vertex AI for enterprise

Pros

  • Largest context window (1M tokens)
  • Most generous free tier
  • Native video and audio understanding
  • Strong at multimodal tasks

Cons

  • API can be less consistent than OpenAI
  • Vertex AI pricing is complex
  • Weaker at precise instruction-following
  • Availability varies by region

Mistral AI

European AI lab with open-weight and commercial models. Best price/performance ratio for many tasks.
Mistral Large: flagship commercial model
Codestral: specialized for code
Mistral Small: fast and affordable
128K context window
Function calling support
Fine-tuning available
EU data residency
Open-weight models (Apache 2.0)

Pros

  • Excellent price/performance ratio
  • EU-based with data sovereignty
  • Open-weight models for self-hosting
  • Codestral excels at code tasks

Cons

  • Smaller ecosystem than OpenAI
  • Less multimodal than competitors
  • Fewer third-party integrations
  • Newer, less proven at enterprise scale

Groq

Fastest inference in the industry. Custom LPU hardware delivers 500+ tokens/second on open-source models.
Llama 3.3 70B at 500+ tok/s
Mixtral 8x7B at 700+ tok/s
Free tier with rate limits
OpenAI-compatible API
Function calling support
Vision model support
Custom LPU hardware
Developer-friendly pricing

Pros

  • 10-100x faster inference than GPUs
  • Free tier available
  • OpenAI-compatible drop-in replacement
  • Great for latency-critical apps

Cons

  • Only serves open-source models
  • Limited model selection
  • Rate limits on free tier
  • No fine-tuning support

Cohere

Enterprise-focused AI with best-in-class RAG, embeddings, and multilingual support.
Command R+: flagship with RAG
128K context window
Built-in RAG with citations
Embed v3: top-tier embeddings
Rerank API for search
100+ language support
Fine-tuning available
On-premise deployment option

Pros

  • Best native RAG with citations
  • Top-tier embedding models
  • Enterprise deployment options
  • Excellent multilingual support

Cons

  • Weaker at creative/coding tasks
  • No vision support
  • Smaller community than OpenAI
  • Less competitive on pure generation

Together.ai

Run any open-source model via API. Llama, Mixtral, DeepSeek, Qwen, and 100+ models available.
100+ open-source models
Llama 3.1 405B available
DeepSeek V3 and R1
Custom fine-tuning
Serverless and dedicated endpoints
OpenAI-compatible API
Embeddings and reranking
Batch inference API

Pros

  • Widest model selection
  • Run latest open-source models instantly
  • Fine-tuning on any model
  • Competitive pricing

Cons

  • No proprietary frontier models
  • Speed varies by model and load
  • Less polished than OpenAI/Anthropic
  • Enterprise support is newer

Code Examples

Make a chat completion request with each provider.

OpenAI
Anthropic
Google
Mistral
Groq
Together
// 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

Pricing Comparison

Cost per 1M tokens (input/output) for each provider's flagship model.

ProviderModelInput (per 1M)Output (per 1M)Free Tier
OpenAIGPT-4o$2.50$10.00$5 credit
OpenAIGPT-4o-mini$0.15$0.60$5 credit
AnthropicClaude Sonnet 4.6$3.00$15.00None
AnthropicClaude Haiku 4.5$0.80$4.00None
GoogleGemini 2.5 Pro$1.25$10.00Free tier
GoogleGemini 2.0 Flash$0.075$0.30Free tier
MistralMistral Large$2.00$6.00Free tier
MistralMistral Small$0.10$0.30Free tier
GroqLlama 3.3 70B$0.59$0.79Free tier
CohereCommand R+$2.50$10.00Free tier
TogetherLlama 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.

Speed & Performance

Typical inference speed and latency benchmarks for each provider.

ProviderModelTokens/sec (output)Time to First TokenThroughput Rating
GroqLlama 3.3 70B500+<100msFastest
GoogleGemini 2.0 Flash200+<200msVery Fast
MistralMistral Small150+<300msFast
OpenAIGPT-4o80-120200-500msGood
AnthropicClaude Sonnet80-100300-600msGood
TogetherLlama 3.1 405B40-80500ms-1sModerate
CohereCommand R+40-60500ms-1sModerate

Speed varies by prompt length, model load, and region. Groq's LPU hardware provides consistently fast inference regardless of load.

Which AI API Should You Choose?

Quick recommendations based on your use case.

General Purpose / Getting Started

OpenAI (GPT-4o)

Largest ecosystem, most tutorials, best third-party support. The safe default choice for most applications.

Coding & Technical Tasks

Anthropic (Claude)

Best instruction-following, 200K context for large codebases, excellent at debugging and code review.

Long Document Analysis

Google (Gemini 2.5 Pro)

1M token context window handles entire books, codebases, or video transcripts in a single request.

Lowest Latency

Groq

500+ tokens/second on LPU hardware. Unmatched for real-time applications, chatbots, and interactive UIs.

Best Value / Budget-Friendly

Mistral or Gemini Flash

Gemini Flash at $0.075/1M input tokens or Mistral Small at $0.10/1M. Great quality at a fraction of GPT-4 pricing.

Enterprise RAG & Search

Cohere

Built-in RAG with citations, top-tier embeddings and reranking. Purpose-built for enterprise knowledge bases.

Frequently Asked Questions

What is the cheapest LLM API in 2026?
Google Gemini 2.0 Flash at $0.075/1M input tokens is the cheapest from a major provider. Mistral Small is $0.10/1M. For open-source models, Groq offers free API access with rate limits. At scale, self-hosting open-source models on your own GPUs can be even cheaper, but requires infrastructure management.
Which AI API is best for code generation?
Claude (Anthropic) and GPT-4o (OpenAI) consistently rank highest on coding benchmarks. Claude excels at understanding large codebases and following complex refactoring instructions. For specialized code tasks, Mistral's Codestral model offers a good balance of quality and price. DeepSeek Coder V3, available via Together.ai, is also competitive.
Can I switch between providers easily?
Many providers offer OpenAI-compatible APIs, making switching relatively easy. Groq, Together.ai, Mistral, and others accept the same request format as OpenAI. You can use libraries like LiteLLM or the Vercel AI SDK that provide a unified interface across all providers. Anthropic uses a slightly different API format but most wrapper libraries handle this.
Is it worth running open-source models instead?
It depends on your scale and requirements. Self-hosting Llama 3.1 or Mixtral eliminates per-token costs but requires GPU infrastructure ($1-10/hour for A100/H100 GPUs). For most developers, using open-source models via API providers like Groq or Together.ai is more practical. Self-hosting makes sense when you need data privacy, custom fine-tuning, or are processing millions of tokens daily.
What is function calling / tool use?
Function calling lets AI models request actions from your code, such as looking up data, calling APIs, or running calculations. You define available functions with their parameters, and the model returns structured JSON specifying which function to call and with what arguments. All major providers support this. It is essential for building AI agents that interact with external systems.
How do I reduce my AI API costs?
Several strategies: (1) Use smaller models for simple tasks (GPT-4o-mini, Haiku, Gemini Flash). (2) Enable prompt caching (Anthropic offers 90% reduction, OpenAI offers 50%). (3) Use batch APIs for non-real-time workloads (50% savings on OpenAI). (4) Minimize context length by trimming conversation history. (5) Use embeddings for RAG instead of stuffing documents into the prompt. (6) Route between models based on query complexity.
Which provider has the best data privacy?
Anthropic and Mistral do not use API data for training by default. OpenAI does not use API data for training (opt-out by default since 2024). Google's terms vary between AI Studio (training opt-out available) and Vertex AI (no training). For maximum privacy, self-host open-source models or use Mistral with EU data residency. Cohere offers on-premise deployment for enterprise customers.

Build AI Applications with Frostbyte

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