Other Model Providers: Gemini, Groq, Mistral & Together AI
Learn Google Gemini and Groq with working examples, plus a comparison against Mistral and Together AI as further alternatives.
Introduction
OpenAI and Anthropic are not the only options — Google, Groq, Mistral, and Together AI each offer their own hosted models with real advantages: multimodal input, extremely low latency, open-weight models, or lower cost. Knowing when to reach for one of these matters just as much as knowing the two most popular SDKs.
- How to install and call Google's Gemini SDK.
- How to install and call Groq for extremely fast inference.
- What Mistral and Together AI offer, and when they fit better than the "big two."
A Real-Life Analogy First
Think about choosing a food delivery app in a city with several: one app has the widest menu and can even show you a photo of a dish and tell you what's in it (Gemini's multimodal strength). Another is famous specifically for delivering unusually fast, sometimes in minutes (Groq's speed). A third is known for being reliably affordable for everyday orders (Mistral's cost efficiency). A fourth is really a marketplace that lets you order from dozens of different kitchens through one single app (Together AI's wide model catalog). None of them is "the wrong app" — the right choice depends entirely on what you need on a given day.
Google Gemini
Use case: the google-genai package calls Google's Gemini models, which are natively multimodal — a single call can accept text, images, and even video or audio input without a separate vision-specific endpoint.
pip install google-genaiGemini in Action
from google import genai
client = genai.Client() # reads GEMINI_API_KEY from the environment
response = client.models.generate_content( model="gemini-2.0-flash", contents="Explain what a REST API is in two sentences.",)
print(response.text)Click Run to see what this code prints.
Groq: Speed-Optimized Inference
Use case: Groq is not a model creator — it runs open-weight models (like Llama and Mixtral) on custom hardware called LPUs, delivering responses dramatically faster than typical GPU-based inference. It exposes an OpenAI-compatible API, so the client code looks almost identical to lesson 5's OpenAI example.
pip install groqGroq in Action
from groq import Groq
client = Groq() # reads GROQ_API_KEY from the environment
response = client.chat.completions.create( model="llama-3.3-70b-versatile", messages=[{"role": "user", "content": "List three benefits of caching."}],)
print(response.choices[0].message.content)Click Run to see what this code prints.
Groq, Together AI, and many other providers deliberately match OpenAI's request/response shape. In practice, you can often switch to them by pointing the openai SDK's base_url at their endpoint instead of installing a separate package at all — like how a universal phone charger works across different phone brands because they all agreed to use the same USB-C standard.
Mistral & Together AI
Mistral AI is a European provider offering both hosted proprietary models and openly licensed ones, popular for cost-efficiency and data residency requirements. Together AI is an inference platform that hosts a wide catalog of open-weight models (Llama, Mixtral, Qwen, and more) behind a single OpenAI-compatible API, making it a common choice when you want to try many open models without running any infrastructure yourself. Both install and call almost identically to Groq above: pip install mistralai or use the openai SDK pointed at Together's endpoint.
Comparing All Four
| Provider | Best Known For | API Style | Delivery-App Analogy |
|---|---|---|---|
| Google Gemini | Native multimodal input (text, image, video, audio) | Own SDK (google-genai) | The app with the widest, most flexible menu |
| Groq | Extremely low-latency inference on open-weight models | OpenAI-compatible | The famously fast delivery app |
| Mistral AI | Cost-efficient models, some openly licensed | Own SDK (mistralai) | The reliably affordable everyday option |
| Together AI | Widest catalog of open-weight models, single endpoint | OpenAI-compatible | The marketplace with dozens of kitchens in one app |
Common Mistakes
- Assuming every provider needs its own dedicated SDK — many are OpenAI-compatible and just need a different base_url.
- Choosing Gemini for a pure-text task where multimodal input adds no value, when a cheaper text-only model may fit better.
- Not checking a model's exact name string, which changes between providers and model versions and will otherwise return a "model not found" error.
Best Practices
- Reach for Gemini specifically when a task involves images, video, or audio input alongside text.
- Reach for Groq or Together AI when latency or the ability to swap between many open-weight models matters more than a single provider's specific model quality.
- Keep a thin abstraction layer over "call a model" so trying a new provider is a configuration change, not a rewrite.
Frequently Asked Questions
No — most learners pick one or two based on the project at hand. This lesson exists so you recognize the options, not so you sign up for everything.
No — these are unrelated products that happen to sound alike. Groq is the low-latency inference provider covered here; Grok is a separate model from a different company.
It changes often as providers compete on price — check each provider's current pricing page rather than relying on any fixed comparison, including this one.
Just pick one to start — OpenAI or Anthropic from lesson 5 are the most common starting points with the most tutorials available. This lesson exists so that when you eventually hit a specific need (speed, cost, multimodal input), you already recognize which provider was built for it, instead of starting your research from zero.
Key Takeaways
- Gemini stands out for native multimodal input across text, image, video, and audio.
- Groq stands out for extremely fast inference on open-weight models via an OpenAI-compatible API.
- Mistral and Together AI round out the landscape with cost-efficient and open-model-focused options.
- Many alternative providers deliberately match OpenAI's API shape, minimizing the code you need to change.
Summary
OpenAI and Anthropic cover most use cases, but Gemini, Groq, Mistral, and Together AI each solve a specific gap — multimodal input, speed, cost, or open-model variety — worth reaching for when that gap matches your project.
- You can install and call Gemini for multimodal-capable requests.
- You can install and call Groq for low-latency inference.
- You know what Mistral and Together AI are best known for.