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Mistral: Mistral Tiny

Mistral: Mistral Tiny

Estimated Ultra-Light LLM for Edge Apps, Bots, and Fast NLP at API Scale

Context: 33 000 tokens
Output: up to 33 000 tokens
Modality:
Text
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Ultra-Light Open-Weight LLM for Fast, Low-Cost API and Local Use

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Mistral Tiny is a projected ultra-lightweight open-weight model in the Mistral AI family, targeting the lower bound of LLM size/performance tradeoffs. While not yet officially released as a standalone product, the name "Mistral Tiny" has become a shorthand for compact, fast-inference models intended for edge devices, automation scripts, and real-time chat interfaces.

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Expected to fit between 1B–3B parameters, Mistral Tiny would provide cost-effective, open-access inference for high-frequency, low-latency workloads.

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Through AnyAPI.ai, developers can explore early-access and substitute models (e.g., distilled versions or small-variant models) for Mistral Tiny-level performance via a unified API.

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Key Features of Mistral Tiny

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Sub-4B Parameter Footprint

Designed for environments with limited compute - ideal for mobile, browser, and embedded contexts.

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Low-Latency Inference (~100–200ms)

Blazing-fast performance makes it suitable for synchronous UIs and API chains.

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Open License Expected (Apache 2.0 or MIT)

Like other Mistral models, expected to be permissively licensed for modification and redistribution.

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Efficient Token Usage

Optimized for short prompts, config generation, system automation, and form-based UIs.

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Multilingual Output (Basic)

Supports basic generation and classification in English and select global languages.

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Use Cases for Mistral Tiny

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CLI Agents and Developer Tools

Embed language interfaces in command-line workflows, code tools, and build scripts.

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IoT and Edge-Based AI

Deploy on-device in smart appliances, AR glasses, or vehicle systems where low power is critical.

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Browser-Based LLM Interfaces

Power extensions, plugins, and JS-based AI modules in browser environments.

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Email and Template Automation

Draft short replies, generate form text, or fill structured templates programmatically.

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Low-Resource LLM Experiments

Use Mistral Tiny for prototyping instruction tuning or quantization techniques.


Why Use Mistral Tiny via AnyAPI.ai

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Unified API Access to Small and Large Models

Explore Mistral Tiny-alternatives alongside full-size Mistral, GPT, Claude, and more.

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Pay-As-You-Go Access to Small Inference

Perfect for high-volume, low-cost LLM workloads like customer messaging or text tagging.

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Preview Access to Distilled or Quantized Models

Try early-stage versions of Tiny-like models or distill your own with full control.

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Zero Setup for Edge-Use Emulation

Run Tiny-level models without setting up local GPU or embedded systems.

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Faster and More Flexible Than Hugging Face Hosted UI

Access production-ready endpoints with full observability, logs, and latency metrics

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Use Lightweight LLMs at Scale with Mistral Tiny

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Mistral Tiny promises to bring ultra-fast, ultra-efficient language AI to edge, automation, and everyday tools.

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Start exploring Tiny-tier models today with AnyAPI.ai - no setup, instant scale, full flexibility.

Comparison with other LLMs

Model
Context Window
Multimodal
Latency
Strengths
Model
Mistral: Mistral Tiny
Context Window
32k
Multimodal
No
Latency
Fast
Strengths
CLI tools, extensions, small agents
Get access
Model
OpenAI: o1-preview
Context Window
128k
Multimodal
Yes
Latency
Very Fast
Strengths
Prototyping, CLI tools, agents
Get access
Model
Mistral: Mistral Medium
Context Window
32k
Multimodal
No
Latency
Very Fast
Strengths
Open-weight, lightweight, ideal for real-time
Get access
Model
OpenAI: Codex Mini
Context Window
200k
Multimodal
Yes
Latency
Fast
Strengths
Real-time IDE codegen, Bash scripts
Get access
Model
DeepSeek: DeepSeek R1
Context Window
164k
Multimodal
No
Latency
Fast
Strengths
RAG, code, private LLMs
Get access

Sample code for 

Mistral: Mistral Tiny

Python
JavaScript
Curl
import requests

url = "https://api.anyapi.ai/v1/chat/completions"

payload = {
    "model": "mistral-tiny",
    "messages": [
        {
            "role": "user",
            "content": "Hello"
        }
    ]
}
headers = {
    "Authorization": "Bearer AnyAPI_API_KEY",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.json())
import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "model": "mistral-tiny", "messages": [ { "role": "user", "content": "Hello" } ] } headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json" } response = requests.post(url, json=payload, headers=headers) print(response.json())
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Code is copied
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
  body: '{"model":"mistral-tiny","messages":[{"role":"user","content":"Hello"}]}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
const url = 'https://api.anyapi.ai/v1/chat/completions'; const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"model":"mistral-tiny","messages":[{"role":"user","content":"Hello"}]}' }; try { const response = await fetch(url, options); const data = await response.json(); console.log(data); } catch (error) { console.error(error); }
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Code is copied
curl --request POST \
  --url https://api.anyapi.ai/v1/chat/completions \
  --header 'Authorization: Bearer AnyAPI_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "mistral-tiny",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}'
curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "model": "mistral-tiny", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Code is copied
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FAQs

Answers to common questions about integrating and using this AI model via AnyAPI.ai

Is Mistral Tiny officially released?

Not yet, but smaller models in the Mistral ecosystem are expected and preview-ready substitutes exist.

Can I run Mistral Tiny on-device?

Yes, once released—it’s optimized for CPU, edge GPU, or mobile inference.

Is Mistral Tiny part of Mistral’s open-source family?

It is expected to be, with the same open-weight philosophy as Mistral 7B and Mistral Medium.

What tasks can Mistral Tiny handle?

Simple generation, automation scripting, CLI bots, and embedded NLP workflows.

Is it useful in production?

Yes - for high-throughput, low-compute environments where low latency and transparency are critical.

Still have questions?

Contact us for more information

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