Is Mistral AI Down? How to Check Mistral AI Status in Real-Time

Quick Answer: To check if Mistral AI is down, visit apistatuscheck.com/api/mistral for real-time monitoring, or check Mistral's official status page. Common signs include API timeout errors, rate limiting issues, model unavailability (especially for Mistral 7B, Mixtral, and Le Chat), streaming failures, and authentication errors.

When your AI-powered application suddenly stops responding, every second of downtime affects user experience and business operations. Mistral AI, Europe's leading AI company, powers thousands of production applications with its state-of-the-art open-source models including Mistral 7B, Mixtral 8x7B, and Mistral Large. Whether you're experiencing API errors, model loading failures, or streaming interruptions, quickly verifying Mistral's operational status can save critical troubleshooting time and help you make informed decisions about failover strategies.

How to Check Mistral AI Status in Real-Time

1. API Status Check (Fastest Method)

The quickest way to verify Mistral AI's operational status is through apistatuscheck.com/api/mistral. This real-time monitoring service:

• Tests actual API endpoints every 60 seconds
• Monitors model availability for all major Mistral models
• Shows response times and latency trends across regions
• Tracks historical uptime over 30/60/90 days
• Provides instant alerts when issues are detected
• Monitors European data centers specifically

Unlike status pages that rely on manual updates, API Status Check performs active health checks against Mistral's production endpoints, giving you the most accurate real-time picture of service availability—critical for businesses relying on European AI infrastructure with GDPR compliance requirements.

2. Official Mistral AI Status Page

Mistral AI maintains an official status page as their primary communication channel for service incidents. The page displays:

• Current operational status for API endpoints
• Model-specific availability (Mistral 7B, Mixtral, Mistral Large, etc.)
• Active incidents and investigations
• Scheduled maintenance windows
• Historical incident reports
• Regional status (EU data centers)

Pro tip: Subscribe to status updates to receive immediate notifications when incidents occur, especially important for production deployments requiring European data residency.

3. Test Le Chat Interface

If chat.mistral.ai (Le Chat) is loading slowly, showing errors, or failing to generate responses, this often indicates broader infrastructure issues affecting the API as well. Pay attention to:

• Login failures or authentication timeouts
• Model selection errors
• Response generation failures
• Streaming interruptions mid-response

Le Chat uses the same underlying infrastructure as the API, making it a good real-time indicator of service health.

4. Test API Endpoints Directly

For developers, making a test API call can quickly confirm connectivity:

from mistralai import Mistral

client = Mistral(api_key="your_api_key_here")

try:
    response = client.chat.complete(
        model="mistral-small-latest",
        messages=[
            {
                "role": "user",
                "content": "API health check"
            }
        ]
    )
    print("API Status: Operational")
    print(f"Response time: {response.usage}")
except Exception as e:
    print(f"API Status: Error - {str(e)}")

Look for connection errors, timeout exceptions, or HTTP 5xx response codes indicating server-side issues.

5. Monitor Community Channels

The AI community is often quick to report issues:

• Twitter/X: Search for "Mistral AI down" or "@MistralAI status"
• Discord: Join Mistral AI community Discord for real-time discussions
• Reddit: Check r/LocalLLaMA and r/MachineLearning
• Hacker News: Often has early reports of major AI infrastructure issues
• GitHub Issues: Check Mistral's official repositories for reported problems

Community reports can alert you to regional issues or emerging problems before official status updates are posted.

Common Mistral AI Issues and How to Identify Them

API Rate Limiting

Symptoms:

• 429 Too Many Requests HTTP status code
• rate_limit_exceeded error messages
• Requests being throttled despite being within your plan limits
• Increased latency during peak hours

What it means: Mistral AI implements rate limiting to ensure fair usage across customers. During high-demand periods or infrastructure stress, rate limits may be enforced more aggressively. Normal rate limits:

• Free tier: 1 request/second
• Paid tiers: Variable based on your plan

Example error response:

{
  "error": {
    "type": "rate_limit_exceeded",
    "message": "Rate limit exceeded. Please retry after 60 seconds.",
    "status": 429
  }
}

Model Availability Issues

Common scenarios:

• Specific models returning 503 Service Unavailable
• Model loading timeout errors
• Fallback to different model versions
• Regional model availability disparities

Popular models affected:

• Mistral 7B - The flagship open-source 7B parameter model
• Mixtral 8x7B - Mixture of experts model (47B total parameters)
• Mistral Large - Production-grade large language model
• Mistral Medium - Balanced performance/cost option
• Mistral Small - Efficient for simple tasks

During partial outages, larger models (Mistral Large, Mixtral) may be taken offline first to preserve capacity, with traffic redirected to smaller models.

Detection code:

from mistralai import Mistral

client = Mistral(api_key="your_api_key")

models_to_test = [
    "mistral-small-latest",
    "mistral-medium-latest", 
    "mistral-large-latest",
    "open-mistral-7b",
    "open-mixtral-8x7b"
]

for model in models_to_test:
    try:
        response = client.chat.complete(
            model=model,
            messages=[{"role": "user", "content": "test"}],
            max_tokens=10
        )
        print(f"✓ {model}: Available")
    except Exception as e:
        print(f"✗ {model}: {str(e)}")

Authentication Errors

Indicators:

• 401 Unauthorized errors with valid API keys
• 403 Forbidden responses
• "Invalid API key" messages for working keys
• Authentication service timeouts

Common causes during outages:

• Authentication service degradation
• API key validation service down
• Database connection issues for credential verification
• Token refresh failures

Example error:

# MistralAPIException: 401 - Invalid API key provided

Debugging steps:

import os
from mistralai import Mistral

# Verify API key is properly loaded
api_key = os.getenv("MISTRAL_API_KEY")
print(f"API Key length: {len(api_key) if api_key else 'NOT SET'}")

# Test with explicit key
client = Mistral(api_key=api_key)

try:
    # Simple list models call to test authentication
    models = client.models.list()
    print("Authentication successful")
except Exception as e:
    print(f"Authentication failed: {e}")

Streaming Failures

Symptoms:

• Stream starts but cuts off mid-response
• ChunkedEncodingError or connection reset errors
• Incomplete JSON responses
• No stream chunks received despite successful connection

Example streaming implementation with error handling:

from mistralai import Mistral

client = Mistral(api_key="your_api_key")

try:
    stream = client.chat.stream(
        model="mistral-small-latest",
        messages=[
            {
                "role": "user",
                "content": "Write a long story about AI"
            }
        ]
    )
    
    for chunk in stream:
        if chunk.data.choices:
            content = chunk.data.choices[0].delta.content
            if content:
                print(content, end="", flush=True)
                
except ConnectionError as e:
    print(f"\n❌ Streaming connection failed: {e}")
except Exception as e:
    print(f"\n❌ Streaming error: {e}")

Retry logic for streaming:

import time
from mistralai import Mistral

def stream_with_retry(client, messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            stream = client.chat.stream(
                model="mistral-small-latest",
                messages=messages
            )
            
            full_response = ""
            for chunk in stream:
                if chunk.data.choices:
                    content = chunk.data.choices[0].delta.content
                    if content:
                        full_response += content
                        
            return full_response
            
        except Exception as e:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Retry {attempt + 1}/{max_retries} after {wait_time}s...")
                time.sleep(wait_time)
            else:
                raise e

Function Calling Issues

Mistral AI supports function calling (tool use), which can fail during outages:

Symptoms:

• Functions not being called despite proper schema
• Invalid function call JSON
• Tool response parsing errors
• Missing function arguments

Example function calling with error handling:

from mistralai import Mistral

client = Mistral(api_key="your_api_key")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

try:
    response = client.chat.complete(
        model="mistral-large-latest",
        messages=[
            {
                "role": "user",
                "content": "What's the weather in Paris?"
            }
        ],
        tools=tools,
        tool_choice="auto"
    )
    
    if response.choices[0].message.tool_calls:
        print("✓ Function calling operational")
    else:
        print("⚠ No function calls generated")
        
except Exception as e:
    print(f"❌ Function calling failed: {e}")

Business Impact When Mistral AI Goes Down

AI Application Downtime

For businesses running AI-powered applications on Mistral:

• Chatbots and virtual assistants become unresponsive
• Content generation pipelines halt
• AI-powered search and recommendations fail
• Document analysis and summarization tools break
• Code generation features in IDEs stop working

Example impact: A customer service chatbot processing 1,000 queries/hour experiences complete service disruption, forcing manual support escalation.

European Data Residency Compliance

Mistral AI is strategically important for European businesses requiring:

• GDPR-compliant AI infrastructure with EU data residency
• Sovereignty over AI workloads without US cloud dependency
• Regulatory compliance for sensitive data processing

Critical consideration: When Mistral goes down, falling back to US-based providers (OpenAI, Anthropic) may violate data residency requirements, especially for healthcare, finance, and government applications.

Compliance impact:

# Companies with strict EU data residency requirements
# CANNOT fail over to non-EU providers during Mistral outages

def get_ai_response(prompt):
    try:
        # Primary: Mistral AI (EU-hosted)
        return mistral_client.chat.complete(messages=[...])
    except MistralAPIError:
        # ❌ COMPLIANCE VIOLATION: Cannot fail to US providers
        # return openai_client.chat.completions.create(...)
        
        # ✓ Better approach: Queue for delayed processing
        return queue_for_retry(prompt)

Production Inference Costs

Unlike development environments, production AI applications face real costs during outages:

• Lost inference capacity = lost business functionality
• Wasted GPU reservations if you have dedicated capacity
• Delayed batch processing jobs that miss SLA deadlines
• Re-processing costs for failed inference requests

Financial impact example:

• 10,000 daily inference requests at $0.001/request = $10/day direct cost
• 2-hour outage during business hours = ~830 failed requests
• Plus engineering time for incident response and recovery

Open-Source Model Deployment Disruption

Many teams use Mistral's API during development before deploying open-source models (Mistral 7B, Mixtral) locally:

• Development workflows blocked when testing against API
• Benchmark comparisons between API and local deployment fail
• Fine-tuning pipelines relying on API-based evaluation break

Competitive Disadvantage

In the fast-moving AI landscape:

• Users switch to competitors offering better reliability
• Enterprise deals delayed due to availability concerns
• Media coverage of outages damages brand reputation
• Developer trust erodes affecting platform adoption

Incident Response Playbook for Mistral AI Outages

1. Implement Exponential Backoff with Jitter

Production-grade retry logic:

import time
import random
from mistralai import Mistral
from mistralai.exceptions import MistralAPIException

def call_mistral_with_retry(
    client: Mistral,
    model: str,
    messages: list,
    max_retries: int = 5,
    base_delay: float = 1.0
):
    """
    Call Mistral API with exponential backoff and jitter.
    
    Args:
        client: Mistral client instance
        model: Model identifier
        messages: Chat messages
        max_retries: Maximum number of retry attempts
        base_delay: Base delay in seconds for exponential backoff
    """
    for attempt in range(max_retries):
        try:
            response = client.chat.complete(
                model=model,
                messages=messages
            )
            return response
            
        except MistralAPIException as e:
            if attempt == max_retries - 1:
                # Final attempt failed
                raise e
                
            # Check if error is retryable
            if e.status_code in [429, 500, 502, 503, 504]:
                # Calculate delay with exponential backoff and jitter
                delay = base_delay * (2 ** attempt)
                jitter = random.uniform(0, delay * 0.1)
                total_delay = delay + jitter
                
                print(f"Attempt {attempt + 1} failed with {e.status_code}. "
                      f"Retrying in {total_delay:.2f}s...")
                time.sleep(total_delay)
            else:
                # Non-retryable error (e.g., 401, 400)
                raise e
                
    raise Exception(f"Failed after {max_retries} attempts")

2. Implement Circuit Breaker Pattern

Prevent cascading failures by stopping requests when Mistral is clearly down:

from datetime import datetime, timedelta
from enum import Enum

class CircuitState(Enum):
    CLOSED = "closed"  # Normal operation
    OPEN = "open"      # Failing, reject requests
    HALF_OPEN = "half_open"  # Testing recovery

class MistralCircuitBreaker:
    def __init__(
        self,
        failure_threshold: int = 5,
        recovery_timeout: int = 60,
        expected_exception: Exception = MistralAPIException
    ):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.expected_exception = expected_exception
        
        self.failure_count = 0
        self.last_failure_time = None
        self.state = CircuitState.CLOSED
        
    def call(self, func, *args, **kwargs):
        if self.state == CircuitState.OPEN:
            if self._should_attempt_reset():
                self.state = CircuitState.HALF_OPEN
            else:
                raise Exception("Circuit breaker is OPEN - Mistral AI unavailable")
                
        try:
            result = func(*args, **kwargs)
            self._on_success()
            return result
            
        except self.expected_exception as e:
            self._on_failure()
            raise e
            
    def _on_success(self):
        self.failure_count = 0
        self.state = CircuitState.CLOSED
        
    def _on_failure(self):
        self.failure_count += 1
        self.last_failure_time = datetime.now()
        
        if self.failure_count >= self.failure_threshold:
            self.state = CircuitState.OPEN
            
    def _should_attempt_reset(self):
        return (datetime.now() - self.last_failure_time).seconds >= self.recovery_timeout

# Usage
circuit_breaker = MistralCircuitBreaker(failure_threshold=3, recovery_timeout=60)

def make_mistral_call():
    client = Mistral(api_key="your_api_key")
    return client.chat.complete(
        model="mistral-small-latest",
        messages=[{"role": "user", "content": "Hello"}]
    )

try:
    response = circuit_breaker.call(make_mistral_call)
except Exception as e:
    print(f"Circuit breaker prevented call: {e}")

3. Implement Model Fallback Strategy

Gracefully degrade to smaller/faster models during capacity issues:

from mistralai import Mistral

class MistralWithFallback:
    def __init__(self, api_key: str):
        self.client = Mistral(api_key=api_key)
        self.model_hierarchy = [
            "mistral-large-latest",  # Primary
            "mistral-medium-latest", # Fallback 1
            "mistral-small-latest",  # Fallback 2
            "open-mistral-7b"        # Last resort
        ]
        
    def complete(self, messages: list, **kwargs):
        """Try models in order until one succeeds."""
        last_error = None
        
        for model in self.model_hierarchy:
            try:
                response = self.client.chat.complete(
                    model=model,
                    messages=messages,
                    **kwargs
                )
                
                if model != self.model_hierarchy[0]:
                    print(f"⚠️ Using fallback model: {model}")
                    
                return response
                
            except Exception as e:
                last_error = e
                print(f"Model {model} failed: {e}")
                continue
                
        raise Exception(f"All models failed. Last error: {last_error}")

# Usage
client = MistralWithFallback(api_key="your_api_key")
response = client.complete(messages=[{"role": "user", "content": "Hello"}])

4. Queue Requests for Delayed Processing

When real-time isn't critical, queue failed requests:

import json
from datetime import datetime
from pathlib import Path

class MistralRequestQueue:
    def __init__(self, queue_file: str = "mistral_queue.jsonl"):
        self.queue_file = Path(queue_file)
        
    def enqueue(self, model: str, messages: list, metadata: dict = None):
        """Add failed request to queue for later processing."""
        request = {
            "timestamp": datetime.now().isoformat(),
            "model": model,
            "messages": messages,
            "metadata": metadata or {}
        }
        
        with open(self.queue_file, "a") as f:
            f.write(json.dumps(request) + "\n")
            
    def process_queue(self, client: Mistral):
        """Process all queued requests."""
        if not self.queue_file.exists():
            return
            
        processed = []
        failed = []
        
        with open(self.queue_file, "r") as f:
            requests = [json.loads(line) for line in f]
            
        for request in requests:
            try:
                response = client.chat.complete(
                    model=request["model"],
                    messages=request["messages"]
                )
                processed.append(request)
                print(f"✓ Processed queued request from {request['timestamp']}")
                
            except Exception as e:
                failed.append(request)
                print(f"✗ Still failing: {e}")
                
        # Rewrite queue with only failed requests
        with open(self.queue_file, "w") as f:
            for req in failed:
                f.write(json.dumps(req) + "\n")
                
        return len(processed), len(failed)

# Usage during outage
queue = MistralRequestQueue()

try:
    response = client.chat.complete(...)
except MistralAPIException:
    queue.enqueue(
        model="mistral-small-latest",
        messages=[{"role": "user", "content": "Important query"}],
        metadata={"user_id": "12345", "request_id": "abc"}
    )
    print("Request queued for later processing")

# Later, when service is restored
processed, failed = queue.process_queue(client)
print(f"Processed: {processed}, Still failed: {failed}")

5. Monitor Multiple Metrics

Comprehensive monitoring catches issues early:

import time
from mistralai import Mistral

class MistralHealthMonitor:
    def __init__(self, api_key: str, alert_threshold_ms: int = 5000):
        self.client = Mistral(api_key=api_key)
        self.alert_threshold_ms = alert_threshold_ms
        
    def health_check(self):
        """Perform comprehensive health check."""
        results = {
            "timestamp": datetime.now().isoformat(),
            "checks": {}
        }
        
        # Test 1: API connectivity
        try:
            start = time.time()
            models = self.client.models.list()
            latency = (time.time() - start) * 1000
            
            results["checks"]["connectivity"] = {
                "status": "ok",
                "latency_ms": latency
            }
        except Exception as e:
            results["checks"]["connectivity"] = {
                "status": "error",
                "error": str(e)
            }
            
        # Test 2: Model availability
        test_models = ["mistral-small-latest", "mistral-large-latest"]
        for model in test_models:
            try:
                start = time.time()
                response = self.client.chat.complete(
                    model=model,
                    messages=[{"role": "user", "content": "ping"}],
                    max_tokens=5
                )
                latency = (time.time() - start) * 1000
                
                results["checks"][f"model_{model}"] = {
                    "status": "ok",
                    "latency_ms": latency
                }
                
                if latency > self.alert_threshold_ms:
                    self._send_alert(f"High latency for {model}: {latency}ms")
                    
            except Exception as e:
                results["checks"][f"model_{model}"] = {
                    "status": "error",
                    "error": str(e)
                }
                self._send_alert(f"Model {model} unavailable: {e}")
                
        # Test 3: Streaming
        try:
            start = time.time()
            stream = self.client.chat.stream(
                model="mistral-small-latest",
                messages=[{"role": "user", "content": "test"}],
                max_tokens=10
            )
            
            chunks = 0
            for chunk in stream:
                chunks += 1
                
            latency = (time.time() - start) * 1000
            results["checks"]["streaming"] = {
                "status": "ok",
                "chunks_received": chunks,
                "latency_ms": latency
            }
        except Exception as e:
            results["checks"]["streaming"] = {
                "status": "error",
                "error": str(e)
            }
            
        return results
        
    def _send_alert(self, message: str):
        """Send alert via your preferred channel."""
        print(f"🚨 ALERT: {message}")
        # Integrate with Slack, PagerDuty, email, etc.

# Run health checks every 5 minutes
monitor = MistralHealthMonitor(api_key="your_api_key")
health_status = monitor.health_check()
print(json.dumps(health_status, indent=2))

6. Communicate Proactively with Users

Status banner example:

<!-- Add to your application when Mistral issues detected -->
<div class="status-banner warning">
  ⚠️ We're experiencing delays with AI features due to provider issues. 
  Your requests are queued and will process automatically when service resumes.
  <a href="https://apistatuscheck.com/api/mistral">Check real-time status →</a>
</div>

User notification email template:

Subject: AI Service Temporary Delay

Hi [User],

We're currently experiencing intermittent issues with our AI features due to 
infrastructure problems with our AI provider (Mistral AI). 

Your request has been safely queued and will complete automatically within the 
next 24 hours. You'll receive a notification when it's ready.

We apologize for the inconvenience and appreciate your patience.

Current status: https://apistatuscheck.com/api/mistral

Best regards,
[Your Team]

Alternative AI Providers for Failover

While Mistral AI offers unique advantages (especially European data residency), consider these alternatives for failover scenarios:

Other European AI Providers

• Aleph Alpha - German AI company with EU hosting
• Cohere - Available in EU regions
• Consider compliance implications carefully

Global AI Providers

• OpenAI - GPT-4, GPT-3.5 (US-based)
• Anthropic (Claude) - Strong reasoning capabilities (US-based)
• Together AI - Open-source model hosting
• Hugging Face Inference - Wide model selection

Important: Failing over from Mistral to US providers may violate GDPR/data residency requirements. Consult your compliance team before implementing cross-region failover.

Frequently Asked Questions

How often does Mistral AI go down?

Mistral AI maintains strong uptime, though as a younger platform (founded 2023) compared to established providers, occasional growing pains occur. Major outages are rare (typically a few per year), but regional issues or specific model unavailability can happen during high-demand periods. Most production users experience 99%+ uptime annually.

What's the difference between Mistral's status page and API Status Check?

Mistral's official status page is manually updated during incidents, which can lag behind actual issues. API Status Check performs automated health checks every 60 seconds against live Mistral API endpoints, often detecting issues before they're officially reported. For production applications, monitor both sources for comprehensive visibility.

Can I use Mistral's open-source models when the API is down?

Yes! One of Mistral's key advantages is offering truly open-source models (Mistral 7B, Mixtral 8x7B) that you can self-host. Download models from Hugging Face and run them locally with vLLM, llama.cpp, or Ollama. This provides ultimate reliability but requires your own infrastructure. The API offers convenience and managed scaling, while self-hosting offers control and guaranteed availability.

Does Mistral AI have regional outages?

Mistral AI primarily operates from European data centers. Regional issues are less common than with globally distributed providers, but network connectivity problems can affect specific geographic regions. The concentration in EU infrastructure is both a feature (data residency) and a potential single point of failure compared to multi-region deployments.

How do I prevent hitting rate limits during recovery?

After an outage, implement exponential backoff with jitter (shown in code examples above) to avoid overwhelming the API during recovery. Don't immediately retry all queued requests—spread them out over several minutes. Mistral may temporarily reduce rate limits during recovery periods to ensure stability.

What models should I prioritize during partial outages?

During capacity issues, smaller models (Mistral 7B, Mistral Small) typically remain available longer than large models (Mistral Large, Mixtral). Implement a fallback hierarchy from largest to smallest. For many tasks, Mistral Small provides excellent performance at lower computational cost, making it ideal for degraded operation.

Should I cache Mistral AI responses?

Yes, absolutely! Implement response caching for identical queries to reduce API dependency:

import hashlib
import json
from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_mistral_call(messages_json: str, model: str):
    messages = json.loads(messages_json)
    response = client.chat.complete(model=model, messages=messages)
    return response

# Usage - convert messages to hashable JSON string
messages = [{"role": "user", "content": "What is AI?"}]
response = cached_mistral_call(json.dumps(messages), "mistral-small-latest")

Caching reduces costs, improves latency, and provides resilience during outages for repeated queries.

How does Mistral AI compare to OpenAI for reliability?

OpenAI has longer operational history and more mature infrastructure, generally resulting in higher uptime percentages. However, Mistral offers strategic advantages: European data residency, open-source model weights, and competitive pricing. For EU-based businesses, Mistral's occasional reliability trade-offs may be worthwhile for compliance benefits. Consider using both with appropriate failover logic based on your requirements.

What's the best way to monitor Mistral AI in production?

Implement multi-layered monitoring:

1. External monitoring: API Status Check for independent health checks
2. Application monitoring: Log error rates, latency, and success rates within your application
3. Synthetic monitoring: Scheduled test requests every 1-5 minutes
4. Alert thresholds: Trigger alerts when error rate exceeds 5% or latency exceeds 10 seconds

Avoid relying solely on official status pages—they often lag behind actual issues.

Can I get an SLA with Mistral AI?

Mistral offers SLA commitments for enterprise customers. Contact their sales team for custom agreements that may include uptime guarantees, dedicated support, and financial credits for downtime. Standard API access typically doesn't include SLA guarantees. Review your specific plan or contact Mistral for details.

Stay Ahead of Mistral AI Outages

Don't let AI infrastructure issues catch you off guard. Subscribe to real-time Mistral AI alerts and get notified instantly when issues are detected—before your users notice.

API Status Check monitors Mistral AI 24/7 with:

• 60-second health checks across all major models
• Instant alerts via email, Slack, Discord, or webhook
• Historical uptime tracking and incident reports
• Multi-provider monitoring for your entire AI stack
• EU-specific infrastructure monitoring

Start monitoring Mistral AI now →

Related AI/ML Monitoring Guides:

• Is OpenAI Down? Real-Time Status Guide
• Is Anthropic Down? Claude API Monitoring
• Is Together AI Down? Status Tracking
• Is Hugging Face Down? Model Hub Status

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Last updated: February 4, 2026. Mistral AI status information is provided in real-time based on active monitoring. For official incident reports, always refer to Mistral AI's official status page.