Ministral 3 14B 2512
Ministral 3 14B 2512 is a 14-billion-parameter AI language model from Mistral’s Ministral 3 series, configured with a 2,512-dimensional internal representation.
What is Ministral 3 14B 2512?
Ministral 3 14B 2512 is a medium-sized transformer-based language model developed by Mistral within the Ministral 3 line. It is mainly used for tasks such as conversation, drafting, summarization, and code or data-assisted text generation. It is also applied in applications that need relatively strong reasoning and language skills while remaining efficient enough for practical deployment. It belongs to Mistral’s Ministral 3 family of models, which extends the company’s earlier Mistral and Mixtral model series.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| Mistral | ~$0.15 | ~$0.45 | — | ~220ms | ~120 tps | 99.9% |
| OpenRouter | ~$0.18 | ~$0.54 | — | ~260ms | ~90 tps | 99.9% |
| Together AI | ~$0.14 | ~$0.42 | — | ~240ms | ~130 tps | 99.9% |
| Anyscale | ~$0.16 | ~$0.48 | — | ~250ms | ~100 tps | 99.9% |
Try this model
Test Ministral 3 14B 2512 right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="mistral/ministral-3-14b-2512",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "mistral/ministral-3-14b-2512",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Conversational Chat
Engages in multi-turn, instruction-following conversations, answering questions and following user intent across diverse general-purpose topics.
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Code Reasoning
Understands and writes code snippets in common programming languages, explaining logic, fixing simple bugs, and suggesting improvements.
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Multilingual Translation
Translates text between major languages, preserving meaning and tone for instructions, explanations, and everyday content.
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Document OCR
Extracts and structures text from images or scanned documents, enabling downstream processing and analysis of the recognized content.
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Image Understanding
Interprets images by identifying entities and relationships, then producing natural-language descriptions and answering related visual questions.
6 Most Valuable Use Cases
- General Text Chatbot
- Code Generation Helper
- Document Summarization
- Legal Text Review
- Contract Change Monitoring
- Product Description Drafting
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, or quality—without changing your code or client integration.
One endpoint, every model. -
Cost-Aware Orchestration
Dynamically balance premium and budget models, enforce spend limits, and visualize per-provider costs so you can ship faster without surprise bills.
Optimize spend by default. -
Automatic Fallback Safety
When a provider fails, times out, or degrades, requests transparently fail over to healthy models so your production apps stay online and responsive.
No single-provider outages. -
Deep LLM Observability
Track latency, errors, tokens, and success metrics across every provider and model with built-in traces and logs, ready for dashboards and alerts.
See every token, everywhere. -
Task-Level Orchestration
Define high-level tasks instead of individual models; LLM.API picks the right provider, parameters, and tools for each job automatically.
Think tasks, not models. -
High-Throughput Batch APIs
Submit massive batches of prompts in a single request with provider-aware throttling, retries, and aggregation to keep pipelines fast and cost-efficient.
Scale workloads, not code.
When to Use — When NOT to Use
Use it if...
- You need a compact general-purpose model that balances capability, latency, and cost effectively.
- You need to power chatbots or assistants where mid-tier reasoning is sufficient.
- Your use case involves summarizing or transforming medium-length documents without extreme accuracy requirements.
- Your use case involves prototyping AI features before committing to larger, pricier models.
- You need a smaller model suitable for on-prem or edge deployment with constrained resources.
- Your use case involves multilingual text tasks where good, but not expert-level, fluency suffices.
Avoid if...
- You need frontier-level reasoning, planning, or coding performance comparable to the strongest flagship models.
- Your workload requires highly reliable long-context understanding across very large documents or codebases.
- You need state-of-the-art performance on complex math, scientific, or safety-critical tasks.
- Your workload requires best-in-class coding assistance for large, interconnected repositories and refactors.
- You need advanced tool use, multi-step agents, or orchestrated workflows demanding top reasoning accuracy.
- Your workload requires heavily optimized inference on specialized hardware already tuned for different architectures.
Frequently Asked Questions
-
What is Ministral 3 14B 2512?
Ministral 3 14B 2512 is a 14B-parameter Mistral language model available through LLM.API for fast, cost-efficient text generation and reasoning.
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What is Ministral 3 14B 2512 best suited for?
It is best for general-purpose chat, code assistance, lightweight agents, and applications needing a strong balance of quality, speed, and price.
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What context window does Ministral 3 14B 2512 support on LLM.API?
Ministral 3 14B 2512 supports a 32K token context window for prompts plus responses on LLM.API.
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How fast is Ministral 3 14B 2512 in terms of latency and throughput?
Typical latency is low hundreds of milliseconds for short prompts, with high token-per-second throughput suitable for interactive applications.
-
What modalities does Ministral 3 14B 2512 support?
Ministral 3 14B 2512 is a text-only model, supporting text input and text output only.
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How is Ministral 3 14B 2512 priced on LLM.API?
LLM.API charges per 1,000 tokens of input and output; check the LLM.API pricing page for current Ministral 3 14B 2512 rates.
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How do I access Ministral 3 14B 2512 via LLM.API?
Call the LLM.API chat or completions endpoint with the model parameter set to the Ministral 3 14B 2512 identifier and your API key.
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How does Ministral 3 14B 2512 compare to similar models?
Compared with larger frontier models, it offers lower latency and cost while delivering mid-to-high-tier quality for common coding and reasoning tasks.
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What are the main limitations of Ministral 3 14B 2512?
It can hallucinate, lacks real-time knowledge or tools, and may underperform very large models on complex multi-step reasoning or niche domains.
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Can I use Ministral 3 14B 2512 for batch and streaming workloads?
Yes, LLM.API supports both standard batched requests and optional token streaming for Ministral 3 14B 2512, depending on your integration.
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