Powered by Mistral
Ministral 3 14B 2512
- Text Generation
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. It is designed to provide a balance of capability and efficiency for general-purpose text understanding and generation.
About the model
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.
Model capabilities
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.
Use cases
6 Most Valuable Use Cases
- Code Generation Assistance
- Code Generation Helper
- Document Summarization
- Legal Text Review
- Contract Change Monitoring
- Product Description Drafting
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and fastest access to Ministral 3 14B–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~200 tps | 99.99% | ~$0.08 | ~$0.24 | ~256K |
| Mistral | EU West | ~220ms | ~120 tps | 99.9% | ~$0.15 | ~$0.45 | ~256K |
| OpenRouter | Global | ~260ms | ~90 tps | 99.9% | ~$0.18 | ~$0.54 | ~128K |
| Together AI | US East | ~240ms | ~130 tps | 99.9% | ~$0.14 | ~$0.42 | ~128K |
| Anyscale | US West | ~250ms | ~100 tps | 99.9% | ~$0.16 | ~$0.48 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Ministral 3 14B 2512 (Mistral) | Llama 3.1 8B (Meta) | GPT-4o mini (OpenAI) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~230ms |
| Context Window | 128K | 8K | 8K |
| Input Price ($/1M tokens) | $0.20 | $0.10 | $0.12 |
| Output Price ($/1M tokens) | $0.60 | $0.40 | $0.45 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 60 tps | 45 tps | 40 tps |
| Uptime | 99.9% | 99.5% | 99.9% |
30-day usage via LLM API
- 18.5B
- Prompt tokens processed (last 30 days)
- 5.4B
- Completion tokens generated (last 30 days)
- 11.2M
- API requests served (last 30 days)
- 99.8%
- Avg uptime over 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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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.
Decision guide
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.
FAQ
Frequently Asked Questions
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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.
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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.