- Text Generation
all-MiniLM-L12-v2 is a compact Sentence Transformers model that generates high-quality sentence embeddings for efficient semantic search and similarity tasks. It is notable for its strong performance-to-size…
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Ministral 3 8B 2512
- Text Generation
Ministral 3 8B 2512 is Mistral AI’s compact 8B-parameter multimodal language model with text-and-image input, tool use, and a long 262K-token context window at low cost. It targets cost-sensitive production and edge deployments that need balanced, general-purpose capabilities.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Ministral 3 8B 2512?
Ministral 3 8B 2512 is an 8-billion-parameter multimodal language model from Mistral AI that processes text and images with a 262,144-token context window. It is mainly used for affordable general-purpose chatbots, drafting and content generation, and multilingual language understanding in cost-sensitive applications. It is also applied in multimodal workflows that combine image interpretation with text analysis, and in lightweight agentic pipelines that rely on tool use and function calling. The model is part of the open-weight Ministral 3 family, alongside 3B and 14B variants and specialized instruct and reasoning editions (e.g., Ministral-3-8B-Instruct-2512 and Ministral-3-8B-Reasoning-2512).
Model capabilities
5 Core Capabilities
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Chat & Dialogue
Handles multi-turn conversational chat, instruction following, and general-purpose text responses for everyday assistant-style interactions.
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Text Generation
Generates coherent written content such as explanations, drafts, summaries, and simple code snippets from text prompts.
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Vision Inputs
Processes image inputs alongside text, enabling multimodal understanding and discussion of visual content within a conversation.
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Tool Use
Supports tool use and function calling, allowing integration with external systems for retrieval, actions, and structured workflows.
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Multilingual Text
Understands and generates text in many languages, enabling cross-lingual queries and content creation across 40+ supported languages.
Use cases
6 Most Valuable Use Cases
- Text Classification
- Invoice Field Extraction
- Legal Case Search
- Regulation Change Monitoring
- Customer Support Assistant
- Code Generation Help
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance option for Ministral 3 8B–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.08 | $0.08 | 256K |
| Mistral | EU West | ~220ms | ~70 tps | 99.9% | ~$0.12 | ~$0.12 | ~128K |
| OpenRouter | Global | ~260ms | ~55 tps | 99.9% | ~$0.14 | ~$0.14 | ~128K |
| Fireworks AI | US East | ~250ms | ~60 tps | 99.9% | ~$0.13 | ~$0.13 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Ministral 3 8B 2512 | Llama 3.1 8B | Qwen2.5 7B |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~210ms |
| Context Window | 128K | 128K | 128K |
| Input Price ($/1M) | $0.15 | $0.20 | $0.18 |
| Output Price ($/1M) | $0.60 | $0.80 | $0.70 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ~120 tps | ~100 tps | ~95 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 92.0B
- Prompt tokens processed (30 days)
- 68.5B
- Completion tokens generated (30 days)
- 11.3M
- API requests served (30 days)
- 99.95%
- Avg API uptime
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 cost, latency, or quality, without changing your code or deployment pipeline.
One endpoint, every model -
Cost-Aware Orchestration
Define cost policies once and let LLM.API choose cheaper equivalents, downgrade gracefully, and prevent runaway spend with guardrails and real-time cost controls.
Slash AI spend safely -
Resilient Fallback Flows
Design multi-provider fallback chains so timeouts, rate limits, or provider outages transparently fail over—keeping your product responsive without brittle client logic.
Never go down on inference -
End-to-End Observability
Trace every request across providers with logs, metrics, and structured events so you can debug prompts, tune routing, and prove reliability in production.
See every token, everywhere -
Task-Level Abstractions
Call high-level tasks like chat, tools, RAG, and agents via a unified schema, letting LLM.API adapt implementation details as models and capabilities evolve.
Code to tasks, not models -
High-Throughput Batch APIs
Submit large batches of requests with automatic chunking, retries, and concurrency control to maximize throughput while staying within provider limits.
Scale inference by the thousands
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a small general-purpose model for cost-efficient experimentation and prototyping.
- You need to handle moderate traffic with low inference costs on a constrained budget.
- Your use case involves short-form content generation, like emails, summaries, or UI text.
- Your use case involves lightweight code assistance, such as boilerplate, refactors, or comments.
- You need an 8B-class model suitable for on-premise or edge deployment scenarios.
- Your use case involves chatbots that answer straightforward questions without heavy reasoning depth.
Avoid if...
- You need frontier-level reasoning for complex math, proofs, or multi-step planning tasks.
- Your workload requires state-of-the-art coding performance on large codebases or complex projects.
- You need highly reliable domain expertise in medicine, law, or other high-stakes fields.
- Your workload requires handling very long documents or extensive multi-turn context windows.
- You need best-in-class safety tooling, red-teaming, and compliance features out-of-the-box.
- Your workload requires top-tier multilingual understanding and generation across many low-resource languages.
FAQ
Frequently Asked Questions
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What is Ministral 3 8B 2512?
Ministral 3 8B 2512 is an 8B-parameter Mistral model available through LLM.API, optimized for fast, cost-efficient general-purpose text generation.
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What is Ministral 3 8B 2512 best suited for?
It works best for lightweight chatbots, drafting content, simple agents, and programmatic text processing where low latency and low cost matter.
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What is the context window of Ministral 3 8B 2512?
Ministral 3 8B 2512 supports a 32K token context window for inputs plus generated output combined.
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Does Ministral 3 8B 2512 support images or other modalities?
No, Ministral 3 8B 2512 is a text-only model that accepts and returns UTF-8 text.
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How is Ministral 3 8B 2512 priced on LLM.API?
LLM.API exposes Ministral 3 8B 2512 with token-based pricing; you are billed separately for input and output tokens.
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How fast is Ministral 3 8B 2512 in terms of latency?
As a small 8B model, it typically returns first tokens quickly and is suitable for low-latency interactive applications.
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How do I call Ministral 3 8B 2512 through LLM.API?
Use the standard LLM.API chat or completion endpoint and set the model field to the Ministral 3 8B 2512 identifier.
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How does Ministral 3 8B 2512 compare to larger Mistral models?
It is cheaper and faster than larger Mistral models but generally weaker on complex reasoning, long multi-step tasks, and nuanced instructions.
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What are key limitations of Ministral 3 8B 2512?
It can hallucinate facts, struggle with very long reasoning chains, and should not be used for high-stakes or safety-critical decisions.
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Can I fine-tune Ministral 3 8B 2512 via LLM.API?
Direct fine-tuning is not exposed; you typically customize behavior using system prompts and retrieval-augmented patterns.
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