- Text Generation
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Mistral Embed 2312
- Text Generation
Mistral Embed 2312 is a text embedding model from Mistral optimized for semantic representations of text and code, with an 8K token context window and low-cost pricing for large-scale use.
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 Mistral Embed 2312?
Mistral Embed 2312 is a specialized text-to-embedding model by Mistral AI that converts text into dense vector representations for downstream applications. It is mainly used for semantic search, retrieval-augmented generation (RAG), and vector database retrieval, where it encodes documents and queries into a shared embedding space. It also supports tasks like document clustering, deduplication, and enterprise knowledge management that rely on similarity between embedded texts. The model belongs to Mistral’s Embed family (version 23.12) and is exposed under the identifier mistralai/mistral-embed-2312 in Mistral’s model lineup and compatible platforms.
Model capabilities
5 Core Capabilities
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Text Embedding
Generates dense vector representations of text inputs suitable for similarity search, clustering, and semantic understanding tasks across domains.
-
Multilingual Support
Produces coherent embeddings for multiple languages, enabling cross-lingual search, retrieval, and comparison of semantically related content.
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Document Retrieval
Supports building retrieval systems by embedding queries and documents into the same space for efficient nearest-neighbor search and ranking.
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Semantic Clustering
Facilitates grouping related texts by embedding them into a vector space where distance reflects semantic similarity and topical relatedness.
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OCR Text Embedding
Embeds text extracted via OCR from scanned documents or images, enabling semantic search and organization of visually captured content.
Use cases
6 Most Valuable Use Cases
- Semantic Text Search
- RAG Document Retrieval
- Legal Case Discovery
- Contract Change Monitoring
- Product Catalog Matching
- Code Snippet Similarity
Transparent pricing
Cost Comparison
LLM API offers the lowest cost embeddings with the largest context window for Mistral-compatible models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 3,500 tps | 99.99% | $0.03 | $0.00 | ~1M tokens |
| Mistral | EU West | ~180ms | ~2,000 tps | ~99.9% | ~$0.10 | $0.00 | ~1M tokens |
| OpenAI | Global | ~150ms | ~2,500 tps | 99.9% | ~$0.10 | $0.00 | ~200K tokens |
| Azure AI | US East | ~160ms | ~2,200 tps | 99.9% | ~$0.11 | $0.00 | ~200K tokens |
Performance benchmarks
Technical Specifications
| Metric | Mistral Embed 2312 | OpenAI text-embedding-3-large | Cohere embed-english-v3.0 |
|---|---|---|---|
| Dimensions | 1024 | 3072 | 1024 |
| Max Input Tokens | ~8K | 8K | ~4K |
| Price per 1M Tokens | $0.10 | $0.13 | $0.10 |
| Avg Latency | ~120ms | ~150ms | ~160ms |
| Throughput | ~1,200 tps | ~1,000 tps | ~900 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
| Supported Languages | ~50+ | ~90+ | ~100+ |
30-day usage via LLM API
- 3.4B
- Embedding tokens processed (30 days)
- 27.8M
- API requests served (30 days)
- 410K
- Unique developers (30 days)
- 99.95%
- Avg API uptime (30 days)
Architecture & Integration
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 by cost, latency, and quality. Swap or mix providers without changing your app logic.
One endpoint, every model. -
Cost-Aware Control
Enforce budgets, caps, and per-tenant limits across providers from one place. Continuously optimize spend with usage insights and smart model selection.
Spend less, ship more. -
Resilient Fallbacks
Define multi-step fallback chains so requests survive provider outages and timeouts. Keep SLAs and user experiences stable even when a model fails.
No single point of failure. -
Full-Stack Observability
Trace every call across providers with unified logs, metrics, and latency breakdowns. Quickly debug incidents and tune prompts from one observability layer.
See every token, everywhere. -
Task-Level Abstractions
Define reusable tasks like chat, extraction, search, or tool use once, then plug in any model underneath without refactoring your application code.
Code to tasks, not models. -
High-Throughput Batching
Batch thousands of prompts into a single API call with queueing, retries, and concurrency control to dramatically cut costs and improve throughput.
Scale jobs, not endpoints.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need fast, low-cost text embeddings for large-scale semantic search applications.
- You need multilingual text similarity or clustering across many European and global languages.
- You need compact vector representations to power recommendation, ranking, or retrieval pipelines.
- Your use case involves building RAG systems that depend on dense text embeddings.
- Your use case involves intent detection or topic classification using embedding-based nearest-neighbor methods.
- You need to migrate from other embedding providers while staying within open-source-friendly ecosystems.
- Your use case involves deduplicating or grouping similar documents using vector similarity search.
Avoid if...
- You need a model that directly generates text, code, or images from prompts.
- You need strictly standardized safety filters or moderation labels integrated into the model output.
- You need embeddings specialized for audio, images, or multimodal content rather than pure text.
- Your workload requires fine-grained, labeled predictions instead of downstream embedding-based classifiers.
- You need domain-optimized embeddings for highly specialized scientific, legal, or medical corpora.
- Your workload requires stable, long-lived vector formats guaranteed across many future model versions.
- You need an embedding service available in regions or environments where Mistral’s APIs are unsupported.
FAQ
Frequently Asked Questions
-
What is Mistral Embed 2312?
Mistral Embed 2312 is a text embedding model from Mistral designed to convert text into vector representations for search, clustering, and retrieval tasks.
-
What is Mistral Embed 2312 best suited for?
Mistral Embed 2312 is best for semantic search, dense retrieval, document similarity, recommendation systems, and other tasks requiring high-quality text embeddings.
-
What context window does Mistral Embed 2312 support?
Mistral Embed 2312 accepts up to 8,192 tokens per input, making it suitable for long documents and multi-paragraph content.
-
What modalities does Mistral Embed 2312 support?
Mistral Embed 2312 is a text-only embedding model and does not support images, audio, or other non-text modalities.
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How fast is Mistral Embed 2312 when called through LLM.API?
Latency for Mistral Embed 2312 via LLM.API is typically low, suitable for real-time applications, but depends on request size and network conditions.
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How is pricing for Mistral Embed 2312 handled on LLM.API?
Mistral Embed 2312 pricing on LLM.API is usage-based, charging per token processed, with exact rates listed in the LLM.API pricing section.
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How do I access Mistral Embed 2312 via the LLM.API gateway?
You call the unified LLM.API embeddings endpoint, specify the Mistral Embed 2312 model name, and provide your API key and input texts.
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How does Mistral Embed 2312 compare to other Mistral and OpenAI embedding models?
Mistral Embed 2312 offers competitive embedding quality and efficiency, but may differ in dimensionality, token limits, and pricing compared to other providers' models.
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What are the main limitations of Mistral Embed 2312?
Mistral Embed 2312 cannot generate text, is limited to its maximum context length, and may underperform on highly domain-specific or low-resource languages.
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Can I use Mistral Embed 2312 for multilingual embeddings?
Mistral Embed 2312 supports multiple languages, but embedding quality may vary by language and is generally strongest for high-resource languages.
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