- Instruction Following
Claude Opus Latest is Anthropic’s current flagship Opus-tier large language model, designed for complex reasoning, coding, and knowledge work with strong safety and alignment features. It…
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Mistral Large 3 2512
- Instruction Following
Mistral Large 3 2512 is Mistral’s most capable open-source sparse mixture-of-experts large language model, offering multimodal (text, image, file) support, a 262K-token context window, and an Apache 2.0 license.
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 Large 3 2512?
Mistral Large 3 2512 is a frontier-class multimodal sparse mixture-of-experts large language model from Mistral with 41B active parameters (675B total) and a 262,144-token context window, released under the Apache 2.0 license. It is mainly used for high-end text generation and chat-style assistants that require long-context reasoning, document-heavy workflows, and enterprise-scale applications. It is also applied to multimodal use cases combining text with images and files, as well as function calling, tool use, and structured outputs. It belongs to the Mistral 3 family of models as the Large 3 2512 flagship variant.
Model capabilities
5 Core Capabilities
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General Chat
Engages in multi-turn, context-aware conversations, following instructions and adapting tone for assistance, explanation, and brainstorming tasks.
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Code Generation
Writes, explains, and debugs code in multiple programming languages, assisting with software development and technical problem solving.
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Language Translation
Translates between major natural languages while preserving meaning and tone, useful for multilingual communication and content localization.
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Document OCR
Extracts and interprets text from images of documents, enabling conversion of scanned or photographed content into machine-readable text.
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Image Understanding
Analyzes images to identify objects, read embedded text, and describe scenes for search, accessibility, and content comprehension.
Use cases
6 Most Valuable Use Cases
- Enterprise Virtual Assistants
- Complex Document Analysis
- Legal Knowledge Search
- Business Workflow Automation
- Multilingual Customer Support
- Code Generation and Review
Transparent pricing
Cost Comparison
LLM API offers the lowest token prices and highest performance for Mistral‑class large models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | 99.99% | $0.20 | $0.60 | 256K |
| Mistral (Native API) | EU West | ~180ms | ~40 tps | 99.9% | ~$0.25 | ~$0.75 | 128K |
| OpenAI (Equivalent: o3-mini or GPT-4.1-mini tier) | Global | ~200ms | ~35 tps | 99.9% | ~$0.30 | ~$0.90 | 128K |
| Anthropic (Equivalent: Claude 3.7 Sonnet tier) | US East | ~210ms | ~30 tps | 99.9% | ~$0.35 | ~$1.00 | 200K |
| AWS Bedrock (Hosted Mistral / similar large model) | US East | ~220ms | ~25 tps | 99.9% | ~$0.28 | ~$0.85 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Mistral Large 3 2512 | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~180ms | ~300ms | ~260ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $2.0 | $5.0 | $3.0 |
| Output Price ($/1M) | $6.0 | $15.0 | $15.0 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 50 tps | 40 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 38.5B
- Prompt tokens processed (last 30 days)
- 11.2B
- Completion tokens generated (last 30 days)
- 7.4M
- API requests served (last 30 days)
- 99.95%
- Avg uptime (last 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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Intelligent AI Routing
Dynamically route each request to the optimal model across providers based on latency, cost, and capability—no client changes required.
One endpoint, every model -
Cost-Aware Orchestration
Automatically balance quality and price with policy-based routing, tiered models, and granular usage controls so you never overspend on inference.
Cut spend, not quality -
Automatic Resilient Fallbacks
Define multi-provider failover chains so requests transparently retry on backup models when providers rate-limit, error, or go down.
No single-point failure -
Full-Stack Observability
Centralize logs, metrics, traces, and payload samples across every model and provider for instant debugging, performance tuning, and audits.
See every token -
Task-Level Abstractions
Call high-level tasks like chat, generation, tools, or embeddings instead of vendor-specific APIs, keeping your app portable as models evolve.
Code to tasks, not vendors -
High-Throughput Batch Jobs
Run large-scale batch inference with automatic sharding, concurrency control, and retries so bulk workloads stay fast, cheap, and reliable.
Ship bulk workloads fast
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong, general-purpose LLM for chatbots, agents, and copilots.
- You need solid reasoning and coding performance without paying GPT-4-level prices.
- Your use case involves multilingual support across many European languages with good quality.
- You need a cloud-hosted model from a non-U.S. provider for data residency reasons.
- Your use case involves building assistants that combine code writing, refactoring, and explanation.
- You need compatible OpenAI-style APIs that integrate easily into existing LLM tooling stacks.
Avoid if...
- You need the absolute strongest reasoning or coding performance available on the market.
- Your workload requires tight integration with proprietary OpenAI features or ecosystem plugins.
- You need on-premise or fully offline deployment of the exact same hosted model.
- Your workload requires extremely fine-grained, production-ready safety tools tightly coupled to the model.
- You need guaranteed long-term support and SLAs from a hyperscaler cloud provider only.
- Your workload requires a fully open-weights model you can self-host and customize deeply.
FAQ
Frequently Asked Questions
-
What is Mistral Large 3 2512?
Mistral Large 3 2512 is a flagship large language model from Mistral focused on high‑quality reasoning, coding, and complex instruction following.
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What is Mistral Large 3 2512 best suited for?
It is best suited for complex multi-step reasoning, advanced code generation, data analysis, and building sophisticated chat or agentic applications.
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How is Mistral Large 3 2512 priced when accessed through LLM.API?
LLM.API applies its own per-token or usage-based pricing for Mistral Large 3 2512, which may differ from Mistral’s native API pricing.
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What context window does Mistral Large 3 2512 support on LLM.API?
Mistral Large 3 2512 supports a large-context chat completion interface on LLM.API; check the model card for the latest maximum token window.
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What is the typical speed and latency of Mistral Large 3 2512 via LLM.API?
Latency depends on region, load, and request size, but LLM.API maintains persistent connections and streaming to minimize perceived response time.
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What modalities does Mistral Large 3 2512 support on LLM.API?
On LLM.API, Mistral Large 3 2512 currently supports text-in, text-out use cases; image or other modalities depend on future provider capabilities.
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How do I call Mistral Large 3 2512 through the LLM.API gateway?
Select the Mistral Large 3 2512 model name in your LLM.API request payload and send standard OpenAI-compatible chat or completion-style requests.
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How does Mistral Large 3 2512 compare to similar large models on LLM.API?
It generally offers competitive reasoning and coding quality at a cost-performance profile often more favorable than many proprietary frontier models.
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What limitations should I be aware of when using Mistral Large 3 2512?
It can hallucinate, reflect training-data biases, struggle with highly domain-specific knowledge, and should not be used as a sole source for critical decisions.
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Can I fine-tune Mistral Large 3 2512 through LLM.API?
Fine-tuning availability depends on LLM.API’s feature set; if unsupported, you use system prompts and few-shot examples to steer behavior instead.
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