Powered by Mistral
Devstral 2 2512
- Text Generation
Devstral 2 2512 is a 123B-parameter open-source large language model from Mistral AI, optimized for agentic coding and long-context software engineering workflows. It supports a 256K/262K-token context window for exploring and editing large codebases with tool use.
About the model
What is Devstral 2 2512?
Devstral 2 2512 is a 123B-parameter dense transformer model by Mistral AI specializing in agentic coding with a roughly 256K–262K token context window. It is primarily used for software engineering agents that can explore large codebases, orchestrate changes across multiple files, and handle tasks like bug fixing or modernizing legacy systems while maintaining architecture-level context. It is also applied to general coding assistance, complex reasoning over long technical documents, and workflows that integrate external tools and APIs. Devstral 2 belongs to Mistral’s Devstral family of open-weight code-focused models, following earlier Devstral and Devstral Small/Medium releases.
Model capabilities
5 Core Capabilities
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General Assistance
Engages in multi-turn conversations, answering questions, explaining concepts, and following instructions across many everyday and technical topics.
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Code Reasoning
Understands and generates source code, explains programming concepts, and helps debug or refactor snippets in common programming languages.
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Text Translation
Translates between multiple natural languages while preserving meaning, tone, and important domain-specific terminology when possible.
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Image Analysis
Interprets images to identify objects, scenes, and visual relationships, and provides concise natural-language descriptions of visual content.
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Text Extraction
Reads text from images or documents, extracting machine-usable content from screenshots, scans, or photos of printed materials.
Use cases
6 Most Valuable Use Cases
- Agentic Code Generation
- Automated Bug Fixing
- Legacy Code Modernization
- Large Codebase Refactoring
- Tool-Augmented Coding Agents
- Multilingual Developer Support
Transparent pricing
Cost Comparison
LLM API offers the lowest token prices and highest performance for Devstral 2–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.05 | $0.15 | 256K |
| Mistral | EU West | ~180ms | ~80 tps | 99.9% | ~$0.25 | ~$0.75 | ~128K |
| OpenAI | US East | ~200ms | ~90 tps | 99.9% | ~$0.30 | ~$0.90 | ~128K |
| Anthropic | US West | ~220ms | ~70 tps | 99.9% | ~$0.35 | ~$1.00 | ~200K |
| Azure | Global | ~210ms | ~85 tps | 99.9% | ~$0.28 | ~$0.85 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Devstral 2 2512 (Mistral) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~220ms | ~350ms | ~320ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $1.80 | $5.00 | $3.00 |
| Output Price ($/1M) | $5.40 | $15.00 | $15.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 120 tps | 60 tps | 70 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 9.4B
- Prompt tokens processed (last 30 days)
- 210M
- Completion tokens generated (last 30 days)
- 27.5M
- API requests served (last 30 days)
- 99.8%
- Average API 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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Unified AI Routing
Dynamically route each request across multiple providers and models based on cost, latency, and quality—without changing your integration code.
One endpoint, every model -
Cost-Aware Optimization
Control spend with per-route cost policies, automatic model downshifts, and clear usage insights so you never overpay for simple workloads.
Cut costs, not coverage -
Resilient Fallback Logic
Define automatic failover chains so if a provider is down, rate-limited, or slow, your requests seamlessly retry against alternative models.
Stay online, automatically -
End-to-End Observability
Trace every request across models and providers with logs, metrics, and latency breakdowns to debug issues and tune performance in production.
See every token hop -
Task-Native Abstractions
Use high-level task APIs—chat, tools, RAG, workflows—instead of provider-specific primitives, so you can swap or compose models without refactoring.
Code to tasks, not vendors -
High-Throughput Batch
Process massive job queues with async, fault-tolerant batch execution, smart chunking, and automatic retries to fully utilize model capacity.
Scale from 10 to millions
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose LLM from Mistral for versatile application prototyping.
- You need good balance between reasoning, coding, and language tasks in one model.
- Your use case involves integrating with the Mistral ecosystem and existing Devstral tooling.
- You need an LLM suitable for typical chatbot, assistant, and productivity-style applications.
- Your use case involves experimenting with newer Mistral releases to evaluate capability improvements.
Avoid if...
- You need guaranteed best-in-class reasoning beyond what standard frontier Mistral models offer.
- Your workload requires strict, audited compliance certifications that Devstral 2 2512 may not hold.
- You need a tiny, on-device model optimized for mobile or embedded deployments.
- Your workload requires a fully open-weights model for self-hosting and offline control.
- You need long-term model stability with frozen behavior rather than frequently updated variants.
FAQ
Frequently Asked Questions
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What is Devstral 2 2512?
Devstral 2 2512 is a Mistral language model accessible via LLM.API, designed for general-purpose text generation and reasoning workloads.
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What is Devstral 2 2512 best suited for?
Devstral 2 2512 is best for building chatbots, code assistants, and knowledge retrieval tools that require strong reasoning and instruction-following.
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How is Devstral 2 2512 priced on LLM.API?
Devstral 2 2512 uses LLM.API’s unified token-based pricing; check your LLM.API dashboard or pricing docs for current per-token input and output rates.
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What context window does Devstral 2 2512 support?
Devstral 2 2512 supports a context window defined by LLM.API’s Mistral configuration; refer to the model card in LLM.API for the exact token limit.
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How fast is Devstral 2 2512 on LLM.API?
Typical latency is low and suitable for interactive applications, but exact speeds depend on your region, load, and chosen LLM.API deployment options.
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Which modalities does Devstral 2 2512 support?
Devstral 2 2512 is exposed on LLM.API as a text-only model, accepting and returning UTF-8 text content.
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How do I call Devstral 2 2512 through LLM.API?
Use the standard LLM.API chat or completion endpoint, specifying the model identifier for Devstral 2 2512 and including your messages array.
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How does Devstral 2 2512 compare to other Mistral models on LLM.API?
Devstral 2 2512 targets strong general-purpose performance, while lighter Mistral variants may be cheaper or faster but somewhat less capable.
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What are the main limitations of Devstral 2 2512?
Devstral 2 2512 can hallucinate, lacks real-time knowledge, and should not be used as the sole basis for safety-critical or legal decisions.
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Does Devstral 2 2512 support streaming responses on LLM.API?
Yes, you can enable streaming via the LLM.API request parameters to progressively receive Devstral 2 2512’s output tokens.