Mistral Products
All Mistral Release Notes
- Oct 24, 2025
- Parsed from source:Oct 24, 2025
- Detected by Releasebot:Oct 26, 2025
- Modified by Releasebot:Oct 26, 2025
Introducing Mistral AI Studio.
Mistral AI unveils AI Studio, a Production AI Platform uniting Observability, Agent Runtime, and AI Registry into a production fabric. It lets teams move from prototypes to production with measurable evaluation, governance, and hybrid self-hosted deployment.
The Production AI Platform.
Many prototypes. Few systems in production. Enterprise AI teams have built dozens of prototypes—copilots, chat interfaces, summarization tools, internal Q&A. The models are capable, the use cases are clear, and the business appetite is there.
What’s missing is a reliable path to production and a robust system to support much of it. Teams are blocked not by model performance, but by the inability to:
- Track how outputs change across model or prompt versions
- Reproduce results or explain regressions
- Monitor real usage and collect structured feedback
- Run evaluations tied to their own domain-specific benchmarks
- Fine-tune models using proprietary data, privately and incrementally
- Deploy governed workflows that satisfy security, compliance, and privacy constraints
As a result, most AI adoption stalls at the prototype stage. Models get hardcoded into apps without evaluation harnesses. Prompts get tuned manually in Notion docs. Deployments run as one-off scripts. And it’s difficult to tell if accuracy improved or got worse. There’s a gap between the pace of experimentation and the maturity of production primitives.
In talking to hundreds of enterprise customers, we have discovered that the real bottleneck is the lack of a system to turn AI into a reliable, observable, and governed capability.
How to close the loop from prompts to production.
Operationalizing AI, therefore, requires infrastructure that supports continuous improvement, safety, and control—at the speed AI workflows demand.The core requirements we consistently hear from enterprise AI teams include:
- Built-in evaluation: Internal benchmarks that reflect business-specific success criteria (not generic leaderboard metrics).
- Traceable feedback loops: A way to collect real usage data, label it, and turn it into datasets that drive the next iteration.
- Provenance and versioning: Across prompts, models, datasets, and judges, with the ability to compare iterations, track regressions, and revert safely.
- Governance: Built-in audit trails, access controls, and environment boundaries that meet enterprise security and compliance standards.
- Flexible deployment: The ability to run AI workflows close to their systems, across hybrid, VPC, or on-prem infrastructure, and migrate between them without re-architecting.
Today, most teams build this piecemeal. They repurpose tools meant for DevOps, MLOps, or experimentation. But the LLM stack has new abstractions. Prompts ship daily. Models change weekly. Evaluation is real-time and use case-specific. Closing that loop from prompts to production is what separates teams that experiment with AI from those that run it as a dependable system.
Introducing Mistral AI Studio: The Production AI Platform
Mistral AI Studio brings the same infrastructure, observability, and operational discipline that power Mistral’s own large-scale systems—now packaged for enterprise teams that need to build, evaluate, and run AI in production.At Mistral AI, we operate AI systems that serve millions of users across complex workloads. Building and maintaining those systems required us to solve the hard problems: how to instrument feedback loops at scale, measure quality reliably, retrain and deploy safely, and maintain governance across distributed environments.
AI Studio productizes those solutions. It captures the primitives that make production AI systems sustainable and repeatable—the ability to observe, to execute durably, and to govern. Those primitives form the three pillars of the platform: Observability, Agent Runtime, and AI Registry.
Observability
Observability in AI Studio provides full visibility into what’s happening, why, and how to improve it. The Explorer lets teams filter and inspect traffic, build datasets, and identify regressions. Judges, which can be built and tested in their own Judge Playground, define evaluation logic and score outputs at scale. Campaigns and Datasets automatically convert production interactions into curated evaluation sets. Experiments, Iterations, and Dashboards make improvement measurable, not anecdotal.
With these capabilities, AI builder teams can trace outcomes back to prompts, prompts back to versions, and versions back to real usage—closing the feedback loop with data, not intuition.
Agent Runtime
The Agent Runtime is the execution backbone of AI Studio. It runs every agent, from simple single-step tasks to complex multi-step business flows, with durability, transparency, and reproducibility.
Each agent operates inside a stateful, fault-tolerant runtime built on Temporal, which guarantees consistent behavior across retries, long-running tasks, and chained calls. The runtime manages large payloads, offloads documents to object storage, and generates static graphs that make execution paths auditable and easy to share.
Every execution emits telemetry and evaluation data that flow directly into Observability for measurement and governance. AI Studio supports hybrid, dedicated, and self-hosted deployments so enterprises can run agents wherever their infrastructure requires while maintaining the same durability, traceability, and control.
AI Registry
The AI Registry is the system of record for every asset across the AI lifecycle—agents, models, datasets, judges, tools, and workflows.
It tracks lineage, ownership, and versioning end to end. The Registry enforces access controls, moderation policies, and promotion gates before deployment. It integrates directly with Observability (for metrics and evaluations) and with the Agent Runtime (for orchestration and deployment).
This unified view enables true governance and reuse: every asset is discoverable, auditable, and portable across environments.
Together, these pillars form the production fabric for enterprise AI.
AI Studio connects creation, observation, and governance into a single operational loop—the same system discipline that lets Mistral run AI at scale, now in the hands of enterprise teams.Go from experimentation to production, on your terms.
Enterprises are entering a new phase of AI adoption. The challenge is no longer access to capable-enough models—it’s the ability to operate them reliably, safely, and at scale. That shift demands production infrastructure built for observability, durability, and governance from day one.Mistral AI Studio represents that next step: a platform born from real operational experience, designed for teams that want to move past pilots and run AI as a core system. It unifies the three production pillars—Observability, Agent Runtime, and AI Registry—into one closed loop where every improvement is measurable and every deployment accountable.
With AI Studio, enterprises gain the same production discipline that powers Mistral’s own large-scale systems:
- Transparent feedback loops and continuous evaluation
- Durable, reproducible workflows across environments
- Unified governance and asset traceability
- Hybrid and self-hosted deployment with full data ownership
This is how AI moves from experimentation to dependable operations—secure, observable, and under your control.
If your organization is ready to operationalize AI with the same rigor as software systems, sign up for the private beta of AI Studio.Go to production with Mistral AI Studio.
You bring the ambition. We bring the platform. Let’s connect.The next chapter of AI is yours.
Original source Report a problem - Sep 16, 2025
- Parsed from source:Sep 16, 2025
- Detected by Releasebot:Oct 26, 2025
September 16
We released Magistral Medium 1.2 (magistral-medium-2509) and Magistral Small 1.2 (magistral-small-2509).
MODEL RELEASED
Original source Report a problem - Sep 12, 2025
- Parsed from source:Sep 12, 2025
- Detected by Releasebot:Oct 26, 2025
- Modified by Releasebot:Nov 9, 2025
v1.8.5: Patch Release
- Make model field optional in TranscriptionRequest by @juliendenize in #128
- Remove all responses and embedding requests. Add transcription docs. by @juliendenize in #133
- Add chunk file by @juliendenize in #129
- allow message content to be empty string by @mingfang in #135
- Add test empty content for AssistantMessage v7 by @juliendenize in #136
- v1.8.5 by @juliendenize in #137
New Contributors
- @mingfang made their first contribution in #135
Full Changelog: v1.8.4...v1.8.5
Original source Report a problem - Aug 26, 2025
- Parsed from source:Aug 26, 2025
- Detected by Releasebot:Oct 26, 2025
August 26
Added a new parameter p to the chunks streamed back by the Completion API.
SECURITY
API UPDATED
- Implemented for security to prevent token-length side-channel attacks, as reported by Microsoft researchers.
- Note that this change may break applications relying on strict parsing of the chunks. Applications using the official SDK are unaffected, but users relying on the mistral-common package may need to update to 1.8.4 or higher.
- Aug 20, 2025
- Parsed from source:Aug 20, 2025
- Detected by Releasebot:Oct 26, 2025
- Modified by Releasebot:Nov 9, 2025
v1.8.4: optional dependencies and allow random padding on ChatCompletionResponseStreamResponse
Changelog
- Update experimental.md by @juliendenize in #124
- Make sentencepiece optional and refactor optional imports by @juliendenize in #126
- Improve UX for contributing by @juliendenize in #127
- feat: allow random padding on ChatCompletionResponseStreamResponse by @aac228 in #131
New Contributors
- @aac228 made their first contribution in #131
Full Changelog: v1.8.3...v1.8.4
Original source Report a problem - Aug 11, 2025
- Parsed from source:Aug 11, 2025
- Detected by Releasebot:Oct 26, 2025
August 11
MODEL RELEASED
We released Mistral Medium 3.1 (mistral-medium-2508).
Original source Report a problem - Jul 30, 2025
- Parsed from source:Jul 30, 2025
- Detected by Releasebot:Oct 26, 2025
- Modified by Releasebot:Oct 26, 2025
Announcing Codestral 25.08 and the Complete Mistral Coding Stack for Enterprise
Mistral rolls out Codestral 25.08 with faster, more accurate IDE completions and improved safety, plus on‑prem or VPC deployment for enterprise data control. New Embed search and Devstral agent workflows enable AI‑powered coding with full observability and security in enterprise IDEs.
How the world’s leading enterprises are using integrated coding solutions from Mistral AI to cut development, review, and testing time by 50%—and why the playbook now fits every company that wants AI-native software development.
AI-powered coding is taking off, but enterprise adoption still lags due to critical limitations
Over the past year, AI coding assistants have introduced powerful capabilities, such as multi-file reasoning, contextual suggestions, and natural-language agents, all directly within the IDE. Despite these improvements, however, adoption inside enterprise environments has been slow. The reasons have less to do with model performance or the interface, and more with how these tools are built, deployed, and governed.
Key limitations holding back enterprise teams include:
- Deployment constraints: Most AI coding tools are SaaS-only, with no options for VPC, on-prem, or air-gapped environments. This is a hard blocker for organizations in finance, defense, healthcare, and other regulated industries.
- Limited customization: Enterprises often need to adapt models to their own codebases and development conventions. Without access to model weights, post-training workflows, or extensibility, teams are locked out of leveraging the best of their codebases.
- Fragmented architecture: Agents, embeddings, completions, and plugins are frequently decoupled across vendors—leading to integration drift, inconsistent context handling, and operational overhead. Moreover, coding copilots are not well-integrated into full enterprise platforms, such as product development tools, CRMs, and customer issue trackers.
- No unified observability or control: Teams lack visibility into how AI is being used across the development lifecycle. Without telemetry, audit trails, and centralized controls, it’s difficult to scale AI usage responsibly or measure real ROI.
- Incompatibility with internal toolchains: Many assistants operate in closed environments, making it hard to connect with internal CI/CD pipelines, knowledge bases, or static analysis frameworks.
For enterprises, these limitations aren’t edge cases—they’re baseline requirements. Solving them is what separates a good developer tool from an AI-native software development platform.
A Full-Stack Approach Built for AI-Native Software Development
Our approach to enterprise coding isn’t a bundle of isolated tools. It’s an integrated system designed to support enterprise-grade software development across every stage—from code suggestion to autonomous pull requests.
It starts with fast, reliable completion—and scales up to full codebase understanding and multi-file automation.
1. Fast, High-Fidelity Code Completion
At the foundation of the stack is Codestral, Mistral’s family of code generation models built specifically for high-precision fill-in-the-middle (FIM) completion. These models are optimized for production engineering environments: latency-sensitive, context-aware, and self-deployable.
Today, we announce its latest update. Codestral 25.08 delivers measurable upgrades over prior versions:
- +30% increase in accepted completions
- +10% more retained code after suggestion
- 50% fewer runaway generations, improving confidence in longer edits
- Improved performance on academic benchmarks for short and long-context FIM completion
These improvements were validated in live IDE usage across production codebases. The model supports a wide range of languages and tasks, and is deployable across cloud, VPC, or on-prem environments—with no architectural changes required.
Codestral-2508 also brings improvements to chat mode:
- Instruction following: +5% on IF eval v8
- Code abilities: +5% in average MultiplE
2. Codebase-Scale Search and Semantic Retrieval
Autocomplete accelerates, but only if the model understands your codebase. Codestral Embed sets a new standard in this domain. Designed specifically for code rather than general text, it outperforms leading embedding models from OpenAI and Cohere in real-world code retrieval benchmarks.
Key advantages include:
- High-recall, low-latency search across massive monorepos and poly-repos. Developers can find internal logic, validation routines, or domain-specific utilities using natural language.
- Flexible embedding outputs, with configurable dimensions (e.g., 256-dim, INT8) that balance retrieval quality with storage efficiency—while outperforming alternatives even at lower dimensionality
- Private deployment for maximum control, ensuring no data leakage via third-party APIs. All embedding inference and index storage can run within enterprise infrastructure
This embedding layer serves as both the context foundation for agentic workflows and the retrieval engine powering in‑IDE code search features—without sacrificing privacy, performance, or precision.
3. Autonomous Multi-Step Development with Agentic Workflows
With relevant context surfaced, AI can take meaningful action. Devstral, powered by the OpenHands agent scaffold, enables enterprise-ready agentic coding workflows. It’s built specifically for engineering tasks—cross-file refactors, test generation, and PR authoring—using structured, context-rich reasoning.
Standout capabilities include:
- Top open‑model performance on SWE‑Bench Verified: Devstral Small 1.1 scores 53.6%, and Devstral Medium reaches 61.6%, outperforming Claude 3.5, GPT‑4.1‑mini, and other open models by wide margins
- Flexible architecture for any environment: Devstral is available in multiple sizes. The open-weight Devstral Small (24B, Apache-2.0) runs efficiently on a single Nvidia RTX 4090 or Mac with 32 GB RAM—ideal for self-hosted, air-gapped, or experimental workflows. The larger Devstral Medium is available through enterprise partnerships and our API for more advanced code understanding and planning capabilities.
- Open model for extensibility: Teams can fine-tune Devstral Small on proprietary code, build custom agents, or embed it directly into CI/CD workflows—without licensing lock-in. For production environments requiring higher model performance, Devstral Medium is available with enterprise-grade support, including the ability for companies to post-train and fine-tune.
Delivering agentic automation within private infrastructure lets engineering organizations reduce friction, ensure compliance, and speed up delivery with repeatable, auditable AI workflows.
4. IDE Integration and Operational Control
All capabilities in the Mistral stack—completion, semantic search, and agentic workflows—are surfaced through Mistral Code, a native plugin for JetBrains and VS Code.
It provides:
- Inline completions using Codestral 25.08, optimized for FIM and multi-line editing
- One-click task automations like “Write commit message”, “Fix function”, or “Add docstring”, powered by Devstral
- Context awareness from Git diffs, terminal history, and static analysis tools
- Integrated semantic search, backed by Codestral Embed
Mistral Code is built to support enterprise deployment requirements:
- Deploy in any environment: cloud, self-managed VPC, or fully on-prem (GA in Q3)
- No mandatory telemetry, and no external API calls for inference or search
- SSO, audit logging, and usage controls for secure, policy-compliant adoption
- Usage observability via the Mistral Console, including metrics on AI-generated code, suggestion acceptance, and agent usage
These features give engineering, platform, and security teams the ability to roll out AI tooling safely, incrementally, and with full visibility.
How It All Fits Together: From Developer Actions to Organizational Impact
The Mistral coding stack integrates autocomplete, semantic retrieval, and agentic workflows directly into the IDE—while giving platform teams control over deployment, observability, and security. In a typical development task:
Say a developer is working on a payments service written in Python. A recent update to a third-party billing API means they need to update the integration logic and add proper error handling.
- They start by navigating to the billing handler. As they modify the function signature, Codestral fills in the expected parameters and suggests a first-pass implementation, reducing the need to copy patterns from other services.
- Before changing the retry logic, they need to understand how similar failures are handled elsewhere. Instead of switching to Slack or searching GitHub manually, they enter a query directly in the IDE: “How do we handle Stripe timeouts in the checkout flow?” The embedding index, running locally, returns a helper module from another service that wraps retry logic with exponential backoff.
- They copy the pattern into their own handler—but realize three other services are using outdated retry code. They invoke a Devstral-powered agent from within the IDE: “Replace all uses of retry_with_sleep in the billing and checkout services with the new retry_exponential helper, and update the docs.” Devstral scans the codebase using the same embeddings, makes the required edits across files, and generates a draft PR. The agent also writes a changelog and updates the README section on error handling.
The developer reviews the PR, confirms the logic, and merges it. A cross-service update that previously would have required search, coordination, and hand-written boilerplate now completes in one editing session—with traceable, reviewable output.
At the organization level, this same workflow unlocks broader advantages:
- Every component in the stack can be self-hosted or run on-prem, giving teams control over data, latency, and deployment architecture.
- Observability is built in. The Mistral Console tracks usage patterns, model acceptance rates, and agent adoption, providing the data needed to tune rollout and measure ROI.
- Security and compliance controls—including SSO, audit logging, and telemetry configuration—make it easy to integrate with internal policies and infrastructure.
- No stitching required. Because completion, search, and agents share architecture, context handling, and support boundaries, teams avoid the drift, overhead, and security gaps of piecing together third-party tools.
The result is a development workflow that’s both faster and easier to govern—designed for individual productivity and organizational scale.
Adopted by Leading Enterprises Across Diverse Environments
The Mistral coding stack is already being used in production by organizations across consulting, finance, transportation, and industry—each with different requirements, but shared constraints around data control, deployment flexibility, and internal code complexity.
- Capgemini has rolled out the stack across global delivery teams to accelerate development while maintaining code ownership and compliance across clients in defense, telecom, and energy.
- Abanca, a leading bank in Spain operating under European banking regulations, uses Mistral’s models in a fully self-hosted deployment to meet data residency and network isolation requirements—without sacrificing usability.
- SNCF, the French national railway company, uses agentic workflows to modernize legacy Java systems safely and incrementally, with human oversight built into the loop.
“Leveraging Mistral’s Codestral has been a game changer in the adoption of private coding assistant for our client projects in regulated industries. We have evolved from basic support for some development activities to systematic value for our development teams“.
Alban Alev, VP head of Solutioning at Capgemini France.
In addition, several tier-1 global banks and industrial manufacturers are actively piloting or scaling adoption across their engineering teams—driven by requirements that hosted copilots and fragmented tooling can’t support.
These use cases reflect a growing shift: organizations are no longer looking for isolated assistants—they’re adopting integrated AI systems that match the complexity, security posture, and velocity of modern enterprise software development.
Get Started
The full Mistral coding stack—Codestral 25.08, Devstral, Codestral Embed, and the Mistral Code IDE extension—is available today for enterprise deployment.
Teams can start with autocomplete and semantic search, then expand to agentic workflows and private deployments at their own pace.
To begin:
- Install Mistral Code from the JetBrains or VS Code marketplace
- Connect to your preferred deployment modality (cloud, VPC, or on-prem)
If you would like to use the models for your own copilot, get your keys at console.mistral.ai. For more information on Mistral’s coding solutions, please visit our website and documentation.
To evaluate on-prem options, enterprise-scale deployments, or schedule a hands-on pilot, fill out the demand form on this page. A member of the Mistral team will follow up to help tailor the rollout to your environment.
Original source Report a problem - Jul 29, 2025
- Parsed from source:Jul 29, 2025
- Detected by Releasebot:Oct 26, 2025
July 29
- Jul 23, 2025
- Parsed from source:Jul 23, 2025
- Detected by Releasebot:Oct 26, 2025
July 23
We released Magistral Medium 1.1 (magistral-medium-2507) and Magistral Small 1.1 (magistral-small-2507).
MODEL RELEASED
We released a Document Library API to manage libraries.
API UPDATED
SDK support for Audio and Transcription available.
OTHER
Original source Report a problem - Jul 16, 2025
- Parsed from source:Jul 16, 2025
- Detected by Releasebot:Oct 26, 2025
- Modified by Releasebot:Nov 11, 2025
v1.8.1: Add AudioURLChunk
Mistral Voxtral Mini adds AudioURLChunk support so you can embed audio via URLs, file paths, or base64 directly in content chunks. The update includes example usage and tokenizer workflow, reflected in the v1.8.0 to v1.8.1 changelog sweep.
Add AudioURLChunk by @juliendenize in #120
Now you can use http(s) URLs, file paths and base64 string (without specifying format) in your content chunks thanks to AudioURLChunk !
from mistral_common.protocol.instruct.messages import AudioURL, AudioURLChunk, TextChunk, UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer repo_id = "mistralai/Voxtral-Mini-3B-2507" tokenizer = MistralTokenizer.from_hf_hub(repo_id) text_chunk = TextChunk(text = "Wat do you think about this audio?") user_msg = UserMessage(content = [AudioURLChunk(audio_url = AudioURL(url = "https://freewavesamples.com/files/Ouch-6.wav")), text_chunk]) request = ChatCompletionRequest(messages = [user_msg]) tokenized = tokenizer.encode_chat_completion(request) # pass tokenized.tokens to your favorite audio model print(tokenized.tokens) print(tokenized.audios) # print text to visually see tokens print(tokenized.text)Full Changelog: v1.8.0...v1.8.1
Original source Report a problem