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954M
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BenchmarksMistral AI2mo ago
Model Selection Guide | Mistral Docs
Model Selection Guide | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling Model Selection Guide API Reference ↗ Inference & Models Inference Model Selection Guide WHY MISTRAL About us Our customers Careers Contact us EXPLORE AI Solutions Partners Research DOC
Model Selection Guide | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling Model Selection Guide API Reference ↗ Inference & Models Inference Model Selection Guide WHY MISTRAL About us Our customers Careers Contact us EXPLORE AI Solutions Partners Research DOC
Mistral Medium 3.5 - Mistral AI | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling API Reference ↗ Inference & Models Models Mistral Medium 3.5 Try in playground ↗ Compare Legal April 28, 2026 Blog GA Modified MIT v 26.04 Mistral Medium 3.5 Our frontier-clas
Mistral Small 4 - Mistral AI | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling API Reference ↗ Inference & Models Models Mistral Small 4 Try in playground ↗ Compare Legal March 16, 2026 GA Apache 2.0 v 26.03 Mistral Small 4 Our powerful hybrid model unifyin
Mistral Saba | Mistral AI Contact sales Menu Products Industries Research Developers Blog Customers Company Contact sales Start building Studio Build, test, and run AI agents and apps. Forge Train, align, and evaluate custom AI models. Vibe AI agent for long-horizon work.
Enabling Creative Exploration for Vibe Design Agents
Vibe design agents turn natural-language briefs into rendered interfaces and frontend code. Yet a useful design agent should do more than produce one valid page: it should help users explore coherent alternatives. Increasing token-level temperature is a blunt solution because it varies aesthetic decisions and syntax-sensitive code at the same time. We instead separate exploration from implementation through an inference architecture that makes design direction an explicit intermediate decision. Inspired by Verbalized Sampling, a pre-pass proposes structured design specifications with typicality scores, an external selector samples one, and the downstream generator realizes the selected specification together with the original request under fixed settings. We apply this approach to UI themes and visual-asset prompts. Across 168 prompts, with 1,255 paired comparisons per temperature for each intervention, theme sampling broadens observed selection coverage and screenshot variation, while LLM-judge preferences vary across interventions, prompt complexity, and viewport. In an online experiment with more than 300,000 tasks, the observed code-export increase remains statistically uncertain, while fewer negative feedback events coexist with more correction interactions and modest operational costs. Together, these findings identify structured design specifications as a practical control point for exploring alternative UI concepts while keeping downstream generation settings fixed.
Enabling Creative Exploration for Vibe Design Agents
Vibe design agents turn natural-language briefs into rendered interfaces and frontend code. Yet a useful design agent should do more than produce one valid page: it should help users explore coherent alternatives. Increasing token-level temperature is a blunt solution because it varies aesthetic decisions and syntax-sensitive code at the same time. We instead separate exploration from implementation through an inference architecture that makes design direction an explicit intermediate decision. Inspired by Verbalized Sampling, a pre-pass proposes structured design specifications with typicality scores, an external selector samples one, and the downstream generator realizes the selected specification together with the original request under fixed settings. We apply this approach to UI themes and visual-asset prompts. Across 168 prompts, with 1,255 paired comparisons per temperature for each intervention, theme sampling broadens observed selection coverage and screenshot variation, while LLM-judge preferences vary across interventions, prompt complexity, and viewport. In an online experiment with more than 300,000 tasks, the observed code-export increase remains statistically uncertain, while fewer negative feedback events coexist with more correction interactions and modest operational costs. Together, these findings identify structured design specifications as a practical control point for exploring alternative UI concepts while keeping downstream generation settings fixed.
We introduce StepAudio 3 Gen, a general-purpose audio generation model that supports zero-shot text-to-speech (TTS), voice design, vocal generation, sound effects, music, vibe speech, and mixtures of multiple audio types within a unified framework. At its core, StepAudio 3 Gen is a discrete autoregressive generator that models audio directly over residual vector quantization (RVQ) tokens, departing from the diffusion Transformer-based continuous generation paradigm prevalent in recent general audio models. Its StepAudio Tokenizer represents general audio at 12.5 Hz in a shared 16 times 2048 residual code space, jointly quantizing semantic and waveform-level acoustic features so that each code layer preserves both types of information. For generation, the backbone predicts the first codebook along the time axis using autoregressive modeling, while a lightweight causal Transformer completes the remaining fifteen codebooks along the codebook axis. Our study further identifies three key design principles: (1) interference-aware progressive pretraining for acquiring audio capabilities while preserving the textual abilities of the large language model, (2) RVQ Adaptor for effectively incorporating multi-codebook acoustic representations, and (3) discrete autoregressive modeling over a shared representation across general audio domains. With progressive pretraining, multi-task instruction training, and supervised fine-tuning, StepAudio 3 Gen achieves state-of-the-art performance on both TTS and voice design, while retaining strong generation capabilities across speech, vocals, sound effects, and music. Audio samples are available at https://stepaudiollm.github.io/step-audio-3-gen/.
Dr. Claw: An AI Scientist Workspace for Vibe Research
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, IDEs, terminals, and writing environments, and the decisions that make it auditable are rarely preserved. We present Dr. Claw, an open-source workspace that wraps existing coding-agent executors in a controllable and auditable human-in-the-loop workflow rather than introducing another autonomous agent. Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution, turning planning, execution, and writing into one traceable, recoverable loop. We demonstrate Dr. Claw through an interactive three-view scenario and a failure-recovery walkthrough, and evaluate it against a bare command-line agent sharing the same backend executor, so the comparison contrasts the whole orchestration layer (task graph, state objects, and skill library) with the agent it wraps. Holding the executor fixed, Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail. Demo access: repository https://github.com/OpenLAIR/dr-claw, released under AGPL-3.0 with GPL-3.0 upstream components.
Mistral Medium 3.5 - Mistral AI | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling API Reference ↗ Inference & Models Models Mistral Medium 3.5 Try in playground ↗ Compare Legal April 28, 2026 Blog GA Modified MIT v 26.04 Mistral Medium 3.5 Our frontier-clas
Mistral Small 4 - Mistral AI | Mistral Docs Docs & API Search docs ⌘K Vibe Studio Inference & Models Admin Resources API Reference Search docs ⌘K Toggle theme Reach out Try Studio Home Inference Models Pricing Model lifecycle policy Regional inference Priority Tier Labs Prompting Sampling API Reference ↗ Inference & Models Models Mistral Small 4 Try in playground ↗ Compare Legal March 16, 2026 GA Apache 2.0 v 26.03 Mistral Small 4 Our powerful hybrid model unifyin
Mistral Saba | Mistral AI Contact sales Menu Products Industries Research Developers Blog Customers Company Contact sales Start building Studio Build, test, and run AI agents and apps. Forge Train, align, and evaluate custom AI models. Vibe AI agent for long-horizon work.