Mistral Small vs cua
Side-by-side comparison to help you choose.
| Feature | Mistral Small | cua |
|---|---|---|
| Type | Model | Agent |
| UnfragileRank | 47/100 | 53/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Generates coherent, instruction-aligned text responses using a 24B parameter decoder-only transformer architecture optimized for latency through reduced layer depth compared to competing models. Processes up to 128K input tokens, enabling long-document analysis, multi-turn conversations, and context-rich reasoning in a single forward pass without sliding-window approximations. Instruction-tuned checkpoint enables reliable task following across classification, summarization, and open-ended generation without explicit prompt engineering.
Unique: Achieves 150 tokens/second throughput (3x faster than Llama 3.3 70B on identical hardware) through architectural optimization with fewer transformer layers while maintaining 128K context window, enabling real-time applications without context truncation
vs alternatives: Faster inference than Llama 3.3 70B and Qwen 32B while maintaining competitive quality on coding/math/reasoning, making it ideal for latency-sensitive production systems where context length matters
Generates and reviews code across multiple programming languages using internal evaluation pipelines that show performance competitive with Llama 3.3 70B-Instruct and Qwen 32B-Instruct on proprietary coding benchmarks. Instruction-tuned checkpoint enables understanding of code context, error detection, and refactoring suggestions without explicit code-specific fine-tuning. Optimized for fast inference (150 tokens/sec) making it suitable for IDE integration and real-time code review workflows.
Unique: Achieves Llama 3.3 70B-level coding performance at 24B parameters through architectural efficiency (fewer layers), enabling deployment on single-GPU infrastructure while maintaining 150 tokens/sec throughput for real-time IDE integration
vs alternatives: Faster code generation than Copilot and Llama 3.3 70B on identical hardware while remaining open-source and Apache 2.0 licensed, eliminating vendor lock-in for code review automation
Fully open-source model released under Apache 2.0 license enabling unrestricted commercial use, modification, and redistribution. Both pretrained and instruction-tuned checkpoints covered by permissive license. Eliminates vendor lock-in and licensing restrictions compared to proprietary models. Enables white-label solutions, commercial products, and derivative works without licensing fees or usage restrictions.
Unique: Apache 2.0 licensed foundation enables unrestricted commercial deployment, white-label solutions, and derivative works without licensing fees, while maintaining competitive performance (150 tokens/sec, 81% MMLU) comparable to proprietary models
vs alternatives: Fully open-source with permissive licensing unlike GPT-4o-mini (proprietary) and Llama 3.3 70B (Llama 2 license with commercial restrictions), enabling true vendor independence and commercial product differentiation
Achieves 81% MMLU accuracy and competitive performance with Llama 3.3 70B and Qwen 32B on internal benchmarks spanning coding, math, general knowledge, and instruction-following tasks. Performance validated through human evaluations on 1k+ proprietary prompts using external third-party vendor. Enables single model deployment for diverse use cases without task-specific fine-tuning.
Unique: Achieves Llama 3.3 70B-competitive performance across diverse benchmarks (coding, math, general knowledge) at 24B parameters through architectural optimization, enabling single-model deployment for diverse use cases while maintaining 3x faster inference
vs alternatives: Competitive with 3x larger models (Llama 3.3 70B, Qwen 32B) on internal benchmarks while delivering 3x faster inference, making it ideal for cost-sensitive production systems requiring broad task coverage without specialization
Solves mathematical problems and performs symbolic reasoning using instruction-tuned weights trained on mathematical task distributions. Internal evaluation shows performance competitive with Llama 3.3 70B-Instruct on math benchmarks. Processes mathematical notation, equations, and multi-step problem descriptions within 128K context window, enabling complex problem decomposition without context loss.
Unique: Delivers Llama 3.3 70B-competitive math reasoning at 24B parameters through architectural optimization, enabling deployment on resource-constrained infrastructure while maintaining 150 tokens/sec throughput for real-time educational applications
vs alternatives: Faster math problem-solving than larger open models while remaining fully open-source and commercially licensable, making it suitable for educational platforms requiring both performance and cost efficiency
Supports function calling through schema-based function registry enabling structured tool invocation without explicit prompt engineering. Model receives function definitions and generates structured function calls that can be executed by external systems. Integration with Mistral API enables seamless function calling workflows; specific schema format and supported function types not documented in available materials.
Unique: Integrates function calling directly into instruction-tuned weights without requiring separate fine-tuning, enabling zero-shot tool invocation across diverse function types while maintaining 150 tokens/sec throughput for real-time agent applications
vs alternatives: Native function calling support without additional prompt engineering overhead, similar to GPT-4o-mini and Claude, but with 3x faster inference speed on identical hardware and full Apache 2.0 licensing for commercial deployment
Generates structured outputs (JSON, XML, or other formats) that conform to user-defined schemas without requiring post-processing or validation. Model is instruction-tuned to understand schema constraints and generate outputs matching specified structure. Enables reliable extraction of structured data from unstructured text, API response formatting, and database record generation within a single model call.
Unique: Instruction-tuned to generate schema-conformant outputs natively without requiring separate fine-tuning or post-processing, enabling single-pass structured data extraction while maintaining 150 tokens/sec throughput for high-volume extraction workflows
vs alternatives: Faster structured output generation than GPT-4o-mini with identical schema support, while remaining open-source and commercially licensable without vendor lock-in
Handles multi-turn customer support conversations using instruction-tuned weights optimized for empathetic, helpful responses. Maintains conversation context across 128K tokens enabling long support threads without context loss. Optimized for fast inference (150 tokens/sec) enabling real-time customer interactions. Suitable for both live chat augmentation and fully automated support workflows.
Unique: Delivers real-time customer support responses (150 tokens/sec) with 128K context window enabling full conversation history retention, while remaining open-source and deployable on-premise for privacy-sensitive support workflows
vs alternatives: 3x faster response generation than Llama 3.3 70B for customer support while maintaining competitive quality, with full Apache 2.0 licensing enabling white-label support solutions without vendor restrictions
+4 more capabilities
Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.
Unique: Implements a unified Responses API message format abstraction layer that normalizes outputs from 100+ heterogeneous VLM providers (native computer-use models like Claude, composed models via grounding adapters, and local model adapters), eliminating provider-specific parsing logic and enabling seamless model swapping without agent code changes.
vs alternatives: Broader model coverage and provider flexibility than Anthropic's native computer-use API alone, with explicit support for local/open-source models and a standardized message format that decouples agent logic from model implementation details.
Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.
Unique: Implements a pluggable provider architecture with unified Computer interface that abstracts OS-specific action handlers (macOS native events via Lume, Linux X11/Wayland via Docker, Windows input simulation via Windows Sandbox API), enabling single agent code to target multiple platforms. Includes Lume VM management with snapshot/restore capabilities for deterministic testing.
vs alternatives: More comprehensive OS coverage than single-platform solutions; Lume provider offers native macOS VM support with snapshot capabilities unavailable in Docker-only alternatives, while unified provider abstraction reduces code duplication vs. platform-specific agent implementations.
cua scores higher at 53/100 vs Mistral Small at 47/100. Mistral Small leads on adoption, while cua is stronger on quality and ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Provides Lume provider for provisioning and managing macOS virtual machines with native support for snapshot creation, restoration, and cleanup. Handles VM lifecycle (boot, shutdown, resource allocation) with optimized startup times. Integrates with image registry for VM image management and caching. Supports both Apple Silicon and Intel Macs. Enables deterministic testing through snapshot-based environment reset between agent runs.
Unique: Implements Lume provider with native macOS VM management including snapshot/restore capabilities for deterministic testing, optimized startup times, and image registry integration. Supports both Apple Silicon and Intel Macs with unified provider interface.
vs alternatives: More efficient than Docker for macOS because Lume uses native virtualization (Virtualization Framework) vs. Docker's slower emulation; snapshot/restore enables faster environment reset vs. full VM recreation.
Provides command-line interface (CLI) for quick-start agent execution, configuration, and testing without writing code. Includes Gradio-based web UI for interactive agent control, real-time monitoring, and trajectory visualization. CLI supports task specification, model selection, environment configuration, and result export. Web UI enables non-technical users to run agents and view execution traces with HUD visualization.
Unique: Implements both CLI and Gradio web UI for agent execution, with CLI supporting quick-start scenarios and web UI enabling interactive control and real-time monitoring with HUD visualization. Reduces barrier to entry for non-technical users.
vs alternatives: More accessible than SDK-only frameworks because CLI and web UI enable non-developers to run agents; Gradio integration provides quick UI prototyping vs. custom web development.
Implements Docker provider for running agents in containerized Linux environments with full isolation. Handles container lifecycle (creation, cleanup), image management, and volume mounting for persistent storage. Supports custom Dockerfiles for environment customization. Provides X11/Wayland display server integration for GUI application interaction. Enables reproducible agent execution across different host systems.
Unique: Implements Docker provider with X11/Wayland display server integration for GUI application interaction, container lifecycle management, and custom Dockerfile support. Enables reproducible agent execution across different host systems with container isolation.
vs alternatives: More lightweight than VMs because Docker uses container isolation vs. full virtualization; X11 integration enables GUI application support vs. headless-only alternatives.
Implements Windows Sandbox provider for isolated agent execution on Windows 10/11 Pro/Enterprise, and host provider for direct OS execution. Windows Sandbox provider creates ephemeral sandboxed environments with automatic cleanup. Host provider enables direct agent execution on live Windows system without isolation. Both providers support native Windows input simulation (SendInput API) and clipboard operations. Handles Windows-specific action execution (window management, registry access).
Unique: Implements both Windows Sandbox provider (ephemeral isolated environments with automatic cleanup) and host provider (direct OS execution) with native Windows input simulation (SendInput API) and clipboard support. Handles Windows-specific action execution including window management.
vs alternatives: Windows Sandbox provides better isolation than host execution while avoiding VM overhead; native SendInput API enables more reliable input simulation than generic input methods.
Implements comprehensive telemetry and logging infrastructure capturing agent execution metrics (latency, token usage, action success rate), errors, and performance data. Supports structured logging with contextual information (task ID, agent ID, timestamp). Integrates with external monitoring systems (e.g., Datadog, CloudWatch) for centralized observability. Provides error categorization and automatic error recovery suggestions. Enables debugging through detailed execution logs with configurable verbosity levels.
Unique: Implements structured telemetry and logging system with contextual information (task ID, agent ID, timestamp), error categorization, and automatic error recovery suggestions. Integrates with external monitoring systems for centralized observability.
vs alternatives: More comprehensive than basic logging because it captures metrics and structured context; integration with external monitoring enables centralized observability vs. log file analysis.
Implements the core agent loop (screenshot → LLM reasoning → action execution → repeat) via the ComputerAgent class, with pluggable callback system and custom loop support. Developers can override loop behavior at multiple extension points: custom agent loops (modify reasoning/action selection), custom tools (add domain-specific actions), and callback hooks (inject monitoring/logging). Supports both synchronous and asynchronous execution patterns.
Unique: Provides a callback-based extension system with multiple hook points (pre/post action, loop iteration, error handling) and explicit support for custom agent loop subclassing, allowing developers to override core loop logic without forking the framework. Supports both native computer-use models and composed models with grounding adapters.
vs alternatives: More flexible than frameworks with fixed loop logic; callback system enables non-invasive monitoring/logging vs. requiring loop subclassing, while custom loop support accommodates novel agent architectures that standard loops cannot express.
+7 more capabilities