Mistral: Devstral Small 1.1 ranks higher at 24/100 vs Mistral Large 2407 at 24/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Mistral: Devstral Small 1.1 | Mistral Large 2407 |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 24/100 | 24/100 |
| Adoption |
| 0 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.00e-7 per prompt token | $2.00e-6 per prompt token |
| Capabilities | 11 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Generates syntactically correct, production-ready code from natural language specifications using a 24B parameter transformer fine-tuned on software engineering tasks. The model applies attention mechanisms trained on code-documentation pairs to map intent to implementation patterns, supporting multiple programming languages through token-level code understanding rather than template matching.
Unique: Fine-tuned specifically for software engineering agents (via collaboration with All Hands AI) rather than general-purpose code generation, using domain-specific training data that emphasizes agent-compatible code patterns and tool-use scaffolding
vs alternatives: Smaller footprint (24B vs Codex 175B) with specialized training for agent workflows makes it faster and cheaper than general LLMs while maintaining code quality comparable to larger models on routine engineering tasks
Predicts and completes code sequences given partial input by leveraging transformer attention over preceding tokens and file context. The model uses causal masking to ensure predictions only depend on prior tokens, enabling real-time completion in IDE-like environments with latency under 500ms for typical completions.
Unique: Trained on software engineering codebases with explicit focus on agent-compatible completion patterns, enabling completions that respect tool-use schemas and function-calling conventions rather than generic code patterns
vs alternatives: Faster inference than larger models (GPT-4, Claude) due to 24B size while maintaining engineering-specific accuracy through specialized fine-tuning, making it suitable for latency-sensitive IDE integrations
Generates infrastructure-as-code (Terraform, CloudFormation, Kubernetes manifests) and DevOps scripts from natural language specifications. The model learns cloud provider APIs and configuration patterns to produce valid, deployable infrastructure code with proper resource dependencies and security configurations.
Unique: Trained on infrastructure-as-code repositories and cloud provider documentation, enabling generation of production-ready configurations that respect cloud provider best practices and resource dependencies
vs alternatives: Produces more complete and deployable infrastructure code than general LLMs by understanding cloud provider semantics and resource relationships, reducing manual configuration overhead
Analyzes source code and generates human-readable explanations, docstrings, and technical documentation by mapping code tokens to semantic intent through transformer attention. The model produces documentation in multiple formats (docstrings, markdown, inline comments) by conditioning on code structure and generating natural language descriptions of logic flow and purpose.
Unique: Specialized training on software engineering documentation patterns enables generation of docstrings that follow language-specific conventions (PEP 257 for Python, JSDoc for JavaScript) and include parameter descriptions, return types, and exception documentation automatically
vs alternatives: Produces more concise and engineering-focused documentation than general-purpose LLMs by filtering for technical accuracy and standard documentation formats, reducing post-generation editing overhead
Identifies bugs and suggests fixes by analyzing code structure, error messages, and execution context through transformer-based pattern matching against known bug categories. The model correlates error traces with code patterns to propose root causes and remediation strategies, leveraging training data that includes bug-fix pairs and error-handling patterns.
Unique: Trained on software engineering debugging workflows and error-fix datasets, enabling pattern recognition of common bug categories (off-by-one errors, null pointer dereferences, type mismatches) with engineering-specific reasoning rather than generic text analysis
vs alternatives: Produces more actionable debugging suggestions than general LLMs by focusing on code-specific error patterns and suggesting concrete fixes rather than generic explanations
Evaluates code quality, style compliance, and architectural patterns by analyzing code against learned best practices and design patterns. The model applies transformer attention to identify violations of common standards (naming conventions, complexity metrics, security patterns) and generates structured feedback with severity levels and remediation suggestions.
Unique: Specialized training on code review datasets and engineering best practices enables detection of architectural anti-patterns and design issues beyond simple style violations, with severity scoring calibrated to software engineering standards
vs alternatives: Provides more contextual and actionable feedback than static analysis tools by understanding code intent and suggesting refactorings that improve maintainability, whereas linters focus only on syntax and style
Understands and translates code across multiple programming languages by learning language-agnostic abstract syntax patterns and semantic equivalences. The model maps code constructs (loops, conditionals, function definitions) to their equivalents in target languages, enabling code translation, language migration, and cross-language documentation.
Unique: Trained on parallel code corpora across 10+ languages with explicit focus on semantic equivalence rather than syntactic mapping, enabling idiomatic translations that respect target language conventions and libraries
vs alternatives: Produces more idiomatic translations than rule-based transpilers by understanding semantic intent and applying language-specific best practices, though still requires manual review for production code
Generates unit tests, integration tests, and test cases from function signatures, docstrings, and code implementations using learned patterns from test datasets. The model produces test code that covers common scenarios (happy path, edge cases, error conditions) by analyzing code logic and generating assertions that validate expected behavior.
Unique: Trained on test-driven development datasets and testing best practices, enabling generation of tests that follow framework conventions (pytest fixtures, Jest mocks) and cover common failure modes identified in engineering practice
vs alternatives: Generates more comprehensive test suites than simple template-based approaches by analyzing code logic to identify edge cases, whereas generic LLMs produce basic happy-path tests only
+3 more capabilities
Maintains conversation state across multiple turns using a transformer-based architecture with attention mechanisms that track dialogue history. The model processes the full conversation context (user messages, assistant responses, and implicit reasoning state) through its 141B parameter transformer to generate contextually coherent replies. Unlike stateless APIs, this implementation preserves semantic relationships across turns without explicit memory management, enabling complex multi-step reasoning within a single conversation thread.
Unique: 141B parameter scale with optimized attention patterns enables tracking complex multi-turn reasoning without explicit memory augmentation, using pure transformer architecture rather than hybrid memory-retrieval systems
vs alternatives: Larger parameter count than GPT-3.5 and comparable to GPT-4 enables deeper reasoning within conversation context, while remaining faster and cheaper than GPT-4 Turbo for most dialogue tasks
Generates syntactically correct code across 40+ programming languages by learning language-specific patterns during pretraining on diverse code repositories. The model uses transformer attention to understand code structure, variable scope, and API conventions, then generates completions that respect language semantics without explicit AST parsing. Supports both inline completion (filling gaps in existing code) and full function/module generation from natural language specifications.
Unique: Trained on diverse code repositories with language-agnostic transformer patterns, enabling generation across 40+ languages without language-specific fine-tuning, using unified attention mechanisms rather than language-specific decoders
vs alternatives: Outperforms Copilot on multi-language code generation and reasoning about code structure, while matching Claude's code quality on single-language tasks at lower latency
Mistral: Devstral Small 1.1 scores higher at 24/100 vs Mistral Large 2407 at 24/100.
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Solves mathematical problems including algebra, calculus, geometry, and logic through learned mathematical reasoning patterns. The model can work through multi-step problems, show intermediate steps, and verify solutions. This is implemented through training on mathematical datasets and chain-of-thought reasoning that prioritizes step-by-step problem solving.
Unique: Trained on mathematical datasets with chain-of-thought reasoning to prioritize step-by-step problem solving, using attention mechanisms that track variable relationships and equation transformations
vs alternatives: Comparable to GPT-4 on mathematical reasoning, while maintaining lower cost; outperforms Llama 2 on complex multi-step problems due to larger parameter count and specialized training
Analyzes code for bugs, security issues, performance problems, and architectural concerns by understanding code semantics and common vulnerability patterns. The model can identify issues across multiple files, suggest fixes, and explain the reasoning behind recommendations. This is implemented through training on code repositories, security datasets, and best practices, combined with attention mechanisms that track variable flow and function calls.
Unique: Analyzes code semantics using learned patterns from diverse repositories, identifying bugs and architectural issues through attention mechanisms that track variable flow and function relationships, without explicit static analysis tools
vs alternatives: More comprehensive than linters for semantic issues, comparable to GPT-4 on code review quality, while maintaining lower latency and cost for most review tasks
Condenses long documents into summaries of varying lengths and focuses, preserving key information while removing redundancy. The model can generate executive summaries, detailed summaries, or summaries focused on specific topics by learning to identify important information and compress it. This is implemented through attention mechanisms that weight important tokens higher and training on summarization datasets.
Unique: Learns to identify important information through attention mechanisms that weight key tokens higher, enabling configurable summarization without explicit extractive or abstractive pipelines
vs alternatives: More flexible than extractive summarization tools, comparable to GPT-4 on abstractive summarization quality, while maintaining lower cost and faster inference
Identifies sentiment (positive, negative, neutral) and extracts opinions, emotions, or attitudes from text by learning sentiment patterns and linguistic markers. The model can provide fine-grained sentiment analysis (aspect-based sentiment, emotion classification) and explain the reasoning behind sentiment judgments. This is implemented through training on sentiment datasets and attention mechanisms that identify sentiment-bearing tokens.
Unique: Learns sentiment patterns from diverse datasets, enabling fine-grained sentiment analysis and emotion classification through attention mechanisms that identify sentiment-bearing tokens and contextual markers
vs alternatives: More nuanced than rule-based sentiment tools, comparable to specialized sentiment models on standard benchmarks, while providing better context-aware analysis than simple keyword matching
Generates valid JSON and structured data by constraining the output space to match provided schemas or format specifications. The model uses guided decoding (token-level constraints during generation) to ensure output conforms to specified JSON schemas, XML structures, or other formal formats. This prevents hallucinated fields, enforces type correctness, and guarantees parseable output without post-processing validation.
Unique: Implements token-level guided decoding that constrains generation to valid schema-conformant outputs during inference, rather than post-processing validation, ensuring zero invalid outputs without retry logic
vs alternatives: More reliable than Claude's JSON mode for complex nested schemas, and faster than GPT-4's structured outputs due to optimized constraint checking in the 141B parameter model
Decomposes complex problems into intermediate reasoning steps using learned patterns from chain-of-thought training data. The model generates explicit reasoning traces (showing work, considering alternatives, validating assumptions) before producing final answers. This is implemented through attention patterns that prioritize reasoning tokens and training objectives that reward step-by-step problem solving over direct answers.
Unique: Trained specifically on chain-of-thought datasets to prioritize reasoning steps, using attention mechanisms that weight intermediate reasoning tokens higher than direct answers, enabling more transparent problem-solving
vs alternatives: Comparable to GPT-4's reasoning on complex problems, while maintaining lower latency and cost; outperforms Llama 2 on multi-step reasoning due to larger parameter count and specialized training
+6 more capabilities