Arcee AI: Coder Large ranks higher at 24/100 vs Qwen: Qwen3 Coder 30B A3B Instruct at 24/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Arcee AI: Coder Large | Qwen: Qwen3 Coder 30B A3B Instruct |
|---|---|---|
| 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 | $5.00e-7 per prompt token | $7.00e-8 per prompt token |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Generates code with awareness of multi-file context by leveraging a 32k token context window, allowing the model to ingest entire modules, related files, and cross-file dependencies simultaneously. Built on Qwen 2.5-Instruct architecture with specialized training on permissively-licensed GitHub corpora, enabling it to understand file relationships, import patterns, and architectural conventions without requiring external indexing or retrieval systems.
Unique: 32B parameter model specifically fine-tuned on permissively-licensed GitHub and CodeSearchNet corpora with synthetic bug-fix data, enabling it to generate production-quality code that matches real-world patterns without requiring external RAG or codebase indexing infrastructure
vs alternatives: Larger context window (32k) than many lightweight code models and specialized training on real GitHub code gives it better multi-file coherence than generic instruction-tuned models, while remaining smaller and faster than 70B+ alternatives
Identifies and generates fixes for code bugs by leveraging training on synthetic bug-fix corpora that pair buggy code with correct implementations. The model learns patterns of common errors (off-by-one, null pointer dereferences, logic errors) and can generate targeted corrections with explanations of what went wrong and why the fix works.
Unique: Trained explicitly on synthetic bug-fix corpora (not just code completion), giving it specialized pattern recognition for common error types and their corrections rather than generic code generation
vs alternatives: More effective at bug identification and correction than general-purpose code models because it was fine-tuned on paired buggy/correct code examples, whereas competitors rely on incidental bug patterns in their training data
Identifies potential security vulnerabilities in code by recognizing dangerous patterns (SQL injection, XSS, insecure deserialization, etc.) learned from security-focused GitHub repositories and generates secure replacement code. Provides explanations of vulnerability types and remediation strategies without requiring external security scanning tools.
Unique: Trained on security-focused repositories and vulnerability patterns, enabling it to recognize dangerous code patterns and generate secure replacements that follow security best practices rather than just flagging issues
vs alternatives: More practical than generic code analysis because it understands security context and generates fixes, but less comprehensive than dedicated security scanning tools because it relies on pattern matching rather than formal verification
Assists with migrating code between languages, frameworks, or architectural patterns by understanding equivalent constructs and idioms across different ecosystems learned from GitHub repositories. Generates migration guides, identifies breaking changes, and produces working implementations in target languages while preserving original functionality.
Unique: Trained on real-world migrations and polyglot repositories, enabling it to understand semantic equivalence across languages and generate idiomatic code in target languages rather than mechanical translations
vs alternatives: More intelligent than automated transpilers because it understands language semantics and idioms, but requires human validation because it cannot guarantee complete behavioral equivalence across different ecosystems
Provides intelligent code completion suggestions that respect project-specific conventions, coding styles, and architectural patterns by analyzing surrounding code context within the 32k token window. Learns completion patterns from GitHub repositories to suggest not just syntactically correct completions but semantically appropriate code that matches project conventions.
Unique: 32k context window enables it to maintain awareness of entire files and related modules, allowing completions that respect project-wide conventions and architectural patterns rather than local context only
vs alternatives: Larger context window than many lightweight completion models enables better understanding of project conventions, but requires more API latency than local completion engines
Generates syntactically correct code across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP, Ruby, Kotlin, Swift, etc.) by learning language-specific syntax and idioms from permissively-licensed GitHub repositories. The model understands language-specific conventions, standard libraries, and common patterns without requiring separate language-specific models.
Unique: Single 32B model trained on diverse GitHub repositories across 15+ languages learns unified representations of algorithmic intent that can be expressed in any target language, rather than using separate language-specific models or rule-based transpilers
vs alternatives: More flexible than language-specific code models and produces more idiomatic code than rule-based transpilers because it understands language semantics and conventions learned from real-world code
Generates natural language explanations of code functionality, architecture, and design decisions by analyzing code structure and patterns learned from GitHub repositories. Produces docstrings, comments, README sections, and architectural documentation that explain what code does and why it was written that way, with support for multiple documentation formats and styles.
Unique: Trained on real GitHub repositories with existing documentation, enabling it to learn documentation patterns and conventions that match community standards rather than generating generic or formulaic explanations
vs alternatives: Produces more idiomatic and community-aligned documentation than generic language models because it learned from real open-source projects with established documentation practices
Analyzes code for potential issues, style violations, performance problems, and architectural concerns by applying patterns learned from GitHub repositories and code review practices. Provides actionable feedback on code quality, security, maintainability, and performance without requiring external linting tools or static analysis frameworks.
Unique: Learned code review patterns from real GitHub pull requests and community feedback, enabling it to provide contextual, pragmatic feedback that aligns with actual development practices rather than rigid linting rules
vs alternatives: More nuanced than traditional linters because it understands code intent and context, but less precise than specialized static analysis tools because it relies on pattern matching rather than formal verification
+5 more capabilities
Generates code with awareness of multi-file repository context by leveraging a 30.5B parameter Mixture-of-Experts architecture with 128 experts (8 active per forward pass), enabling efficient processing of large codebases without full context loading. The MoE design allows selective expert activation for different code domains (e.g., frontend vs backend patterns), reducing computational overhead while maintaining semantic coherence across file boundaries.
Unique: Uses sparse Mixture-of-Experts (128 experts, 8 active) instead of dense parameters, enabling efficient processing of repository-scale context while maintaining 30.5B effective capacity; expert routing allows domain-specific activation for different code patterns (web, systems, data, etc.)
vs alternatives: More efficient than dense 30B models for large codebases due to MoE sparsity, and more context-aware than smaller models like Copilot-base due to explicit repository-scale training
Supports function calling and tool orchestration through structured schema-based interfaces, enabling the model to invoke external APIs, libraries, and system commands as part of code generation and reasoning workflows. The model is trained to parse tool schemas, generate valid function calls with appropriate parameters, and reason about tool sequencing for multi-step tasks.
Unique: Trained specifically for agentic tool use with multi-step reasoning, allowing the model to generate valid function calls, handle tool errors, and compose tool sequences without explicit chain-of-thought prompting; MoE architecture allows expert specialization for different tool domains
vs alternatives: More reliable tool calling than general-purpose models due to specialized training, and more flexible than fixed tool sets because it supports arbitrary schema-based function definitions
Arcee AI: Coder Large scores higher at 24/100 vs Qwen: Qwen3 Coder 30B A3B Instruct at 24/100.
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Analyzes code for performance bottlenecks and generates optimized implementations by identifying inefficient patterns, suggesting algorithmic improvements, and applying performance-enhancing transformations. The model reasons about time and space complexity, considers trade-offs between performance and readability, and generates code with performance characteristics explained.
Unique: Analyzes and optimizes code by reasoning about algorithmic complexity and performance patterns; MoE experts can specialize in different optimization domains (memory, CPU, I/O) and apply domain-specific optimizations
vs alternatives: More comprehensive than simple profiling tools because it suggests algorithmic improvements, and more accurate than generic optimization patterns because it understands code context and constraints
Generates API designs, specifications, and contracts by analyzing code and requirements to produce well-structured, documented APIs. The model applies API design best practices, generates OpenAPI/GraphQL schemas, and creates client and server code that adheres to the specified contract.
Unique: Generates API designs and contracts by applying best practices and reasoning about API structure; can produce specifications in multiple formats (OpenAPI, GraphQL) with corresponding implementation code
vs alternatives: More comprehensive than simple code generation because it designs the entire API contract, and more maintainable than manual API design because it keeps specification and implementation synchronized
Designs database schemas and generates SQL queries by analyzing requirements and applying database design best practices. The model creates normalized schemas, generates efficient queries, and produces migration scripts while considering performance and maintainability implications.
Unique: Generates database schemas and queries by applying normalization principles and query optimization patterns; can produce code for multiple database systems with appropriate optimizations
vs alternatives: More comprehensive than simple query builders because it designs entire schemas, and more optimized than manual design because it applies best practices and considers performance implications
Generates infrastructure-as-code and deployment configurations by analyzing application requirements and applying cloud-native best practices. The model produces Terraform, Docker, Kubernetes, and CI/CD configurations that are production-ready and follow security and operational best practices.
Unique: Generates infrastructure and deployment code by applying cloud-native best practices and security patterns; can produce code for multiple platforms (Docker, Kubernetes, Terraform) with appropriate optimizations
vs alternatives: More comprehensive than simple configuration templates because it understands application requirements and generates appropriate infrastructure, and more maintainable than manual configuration because it applies consistent patterns
Generates code by following detailed natural language instructions with domain-specific reasoning about implementation trade-offs, performance characteristics, and architectural patterns. The model applies instruction-tuning to balance multiple objectives (correctness, efficiency, readability, maintainability) and reason about when to apply specific patterns based on context.
Unique: Instruction-tuned specifically for code generation with explicit reasoning about domain-specific trade-offs; MoE architecture allows different experts to specialize in different programming paradigms (imperative, functional, declarative) and apply appropriate reasoning for each
vs alternatives: More responsive to detailed specifications than base models, and more reasoning-aware than simple code completion tools because it explicitly considers multiple implementation approaches
Generates syntactically correct code across 40+ programming languages by maintaining language-specific syntax awareness and idiom knowledge. The model leverages training data spanning multiple language ecosystems to apply language-specific best practices, naming conventions, and error handling patterns appropriate to each language.
Unique: Trained on diverse language ecosystems with syntax-aware tokenization, allowing the model to maintain language-specific context and apply idioms without explicit language-specific prompting; MoE experts can specialize by language family (C-like, Python-like, functional, etc.)
vs alternatives: Broader language coverage than language-specific models, and more idiom-aware than generic code completion because it applies language-specific best practices learned from training data
+6 more capabilities