| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.00e-7 per prompt token | $6.00e-7 per prompt token |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Reka Flash 3 processes multi-turn conversational inputs and generates contextually appropriate responses using a 21B parameter instruction-tuned transformer architecture. The model maintains conversation history through context windowing and applies instruction-following fine-tuning to adhere to user directives, system prompts, and role-based constraints without explicit prompt engineering overhead.
Unique: 21B parameter size optimized for inference latency and cost efficiency while maintaining instruction-following capability through specialized fine-tuning, positioned between smaller 7B models and larger 70B+ alternatives
vs alternatives: Faster and cheaper than Llama 2 70B or Mixtral 8x7B while maintaining comparable instruction-following quality through Reka's proprietary fine-tuning approach
Reka Flash 3 generates syntactically correct code snippets and complete functions across multiple programming languages using transformer-based code understanding trained on diverse codebases. The model accepts natural language descriptions, partial code, or function signatures and outputs executable code with proper indentation, imports, and error handling patterns learned during pre-training.
Unique: Trained on diverse codebases with instruction-tuning specifically for code tasks, enabling natural language-to-code translation without requiring explicit code-specific prompting patterns
vs alternatives: More cost-effective than GitHub Copilot or Claude for routine code generation while maintaining reasonable quality for non-specialized domains
Reka Flash 3 supports structured function calling by accepting JSON schemas that define available functions, parameters, and return types, then generating properly formatted function calls with bound arguments extracted from user intent. The model parses user requests, maps them to appropriate functions, and outputs structured JSON containing function name, arguments, and metadata without requiring manual prompt engineering for each function.
Unique: Instruction-tuned specifically for function calling tasks, enabling reliable schema-based argument binding without requiring specialized prompt templates or few-shot examples
vs alternatives: Comparable function calling reliability to GPT-3.5 Turbo at significantly lower cost, though slightly less accurate than GPT-4 on complex multi-step function orchestration
Reka Flash 3 answers factual questions across diverse domains (science, history, current events, technical topics) by retrieving relevant knowledge from its training data and synthesizing coherent responses. The model applies instruction-tuning to distinguish between confident answers and uncertain knowledge, enabling it to express confidence levels and acknowledge knowledge cutoffs without hallucinating unsupported claims.
Unique: Instruction-tuned to express confidence and acknowledge knowledge limitations, reducing overconfident hallucinations compared to base models while maintaining broad knowledge coverage
vs alternatives: Faster and cheaper than RAG-augmented systems for general knowledge while maintaining reasonable accuracy for common questions, though less reliable than systems with real-time fact-checking
Reka Flash 3 generates creative content (stories, poetry, marketing copy, dialogue) with controllable style and tone through instruction-based prompting. The model learns style patterns from training data and applies them consistently across generated text, enabling users to specify tone (formal, casual, humorous) and genre without fine-tuning or specialized prompt engineering.
Unique: Instruction-tuned for style and tone control, enabling consistent creative output across different genres without requiring specialized prompting techniques or separate fine-tuned models
vs alternatives: More cost-effective than Claude or GPT-4 for routine creative generation while maintaining reasonable quality for non-specialized creative domains
Reka Flash 3 condenses long-form text (articles, documents, conversations) into summaries of variable length and detail through instruction-based control. The model extracts key information, preserves essential facts, and adjusts summary granularity (brief bullet points vs. detailed paragraphs) based on user specifications without requiring separate models or fine-tuning.
Unique: Instruction-tuned to respect user-specified summary length and detail constraints, enabling consistent summarization across different document types without requiring separate models
vs alternatives: Faster and cheaper than Claude or GPT-4 for routine summarization while maintaining reasonable quality for general-domain documents
Reka Flash 3 translates text between languages while preserving meaning, tone, and context through multilingual transformer training and instruction-tuning. The model handles idiomatic expressions, cultural references, and technical terminology by learning translation patterns across diverse language pairs, enabling natural-sounding translations without requiring language-specific fine-tuning.
Unique: Multilingual instruction-tuning enables context-aware translation that preserves tone and idiomatic meaning across diverse language pairs without requiring language-specific models
vs alternatives: More cost-effective than professional translation services or specialized translation APIs while maintaining reasonable quality for general-domain content
Reka Flash 3 strictly follows complex, multi-part instructions and adheres to specified constraints (output format, length limits, style requirements) through instruction-tuning that prioritizes constraint satisfaction. The model parses compound instructions, maintains constraint awareness throughout generation, and produces outputs that satisfy all specified requirements without requiring explicit constraint encoding in prompts.
Unique: Specialized instruction-tuning for constraint satisfaction enables reliable adherence to complex output format and style requirements without requiring explicit constraint encoding or post-processing
vs alternatives: More reliable constraint adherence than base models while maintaining lower latency and cost compared to larger models like GPT-4
+1 more capabilities
GLM-5 processes extended code contexts (supporting multi-file projects and large codebases) while maintaining semantic understanding of system architecture through attention mechanisms optimized for code structure. The model uses specialized tokenization for programming languages and maintains coherence across thousands of tokens of code context, enabling generation of complex features that respect existing patterns and dependencies.
Unique: Engineered specifically for complex systems design with attention mechanisms tuned for code structure and architectural patterns, rather than generic language modeling — enables understanding of system-wide dependencies and design constraints across extended contexts
vs alternatives: Outperforms general-purpose models on large-scale programming tasks because it's optimized for architectural coherence and long-horizon code generation rather than treating code as generic text
GLM-5 supports extended reasoning chains for agentic workflows through structured prompt patterns that enable step-by-step decomposition of complex tasks. The model can maintain state across multiple turns, reason about tool outputs, and make decisions about next actions — designed for long-horizon agent loops where the model must plan, execute, observe, and adapt across dozens of steps.
Unique: Explicitly engineered for long-horizon agent workflows with architectural patterns optimized for extended reasoning chains, rather than single-turn tool calling — maintains coherence and decision quality across dozens of reasoning steps
vs alternatives: Better suited for multi-step agentic tasks than general-purpose models because reasoning and tool-use patterns are baked into the training, not bolted on via prompt engineering
Z.ai: GLM 5 scores higher at 24/100 vs Reka Flash 3 at 23/100.
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GLM-5 analyzes code for performance bottlenecks and suggests optimization strategies through understanding of algorithmic complexity, memory management, and system-level performance patterns. The model can identify inefficient algorithms, suggest data structure improvements, and recommend caching or parallelization strategies — enabling targeted performance improvements with understanding of trade-offs.
Unique: Understands algorithmic complexity and system-level performance patterns, enabling identification of fundamental bottlenecks and suggestion of targeted optimizations rather than micro-optimizations
vs alternatives: Identifies more fundamental performance issues than profiling tools because it understands algorithmic complexity and can suggest architectural improvements, not just code-level optimizations
GLM-5 generates comprehensive API specifications, including endpoint definitions, request/response schemas, error handling, and usage examples through understanding of API design best practices and REST/GraphQL patterns. The model can produce OpenAPI/Swagger specifications, generate API documentation, and suggest design improvements — enabling rapid API specification and documentation.
Unique: Generates comprehensive API specifications that follow REST/GraphQL best practices and include error handling, authentication, and usage examples — not just endpoint definitions
vs alternatives: Produces more complete and best-practice-aligned API specifications than simple code-to-spec tools because it understands API design patterns and includes comprehensive documentation
GLM-5 generates high-quality technical documentation, design documents, and architectural specifications through training on expert-level technical writing patterns. The model understands domain-specific terminology, maintains consistency across long documents, and can generate structured documentation (API specs, RFC-style documents, architecture decision records) with appropriate technical depth and precision.
Unique: Trained on expert-level technical documentation patterns and domain-specific terminology, enabling generation of publication-ready documentation with appropriate technical depth rather than generic summaries
vs alternatives: Produces more technically precise and domain-aware documentation than general-purpose models because it understands architectural patterns, trade-offs, and expert writing conventions specific to software engineering
GLM-5 breaks down complex, ambiguous problems into structured task hierarchies and implementation plans through chain-of-thought reasoning patterns. The model can identify dependencies, suggest phased approaches, and generate detailed step-by-step plans for tackling large engineering challenges — useful for translating high-level requirements into actionable development roadmaps.
Unique: Optimized for expert-level problem decomposition through training on complex system design patterns and architectural reasoning, enabling generation of sophisticated multi-phase plans rather than simple task lists
vs alternatives: Produces more sophisticated and architecturally-aware plans than general-purpose models because it understands system design patterns, dependency relationships, and phased implementation strategies
GLM-5 analyzes code for quality issues, architectural violations, and design improvements through patterns learned from expert code review practices. The model can identify performance bottlenecks, suggest refactoring opportunities, flag architectural inconsistencies, and provide detailed feedback on code quality — going beyond simple linting to understand design intent and system-wide implications.
Unique: Trained on expert code review patterns and architectural reasoning, enabling detection of design issues and architectural violations rather than just syntax and style problems
vs alternatives: Provides more sophisticated architectural and design feedback than linting tools because it understands system-wide implications and expert design patterns, not just local code quality
GLM-5 translates code between programming languages while preserving semantic meaning and adapting to language-specific idioms. The model understands language-specific patterns, libraries, and best practices, enabling translation that produces idiomatic code rather than mechanical line-by-line conversions — useful for migrating systems across language ecosystems or supporting polyglot architectures.
Unique: Produces idiomatic, language-specific code rather than mechanical translations because it understands language-specific patterns, libraries, and best practices learned from diverse codebases
vs alternatives: Generates more idiomatic and maintainable translations than simple pattern-matching tools because it understands semantic equivalence and language-specific idioms
+4 more capabilities