| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $1.00e-7 per prompt token | $1.05e-6 per prompt token |
| Capabilities | 9 decomposed | 13 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-5.1 executes multi-step coding tasks over extended timeframes without requiring human intervention between steps, using an internal planning mechanism that decomposes complex objectives into sub-tasks and maintains execution state across sequential operations. Unlike minute-level interaction models that require prompting after each step, this capability enables the model to autonomously navigate decision trees, handle errors, and adapt strategy based on intermediate results without context resets.
Unique: Designed specifically for minute+ autonomous execution windows rather than single-turn interactions; maintains internal execution state and decision-making across extended task horizons without requiring external orchestration or re-prompting between steps
vs alternatives: Outperforms GPT-4 and Claude for long-horizon coding tasks because it's architected for continuous autonomous operation rather than stateless request-response cycles
GLM-5.1 generates and refactors code with awareness of the full codebase structure, dependencies, and patterns, using semantic understanding of how changes in one file propagate to others. The model analyzes import graphs, function signatures, and usage patterns across files to ensure generated code maintains consistency and doesn't introduce breaking changes, rather than treating each file in isolation.
Unique: Maintains semantic awareness of codebase structure and cross-file dependencies during generation, enabling it to make coordinated changes across multiple files rather than treating each file independently
vs alternatives: Produces more consistent multi-file refactorings than Copilot or Claude because it reasons about the entire codebase context simultaneously rather than file-by-file
Z.ai: GLM 5.1 scores higher at 24/100 vs Reka Flash 3 at 23/100.
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GLM-5.1 diagnoses errors and bugs by analyzing error messages, stack traces, and code context to identify root causes and suggest fixes. The model correlates error symptoms with likely causes, explains why errors occur, and provides specific debugging steps or code fixes.
Unique: Diagnoses errors by correlating symptoms with root causes using semantic understanding of code and error patterns, providing explanations and fixes rather than just pattern matching
vs alternatives: More effective at diagnosing subtle bugs than search-based solutions because it reasons about code semantics and error causality
GLM-5.1 identifies performance bottlenecks in code and suggests optimizations with specific implementation guidance, analyzing algorithms, data structures, and resource usage to recommend improvements. The model understands performance implications of different approaches and can suggest algorithmic or architectural changes to improve efficiency.
Unique: Suggests optimizations based on algorithmic and architectural analysis rather than just code-level tweaks, understanding performance implications of different approaches
vs alternatives: Provides more meaningful performance guidance than generic LLMs because it understands algorithm complexity and can suggest structural improvements
GLM-5.1 analyzes code for security vulnerabilities including injection attacks, authentication/authorization issues, cryptographic weaknesses, and data exposure risks, providing specific remediation guidance. The model understands common vulnerability patterns and security best practices to identify risks and suggest secure implementations.
Unique: Identifies security vulnerabilities through semantic analysis of code patterns and provides remediation guidance based on security best practices, not just pattern matching against known CVEs
vs alternatives: More effective at finding context-specific security issues than SAST tools because it understands code intent and can suggest secure implementations
GLM-5.1 performs step-by-step reasoning about code behavior by internally simulating or tracing execution paths, allowing it to predict runtime behavior, identify bugs, and explain code logic without requiring actual execution. This capability uses chain-of-thought-like reasoning applied specifically to code semantics, tracking variable state, control flow, and function call sequences to reason about correctness.
Unique: Applies extended reasoning specifically to code semantics and execution paths, enabling it to predict runtime behavior and identify subtle bugs through symbolic execution simulation rather than pattern matching
vs alternatives: More effective at finding subtle logic bugs than GPT-4 because it explicitly traces execution state rather than relying on pattern recognition
GLM-5.1 maintains rich context across multiple conversation turns when working on code, remembering previous edits, design decisions, and constraints without requiring users to re-specify them. The model builds an internal model of the codebase state and user intent that persists across turns, enabling iterative refinement where each turn builds on previous work rather than starting fresh.
Unique: Maintains stateful context across turns specifically optimized for code collaboration, remembering design decisions and codebase state without explicit memory structures
vs alternatives: Provides better iterative code refinement than stateless models because it retains context about previous edits and design intent across turns
GLM-5.1 translates natural language specifications into code that preserves semantic intent, handling ambiguous or underspecified requirements by inferring reasonable implementations based on context and common patterns. The model uses semantic understanding of both natural language and code to bridge the gap between high-level intent and low-level implementation details.
Unique: Translates natural language to code with explicit semantic fidelity checking, inferring reasonable implementations for underspecified requirements rather than producing literal or incomplete code
vs alternatives: Handles ambiguous requirements better than Copilot because it uses semantic reasoning to infer intent rather than pattern matching against training data
+5 more capabilities