dolphin-2.9.1-yi-1.5-34b vs vitest-llm-reporter
Side-by-side comparison to help you choose.
| Feature | dolphin-2.9.1-yi-1.5-34b | vitest-llm-reporter |
|---|---|---|
| Type | Model | Repository |
| UnfragileRank | 48/100 | 30/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Processes natural language instructions across code, math, reasoning, and agent tasks using a transformer-based decoder architecture fine-tuned on 7+ specialized datasets (Dolphin, OpenHermes, CodeFeedback, Agent-FLAN). Implements ChatML format for structured multi-turn conversations with explicit function-calling schema support via the Locutusque/function-calling-chatml dataset, enabling the model to generate tool invocations alongside natural language responses.
Unique: Combines 7 diverse training datasets (Dolphin reasoning, OpenHermes instruction-following, CodeFeedback code quality, Agent-FLAN agent reasoning, Orca math, Samantha conversational, function-calling-chatml) into a single 34B model with explicit function-calling support via ChatML format, rather than relying on post-hoc prompt engineering or separate specialized models
vs alternatives: Outperforms base Yi-1.5-34B by 15-25% on instruction-following benchmarks while maintaining function-calling capabilities that require separate fine-tuning in most open-source alternatives; smaller than Mixtral-8x34B but with better instruction adherence due to targeted dataset curation
Generates syntactically correct and semantically sound code across Python, JavaScript, SQL, and other languages through training on CodeFeedback-Filtered-Instruction and dolphin-coder datasets. Uses the Yi-1.5 base architecture's token embeddings to understand code structure, variable scoping, and language-specific idioms, enabling both code completion and code-from-description generation without language-specific tokenizers.
Unique: Trained on CodeFeedback-Filtered-Instruction (human-curated code quality feedback) and dolphin-coder datasets, enabling the model to generate not just syntactically valid code but code that follows best practices and idioms, rather than generic token-matching approaches used in simpler code completion models
vs alternatives: Generates more idiomatic and maintainable code than base language models due to CodeFeedback training, while remaining fully open-source and deployable locally unlike Copilot; smaller than Codex-scale models but with better instruction-following for code generation tasks
Solves mathematical word problems and performs step-by-step reasoning through training on Microsoft's Orca-Math-Word-Problems-200K dataset. The model learns to decompose complex math problems into intermediate reasoning steps, leveraging the Yi-1.5 base's strong numerical understanding and the Dolphin training's chain-of-thought patterns to produce verifiable mathematical solutions.
Unique: Integrates Microsoft's Orca-Math-Word-Problems-200K dataset (200K curated math problems with reasoning traces) with Dolphin's chain-of-thought training, enabling the model to produce explicit intermediate reasoning steps rather than just final answers, making solutions auditable and educational
vs alternatives: Provides transparent step-by-step reasoning for math problems unlike black-box proprietary models; smaller and faster to deploy than specialized math models like Minerva while maintaining competitive accuracy on word problems within training distribution
Decomposes complex user requests into executable sub-tasks and generates action plans through training on internlm/Agent-FLAN dataset. The model learns to identify task dependencies, prioritize steps, and generate structured action sequences that can be executed by downstream systems, enabling autonomous agent behavior without explicit prompt engineering for each task type.
Unique: Trained on internlm/Agent-FLAN dataset (agent-specific instruction following with task decomposition patterns), enabling the model to natively understand and generate agent-compatible task plans without requiring separate planning modules or prompt engineering for each agent framework
vs alternatives: Produces more structured and executable task plans than general-purpose instruction-following models due to Agent-FLAN specialization; fully open-source and deployable locally unlike proprietary agent planning APIs, with explicit task dependency awareness
Maintains coherent multi-turn conversations through ChatML format support and training on Samantha-data and OpenHermes-2.5 conversational datasets. The model tracks conversation history, maintains persona consistency, and generates contextually appropriate responses by leveraging the ChatML message structure (system/user/assistant roles) to explicitly separate conversation turns and context boundaries.
Unique: Combines Samantha-data (conversational personality and empathy training) with OpenHermes-2.5 (instruction-following dialogue) and explicit ChatML format support, enabling the model to maintain both conversational naturalness and instruction adherence across multi-turn interactions without separate dialogue state management
vs alternatives: Produces more natural and contextually coherent conversations than base instruction-following models due to Samantha training; fully open-source and deployable locally with explicit ChatML support, unlike proprietary conversational APIs that require cloud inference
Follows complex natural language instructions with explicit reasoning traces through training on Dolphin-2.9 dataset (curated instruction-following with reasoning explanations). The model generates not just task outputs but also intermediate reasoning steps, enabling users to understand and audit the model's decision-making process. Uses the Dolphin training methodology of pairing instructions with detailed reasoning chains to improve both accuracy and interpretability.
Unique: Trained on Dolphin-2.9 dataset (instruction-following with explicit reasoning traces), enabling the model to generate transparent intermediate reasoning steps alongside task outputs, rather than treating reasoning as an optional post-hoc explanation or relying on prompt engineering for chain-of-thought behavior
vs alternatives: Produces more transparent and auditable reasoning than base instruction-following models; reasoning quality is built into the model weights rather than dependent on prompt engineering, making it more reliable across diverse task types
Transforms Vitest's native test execution output into a machine-readable JSON or text format optimized for LLM parsing, eliminating verbose formatting and ANSI color codes that confuse language models. The reporter intercepts Vitest's test lifecycle hooks (onTestEnd, onFinish) and serializes results with consistent field ordering, normalized error messages, and hierarchical test suite structure to enable reliable downstream LLM analysis without preprocessing.
Unique: Purpose-built reporter that strips formatting noise and normalizes test output specifically for LLM token efficiency and parsing reliability, rather than human readability — uses compact field names, removes color codes, and orders fields predictably for consistent LLM tokenization
vs alternatives: Unlike default Vitest reporters (verbose, ANSI-formatted) or generic JSON reporters, this reporter optimizes output structure and verbosity specifically for LLM consumption, reducing context window usage and improving parse accuracy in AI agents
Organizes test results into a nested tree structure that mirrors the test file hierarchy and describe-block nesting, enabling LLMs to understand test organization and scope relationships. The reporter builds this hierarchy by tracking describe-block entry/exit events and associating individual test results with their parent suite context, preserving semantic relationships that flat test lists would lose.
Unique: Preserves and exposes Vitest's describe-block hierarchy in output structure rather than flattening results, allowing LLMs to reason about test scope, shared setup, and feature-level organization without post-processing
vs alternatives: Standard test reporters either flatten results (losing hierarchy) or format hierarchy for human reading (verbose); this reporter exposes hierarchy as queryable JSON structure optimized for LLM traversal and scope-aware analysis
dolphin-2.9.1-yi-1.5-34b scores higher at 48/100 vs vitest-llm-reporter at 30/100. dolphin-2.9.1-yi-1.5-34b leads on adoption, while vitest-llm-reporter is stronger on quality and ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Parses and normalizes test failure stack traces into a structured format that removes framework noise, extracts file paths and line numbers, and presents error messages in a form LLMs can reliably parse. The reporter processes raw error objects from Vitest, strips internal framework frames, identifies the first user-code frame, and formats the stack in a consistent structure with separated message, file, line, and code context fields.
Unique: Specifically targets Vitest's error format and strips framework-internal frames to expose user-code errors, rather than generic stack trace parsing that would preserve irrelevant framework context
vs alternatives: Unlike raw Vitest error output (verbose, framework-heavy) or generic JSON reporters (unstructured errors), this reporter extracts and normalizes error data into a format LLMs can reliably parse for automated diagnosis
Captures and aggregates test execution timing data (per-test duration, suite duration, total runtime) and formats it for LLM analysis of performance patterns. The reporter hooks into Vitest's timing events, calculates duration deltas, and includes timing data in the output structure, enabling LLMs to identify slow tests, performance regressions, or timing-related flakiness.
Unique: Integrates timing data directly into LLM-optimized output structure rather than as a separate metrics report, enabling LLMs to correlate test failures with performance characteristics in a single analysis pass
vs alternatives: Standard reporters show timing for human review; this reporter structures timing data for LLM consumption, enabling automated performance analysis and optimization suggestions
Provides configuration options to customize the reporter's output format (JSON, text, custom), verbosity level (minimal, standard, verbose), and field inclusion, allowing users to optimize output for specific LLM contexts or token budgets. The reporter uses a configuration object to control which fields are included, how deeply nested structures are serialized, and whether to include optional metadata like file paths or error context.
Unique: Exposes granular configuration for LLM-specific output optimization (token count, format, verbosity) rather than fixed output format, enabling users to tune reporter behavior for different LLM contexts
vs alternatives: Unlike fixed-format reporters, this reporter allows customization of output structure and verbosity, enabling optimization for specific LLM models or token budgets without forking the reporter
Categorizes test results into discrete status classes (passed, failed, skipped, todo) and enables filtering or highlighting of specific status categories in output. The reporter maps Vitest's test state to standardized status values and optionally filters output to include only relevant statuses, reducing noise for LLM analysis of specific failure types.
Unique: Provides status-based filtering at the reporter level rather than requiring post-processing, enabling LLMs to receive pre-filtered results focused on specific failure types
vs alternatives: Standard reporters show all test results; this reporter enables filtering by status to reduce noise and focus LLM analysis on relevant failures without post-processing
Extracts and normalizes file paths and source locations for each test, enabling LLMs to reference exact test file locations and line numbers. The reporter captures file paths from Vitest's test metadata, normalizes paths (absolute to relative), and includes line number information for each test, allowing LLMs to generate file-specific fix suggestions or navigate to test definitions.
Unique: Normalizes and exposes file paths and line numbers in a structured format optimized for LLM reference and code generation, rather than as human-readable file references
vs alternatives: Unlike reporters that include file paths as text, this reporter structures location data for LLM consumption, enabling precise code generation and automated remediation
Parses and extracts assertion messages from failed tests, normalizing them into a structured format that LLMs can reliably interpret. The reporter processes assertion error messages, separates expected vs actual values, and formats them consistently to enable LLMs to understand assertion failures without parsing verbose assertion library output.
Unique: Specifically parses Vitest assertion messages to extract expected/actual values and normalize them for LLM consumption, rather than passing raw assertion output
vs alternatives: Unlike raw error messages (verbose, library-specific) or generic error parsing (loses assertion semantics), this reporter extracts assertion-specific data for LLM-driven fix generation