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| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
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A 34-billion parameter decoder-only transformer model trained on 3 trillion tokens with native support for both English and Chinese language understanding and generation. The model uses standard transformer architecture with optimized attention mechanisms for efficient inference across both languages, leveraging balanced training data to maintain competitive performance in each language without degradation. Implements a unified vocabulary and embedding space that allows seamless code-switching and cross-lingual reasoning within single prompts.
Unique: Unified bilingual architecture trained on 3 trillion tokens with balanced English-Chinese data composition, avoiding the performance degradation typical of post-hoc language adaptation or separate model ensembles. Maintains competitive MMLU performance (76.3%) while achieving 'particularly strong' Chinese capability through integrated training rather than fine-tuning.
vs alternatives: Outperforms single-language 34B models on bilingual workloads by eliminating model-switching latency and inference overhead, while maintaining better English performance than Chinese-optimized models through unified training.
Achieves 76.3% accuracy on the Massive Multitask Language Understanding (MMLU) benchmark, indicating strong performance across 57 diverse knowledge domains including STEM, humanities, social sciences, and professional fields. The model demonstrates broad factual knowledge and reasoning capability across these domains through transformer-based pattern matching and learned world knowledge from the 3 trillion token training corpus. Performance is competitive within the 34B parameter class, positioning it as a capable general-purpose reasoning engine for knowledge-intensive tasks.
Unique: Achieves 76.3% MMLU through dense transformer training on 3 trillion tokens without documented RLHF or specialized reasoning fine-tuning, suggesting strong base model quality from pretraining alone. Competitive performance at 34B scale indicates efficient architecture and data composition relative to other models in the size class.
vs alternatives: Delivers MMLU performance comparable to larger open models (Llama 2 70B achieves ~71%) at half the parameter count, reducing inference latency and hardware requirements while maintaining knowledge breadth.
Adapts to new tasks through in-context learning by observing examples in the prompt without parameter updates, enabling the model to generalize to unseen tasks by inferring patterns from provided examples. The transformer attention mechanisms learn to recognize task structure from examples and apply learned patterns to generate appropriate outputs for new instances of the same task.
Unique: Bilingual in-context learning enables cross-lingual few-shot adaptation — users can provide examples in English and apply the learned pattern to Chinese inputs or vice versa
vs alternatives: Few-shot performance is likely comparable to Llama 2 34B but inferior to GPT-3.5 and Claude, which demonstrate superior in-context learning and few-shot generalization
Supports an extended context window variant with 200K token capacity (vs. 4K base variant), enabling processing of long-form documents, multi-turn conversations, and large code repositories within a single inference pass. The extended variant likely uses position interpolation, ALiBi, or similar techniques to extend the context window beyond the base training length without retraining. This allows models to maintain coherence and reference accuracy across significantly longer input sequences, critical for document analysis, code understanding, and multi-document reasoning tasks.
Unique: Provides 200K context window variant alongside 4K base, likely using position interpolation or similar techniques to extend context without full retraining. Enables single-pass processing of entire documents and long conversations without summarization or chunking overhead.
vs alternatives: Matches Claude 3's 200K context capability at 1/3 the parameter count (34B vs 100B+), reducing inference cost and latency while maintaining competitive long-context reasoning for document analysis and multi-turn conversations.
Demonstrates competitive performance on coding tasks (specific benchmarks undocumented) through transformer-based code understanding and generation. The model processes code as text tokens, leveraging the 3 trillion token training corpus which likely includes substantial code data from public repositories. Coding capability emerges from pretraining without documented specialized code fine-tuning, suggesting the base transformer architecture and training data composition are sufficient for code reasoning, completion, and generation tasks.
Unique: Achieves competitive coding performance through general-purpose transformer pretraining on 3 trillion tokens without documented code-specific fine-tuning or instruction tuning, suggesting strong code representation learning from raw pretraining data. Bilingual training enables code generation with Chinese comments and documentation.
vs alternatives: Provides competitive coding capability at 34B scale without the specialized training overhead of CodeLlama or Codex, reducing model size and inference cost while maintaining reasonable code quality for non-critical applications.
Demonstrates competitive performance on mathematical reasoning tasks (specific benchmarks undocumented) through transformer-based pattern matching and learned mathematical relationships. The model processes mathematical notation and reasoning as text tokens, leveraging training data that includes mathematical problems, proofs, and explanations. Mathematical capability emerges from pretraining without documented specialized math fine-tuning or chain-of-thought training, relying on the transformer's ability to learn mathematical patterns and reasoning from examples in the training corpus.
Unique: Achieves competitive mathematical reasoning through general-purpose transformer pretraining without documented chain-of-thought training or specialized math fine-tuning, suggesting strong mathematical pattern learning from raw pretraining data. Supports both English and Chinese mathematical notation and problem-solving.
vs alternatives: Delivers competitive math performance at 34B scale without specialized training overhead, reducing model size and inference cost while maintaining reasonable mathematical reasoning for educational and problem-solving applications.
Distributed under Apache 2.0 license, enabling unrestricted commercial use, modification, and redistribution of model weights and architecture. The permissive license allows developers to integrate Yi-34B into proprietary products, fine-tune for specialized domains, and deploy in any environment (cloud, on-premise, edge) without licensing fees or usage restrictions. This open-source distribution model contrasts with closed-source commercial APIs and enables full model ownership and customization for organizations with specific requirements.
Unique: Apache 2.0 licensed distribution enables unrestricted commercial use and modification without licensing fees, contrasting with restricted-use open models or closed-source commercial APIs. Allows full model ownership, on-premise deployment, and proprietary fine-tuning without external dependencies.
vs alternatives: Provides commercial-grade model with permissive licensing at no cost, compared to proprietary models (GPT-4, Claude) requiring API subscriptions or restricted-use models (Llama 2 with acceptable use policy) with usage limitations.
Serves as a foundation model for creating specialized variants through instruction tuning, domain-specific fine-tuning, and alignment training. The 34B base model provides a strong starting point for organizations to adapt to specific use cases (customer service, medical diagnosis, legal analysis, etc.) without training from scratch. This capability is evidenced by Yi-34B's role as the foundation for Yi-1.5 and subsequent models from 01.AI, demonstrating the model's suitability for downstream adaptation and specialization.
Unique: Designed as a foundation model for downstream specialization, as evidenced by its role in creating Yi-1.5 and subsequent 01.AI models. Strong base performance (76.3% MMLU, competitive coding/math) provides a robust starting point for fine-tuning without requiring full pretraining.
vs alternatives: Enables faster specialization than training from scratch while maintaining competitive base performance, reducing time-to-market for domain-specific models compared to full pretraining or using smaller foundation models.
+3 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs Yi-34B at 58/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities