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| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
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Processes multiple images (minimum 30 high-resolution images documented to fit within 128K context) interleaved with text prompts in a single conversation, using a dedicated 1B-parameter vision encoder that tokenizes visual input alongside text tokens. The architecture maintains Mistral Large 2's text foundation while extending the attention mechanism to handle mixed modality sequences, enabling coherent reasoning across image-text pairs without requiring separate API calls per image.
Unique: Supports true interleaved image-text conversations within a single 128K context window using a dedicated 1B vision encoder, rather than treating images as separate preprocessing steps or requiring image-to-text conversion before text processing
vs alternatives: Enables multi-image reasoning in a single conversation turn without context resets, whereas GPT-4V and Gemini require sequential image processing or separate API calls for each image batch
Analyzes scanned documents, PDFs, and forms by extracting text and visual layout information through the vision encoder, then answering natural language questions about document content, structure, and relationships. The model combines OCR-level text extraction with spatial reasoning about document layout, enabling it to locate and reason about specific information within complex multi-page or multi-section documents.
Unique: Combines vision encoding with spatial layout reasoning to understand document structure and relationships, rather than treating document analysis as pure text extraction; achieves this within a single 124B model without separate layout analysis modules
vs alternatives: Outperforms GPT-4o and Gemini-1.5 Pro on DocVQA benchmarks while being available for self-hosted deployment, eliminating API dependency for document processing pipelines
Processes documents and images containing text in multiple languages, with demonstrated support for Swiss German and French. Vision encoder extracts text regardless of language, and language decoder applies multilingual understanding to answer questions and extract information. Specific language support list not documented, but multilingual OCR capability confirmed through receipt processing examples.
Unique: Inherits multilingual capabilities from Mistral Large 2 and applies them to vision-extracted text, enabling end-to-end multilingual document understanding without separate language detection or translation steps
vs alternatives: Supports multilingual OCR and reasoning in single model, but specific language coverage and performance on non-European languages unknown vs specialized multilingual vision models
Interprets charts, graphs, tables, and other data visualizations by analyzing visual elements (axes, legends, data points, trends) and answering questions about data relationships, trends, and specific values. The vision encoder extracts visual structure while the language model reasons about the underlying data semantics, enabling both factual queries ('what is the value at X') and analytical questions ('what trend does this show').
Unique: Combines visual element detection with semantic data reasoning in a single model, enabling both factual extraction and analytical interpretation without separate chart parsing or data extraction modules
vs alternatives: Achieves superior ChartQA performance compared to GPT-4o and Gemini-1.5 Pro while supporting self-hosted deployment, avoiding cloud dependency for sensitive financial or business data
Extracts text from images across multiple languages (documented with Swiss German example) while simultaneously reasoning about extracted content, context, and relationships. Unlike traditional OCR engines that output raw text, this capability integrates text extraction with language understanding, enabling the model to correct OCR errors, understand context-dependent meaning, and answer questions about extracted text in a single pass.
Unique: Integrates OCR with language understanding in a single model, enabling context-aware error correction and semantic reasoning about extracted text rather than raw character output; supports multiple languages within the same model without language-specific preprocessing
vs alternatives: Provides context-aware OCR with simultaneous reasoning about extracted content, whereas traditional OCR engines (Tesseract, AWS Textract) output raw text requiring separate NLP processing for understanding
Solves mathematical problems presented in visual form (equations in images, mathematical diagrams, geometry problems, word problems with visual context) by combining visual understanding with mathematical reasoning. The model achieves 69.4% on MathVista benchmark, outperforming all tested alternatives, through integrated visual parsing and symbolic/numerical reasoning without requiring separate math engines.
Unique: Achieves 69.4% on MathVista benchmark (outperforming all tested models) through integrated visual parsing and mathematical reasoning in a single 124B model, without requiring separate symbolic math engines or specialized mathematical libraries
vs alternatives: Outperforms GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet on MathVista while being available for self-hosted deployment, eliminating API dependency for educational or research mathematical analysis
Integrates visual understanding with tool-use capabilities, enabling the model to analyze images and invoke external functions or APIs based on visual content understanding. The model can interpret visual data, extract relevant parameters from images, and call appropriate tools with image-derived context, supporting workflows where visual analysis triggers downstream automation.
Unique: Combines visual understanding with tool invocation in a single model, enabling image-based parameter extraction and tool selection without separate vision-to-function-call translation layers
vs alternatives: Enables direct image-to-tool-call workflows, whereas most vision models require intermediate text extraction or manual parameter mapping before tool invocation
Maintains full text-only language capabilities from Mistral Large 2 foundation model without documented performance degradation, supporting general language understanding, reasoning, and generation tasks. The 124B architecture extends Mistral Large 2 with vision capabilities while preserving text-only performance, enabling the model to handle pure text tasks alongside multimodal inputs in the same conversation.
Unique: Extends Mistral Large 2's text capabilities with vision without documented architectural modifications to text processing, maintaining compatibility with Mistral Large 2 text-only workflows
vs alternatives: Provides text-only performance equivalent to Mistral Large 2 while adding vision, whereas most multimodal models show text performance degradation compared to text-only baselines
+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 Pixtral Large at 60/100.
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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