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| Pricing | Free | Free |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates language models on 70,000 multiple-choice questions where incorrect options were generated by language models and adversarially selected to fool machines while remaining obviously wrong to humans. The filtering process uses a two-stage approach: LLM-generated distractors are ranked by their ability to confuse models (measured via model accuracy on that specific question), then human annotators validate that the hard-for-models options remain easy for humans. This creates a dataset where model performance gaps vs human performance (95.6% human accuracy) directly measure commonsense reasoning gaps rather than dataset artifacts.
Unique: Uses adversarial filtering where distractors are selected based on measured model confusion rather than human-written plausibility, creating a dataset that specifically targets machine weaknesses while maintaining human interpretability. This two-stage LLM-generation + human-validation approach is more scalable than purely human-written distractors while maintaining higher quality than random negatives.
vs alternatives: Harder than SWAG (predecessor) because distractors are adversarially selected for model confusion, and more human-aligned than synthetic reasoning datasets because human accuracy (95.6%) validates that hard-for-models questions remain easy for humans.
Tests models' ability to predict the next action or outcome in video-like scenarios involving physical activities (cooking, sports, repairs, etc.). Each question presents a sequence of events and asks which of four options most plausibly continues the sequence. The dataset uses real-world video captions and activities, grounding commonsense in concrete physical interactions rather than abstract reasoning. Models must understand object physics, tool usage, body mechanics, and temporal causality to select correct continuations.
Unique: Grounds commonsense reasoning in real video captions and activities rather than synthetic scenarios, ensuring that correct answers reflect actual physical outcomes humans observe. The adversarial filtering specifically targets models that fail at physical reasoning while humans succeed, creating a diagnostic tool for embodied understanding gaps.
vs alternatives: More grounded in real-world physics than abstract reasoning benchmarks like MMLU, and more challenging than simple video QA because distractors are adversarially selected to confuse models specifically about physical causality.
Assesses models' understanding of social dynamics, conversational context, and temporal sequences in everyday scenarios. Questions test whether models can reason about social norms (what's appropriate to say/do), emotional reactions, and cause-effect relationships across time. The dataset includes scenarios involving interpersonal interactions, social etiquette, and temporal ordering of events. Adversarial distractors specifically target models that misunderstand social context or temporal logic while remaining obviously wrong to humans.
Unique: Combines social understanding with temporal reasoning in a single benchmark, testing whether models understand not just what happens next but why it happens and how humans would react. Adversarial filtering specifically targets models that fail at social reasoning while humans succeed.
vs alternatives: More comprehensive than social bias benchmarks because it tests positive social understanding (what's appropriate) rather than just detecting bias, and more grounded than abstract reasoning datasets.
Provides a calibrated benchmark where human accuracy (95.6%) is known and adversarial filtering ensures that questions hard for machines remain easy for humans. This enables precise measurement of the performance gap between models and humans on commonsense reasoning. Researchers can use this gap to quantify progress toward human-level understanding and identify which types of commonsense reasoning (physical, social, temporal) show the largest model-human gaps.
Unique: Provides a human-calibrated baseline (95.6% accuracy) with adversarial filtering that ensures the gap is meaningful — questions hard for machines are easy for humans, so the gap reflects genuine commonsense reasoning deficits rather than dataset ambiguity. This enables precise measurement of progress toward human-level understanding.
vs alternatives: More interpretable than benchmarks without human baselines because the gap directly measures commonsense reasoning deficit, and more reliable than benchmarks where hard questions are hard for both humans and machines.
Provides a fixed, versioned dataset of 70,000 examples with consistent train/validation/test splits, enabling reproducible evaluation across models and time. The dataset is hosted on Hugging Face with version control, allowing researchers to cite specific versions and ensuring that benchmark results are comparable across papers. The fixed nature of the dataset (no dynamic generation or augmentation) means that model improvements reflect genuine capability gains rather than dataset variance.
Unique: Provides a fixed, versioned dataset on Hugging Face with explicit train/validation/test splits, enabling reproducible evaluation and fair comparison across models. The fixed nature ensures that improvements reflect genuine capability gains rather than dataset variance or adversarial augmentation at test time.
vs alternatives: More reproducible than dynamically-generated benchmarks because the dataset is fixed and versioned, and more comparable than benchmarks with multiple variants because all researchers use the same evaluation set.
Mistral Large processes up to 128,000 tokens in a single context window, enabling analysis of entire codebases, long documents, or multi-turn conversations without context truncation. The architecture uses optimized attention mechanisms (likely grouped-query attention based on Mistral's prior work) to maintain computational efficiency while supporting this extended context, allowing developers to maintain coherent reasoning across large information volumes without manual chunking or sliding-window strategies.
Unique: 128K context window with grouped-query attention optimization enables full-codebase and full-document analysis without external retrieval, differentiating from GPT-4's 128K (which uses standard attention) through computational efficiency gains that reduce latency penalty
vs alternatives: Larger than Claude 3.5 Sonnet's 200K context but more cost-efficient per token than GPT-4o's extended context for most enterprise use cases due to optimized attention architecture
Mistral Large implements function calling through a schema-based interface where developers define tool signatures in JSON Schema format, and the model outputs structured function calls that can be directly dispatched to registered handlers. The implementation uses constrained decoding to ensure valid JSON output matching the provided schema, preventing malformed function calls and enabling reliable tool orchestration without post-processing validation.
Unique: Uses constrained decoding with JSON Schema validation to guarantee valid function calls without post-processing, whereas competitors like GPT-4 rely on post-hoc validation of model output, reducing error rates and enabling direct dispatch
vs alternatives: More reliable than Claude's tool_use format for complex multi-step workflows because constrained decoding prevents malformed calls, and simpler to integrate than OpenAI's function calling which requires additional validation layers
Mistral Large scores higher at 77/100 vs HellaSwag at 59/100. HellaSwag leads on ecosystem, while Mistral Large is stronger on quality.
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Mistral Large can be deployed on-premises or in private cloud environments, enabling organizations to maintain data sovereignty and avoid sending sensitive information to external APIs. Self-hosted deployments support custom fine-tuning on proprietary datasets, enabling domain-specific optimization without sharing training data with Mistral. Deployment uses standard container formats (Docker) and supports multiple hardware backends (NVIDIA GPUs, AMD ROCm, Intel Gaudi).
Unique: Supports full self-hosted deployment with custom fine-tuning on proprietary data, enabling organizations to maintain complete control over model behavior and data, whereas most competitors restrict fine-tuning to managed services
vs alternatives: More flexible than OpenAI's fine-tuning (which is API-only) and more cost-effective than Claude for high-volume on-premises deployments due to lower licensing costs
Mistral Large achieves performance competitive with GPT-4o and Claude 3.5 Sonnet on major reasoning benchmarks including MMLU (84.0%), HumanEval, and MATH, indicating comparable capability for complex reasoning, code generation, and mathematical problem-solving. This performance is achieved with a 123B parameter model, making it more efficient than larger competitors in terms of inference cost and latency.
Unique: Achieves GPT-4o and Claude 3.5 Sonnet-level performance on major benchmarks with a 123B parameter model, enabling competitive reasoning capability at lower inference cost due to smaller model size and optimized architecture
vs alternatives: More cost-efficient than GPT-4o and Claude 3.5 Sonnet for equivalent reasoning performance, making it ideal for cost-sensitive applications where benchmark-level performance is sufficient
Mistral Large exposes temperature and top-p (nucleus sampling) parameters to control the randomness and diversity of generated outputs. Temperature scales the logit distribution (higher = more random), while top-p limits sampling to the smallest set of tokens with cumulative probability ≥ p. These parameters enable tuning the model's behavior from deterministic (temperature=0) to highly creative (temperature=2.0), allowing builders to balance consistency and diversity for different use cases.
Unique: Exposes temperature and top-p parameters with standard semantics, enabling fine-grained control over output diversity and consistency without model retraining
vs alternatives: Standard parameter set comparable to GPT-4o and Claude, with no unique advantages but consistent behavior across models
Mistral Large can be constrained to output only valid JSON matching a provided schema, using constrained decoding to enforce structural validity at generation time rather than post-processing. This ensures every generated token respects the schema constraints, preventing partial or malformed JSON and enabling reliable downstream parsing without error handling for invalid output.
Unique: Enforces schema compliance at token generation time using constrained decoding, guaranteeing valid JSON output without post-processing, whereas most competitors (including GPT-4) generate JSON then validate, allowing invalid output to be produced
vs alternatives: More efficient than Claude's JSON mode because validation happens during generation rather than after, eliminating retry loops for invalid output and reducing latency for structured extraction tasks
Mistral Large is trained on multilingual data and maintains reasoning capability across 10+ languages including English, French, Spanish, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, and Arabic. The model uses a shared embedding space and unified transformer architecture rather than language-specific branches, enabling cross-lingual transfer and reasoning without language-specific fine-tuning.
Unique: Unified transformer architecture with shared embeddings across 10+ languages enables consistent reasoning quality and cross-lingual transfer, whereas competitors often use separate language-specific models or language adapters that add latency
vs alternatives: More efficient than running separate language models for each language, and maintains better cross-lingual reasoning than GPT-4o which uses separate tokenizers per language
Mistral Large uses a distinct system prompt format optimized for instruction following, where system instructions are formatted as structured directives that the model interprets with higher fidelity than standard text prompts. The architecture includes special tokens and attention patterns that prioritize system instructions over user input, enabling more reliable behavior control and reducing prompt injection vulnerabilities.
Unique: Dedicated system prompt format with special tokens and attention masking prioritizes instructions over user input, reducing prompt injection risk and improving instruction adherence vs standard chat templates used by competitors
vs alternatives: More robust instruction following than GPT-4o's system message format because special tokenization prevents user input from overriding system directives, and simpler than Claude's system prompt which requires careful phrasing to avoid conflicts
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