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| Pricing | Free | Free |
| Capabilities | 12 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Provides curated few-shot chain-of-thought (CoT) exemplars for 23 hard reasoning tasks, enabling models to learn structured step-by-step problem decomposition through in-context learning. Each task includes 3-5 hand-crafted examples showing intermediate reasoning steps, allowing models to adopt explicit reasoning patterns without fine-tuning. The dataset leverages prompt engineering patterns where models observe reasoning trajectories before solving novel instances.
Unique: Curated subset specifically filtered to tasks where models initially underperformed humans (below 50th percentile), creating a hard-mode benchmark rather than a balanced difficulty distribution. This selection strategy focuses evaluation on frontier model improvements rather than general capability assessment.
vs alternatives: Harder and more reasoning-focused than general benchmarks like MMLU or HellaSwag; includes explicit CoT examples unlike raw BIG-Bench, making it more suitable for prompt engineering evaluation than raw task suites.
Organizes 23 tasks across distinct reasoning domains (algorithmic, arithmetic, logical, causal, spatial) with consistent evaluation structure, enabling fine-grained analysis of model strengths and weaknesses by reasoning type. Each task is independently evaluable with its own test set and metrics, allowing researchers to identify which reasoning modalities their models excel or fail at. The stratification enables targeted model development and capability analysis.
Unique: Explicitly stratifies tasks by reasoning modality (algorithmic, arithmetic, logical, causal, spatial) rather than treating all hard tasks as monolithic, enabling domain-specific capability assessment. This structure allows researchers to correlate model architecture choices with specific reasoning strengths.
vs alternatives: More analytically useful than generic hard task collections because stratification enables root-cause analysis of reasoning failures; more focused than full BIG-Bench which lacks explicit domain organization.
Designed specifically to evaluate frontier language models (GPT-4, Claude, Llama 2+, etc.) on hard reasoning tasks where initial model performance was below human level, enabling measurement of model improvement over time and comparison of frontier model capabilities. The dataset enables researchers to track whether new model releases improve on hard reasoning and to identify reasoning capabilities that remain unsolved. Results are directly comparable across models because of standardized evaluation infrastructure.
Unique: Explicitly designed for frontier model evaluation by selecting tasks where initial models underperformed humans, creating a benchmark that remains challenging as models improve. This selection strategy ensures the benchmark is useful for measuring frontier model progress rather than becoming trivial.
vs alternatives: More suitable for frontier model evaluation than general benchmarks because it focuses on hard reasoning tasks; more challenging than benchmarks where models already exceed human performance, which may not drive model improvement.
Enables reproducible evaluation across different models and research groups by providing standardized task definitions, test sets, evaluation metrics, and result aggregation. The dataset structure ensures that different teams can run identical evaluations and compare results directly, reducing evaluation variance and enabling fair model comparison. Standardized evaluation infrastructure supports publishing reproducible results and enables meta-analysis across multiple model evaluations.
Unique: Provides standardized evaluation infrastructure that enables reproducible results across different models and research groups, reducing evaluation variance and enabling fair model comparison. The dataset structure enforces consistent task definitions and metrics.
vs alternatives: More reproducible than ad-hoc evaluation because it enforces standardized task definitions and metrics; more comparable than benchmarks without standardized infrastructure because it enables direct result comparison across models.
Includes human rater performance data for all 23 tasks, establishing ground-truth difficulty calibration and enabling measurement of model-vs-human performance gaps. Tasks were specifically selected where initial model performance fell below human median (50th percentile), creating a calibrated hard benchmark. Human baselines enable researchers to quantify progress toward human-level reasoning and identify tasks where models have surpassed human performance.
Unique: Explicitly selected tasks where models underperformed humans at time of curation, creating a self-calibrated hard benchmark where human performance is the reference point rather than an afterthought. This selection strategy ensures the benchmark remains challenging as models improve.
vs alternatives: More rigorous than benchmarks without human baselines because it enables quantitative model-vs-human comparison; more meaningful than benchmarks where humans outperform models by large margins, which may indicate task misalignment rather than genuine reasoning difficulty.
Provides consistent evaluation infrastructure across 23 heterogeneous reasoning tasks with unified input/output schemas, metrics computation, and result aggregation. Each task includes standardized test sets, answer formats, and evaluation functions, enabling researchers to run comprehensive benchmarks with a single evaluation script. The harness abstracts task-specific complexity and enables reproducible, comparable results across models and research groups.
Unique: Provides unified evaluation infrastructure across heterogeneous task types (arithmetic, logic, spatial, causal) with consistent metrics and result aggregation, rather than requiring task-specific evaluation code. This standardization enables reproducible cross-model comparison and reduces evaluation implementation burden.
vs alternatives: More reproducible than ad-hoc evaluation because it enforces consistent metrics and input/output handling; more comprehensive than single-task benchmarks because it enables multi-domain capability assessment in one evaluation run.
Includes algorithmic reasoning tasks (e.g., sorting, graph traversal, dynamic programming) that test whether models can learn and apply computational algorithms through few-shot examples. Tasks present problem descriptions and expect models to reason through algorithmic steps, testing whether models can generalize algorithmic patterns beyond memorized examples. This capability isolates algorithmic reasoning from knowledge retrieval or common-sense reasoning.
Unique: Isolates algorithmic reasoning as a distinct capability by presenting algorithm problems in natural language with few-shot examples, testing whether models can learn algorithmic patterns without explicit training. This approach measures algorithmic reasoning generalization rather than memorization.
vs alternatives: More focused on algorithmic reasoning than general reasoning benchmarks; more accessible than formal algorithm verification tasks because it uses natural language rather than pseudocode or formal logic.
Includes multi-step arithmetic and mathematical reasoning tasks (e.g., word problems, numerical reasoning, mathematical deduction) that test whether models can perform accurate calculations and apply mathematical reasoning through few-shot examples. Tasks range from basic arithmetic to more complex mathematical inference, isolating numerical reasoning from language understanding. Evaluation measures both intermediate calculation accuracy and final answer correctness.
Unique: Focuses specifically on multi-step arithmetic and mathematical reasoning through few-shot examples, isolating numerical reasoning capability from general language understanding. Tasks test both calculation accuracy and mathematical inference patterns.
vs alternatives: More focused on mathematical reasoning than general reasoning benchmarks; more accessible than formal mathematics verification because it uses natural language problem statements rather than symbolic notation.
+4 more capabilities
GPT-4o processes text, images, and audio through a single transformer architecture with shared token representations, eliminating separate modality encoders. Images are tokenized into visual patches and embedded into the same vector space as text tokens, enabling seamless cross-modal reasoning without explicit fusion layers. Audio is converted to mel-spectrogram tokens and processed identically to text, allowing the model to reason about speech content, speaker characteristics, and emotional tone in a single forward pass.
Unique: Single unified transformer processes all modalities through shared token space rather than separate encoders + fusion layers; eliminates modality-specific bottlenecks and enables emergent cross-modal reasoning patterns not possible with bolted-on vision/audio modules
vs alternatives: Faster and more coherent multimodal reasoning than Claude 3.5 Sonnet or Gemini 2.0 because unified architecture avoids cross-encoder latency and modality mismatch artifacts
GPT-4o implements a 128,000-token context window using optimized attention patterns (likely sparse or grouped-query attention variants) that reduce memory complexity from O(n²) to near-linear scaling. This enables processing of entire codebases, long documents, or multi-turn conversations without truncation. The model maintains coherence across the full context through learned positional embeddings that generalize beyond training sequence lengths.
Unique: Achieves 128K context with sub-linear attention complexity through architectural optimizations (likely grouped-query attention or sparse patterns) rather than naive quadratic attention, enabling practical long-context inference without prohibitive memory costs
vs alternatives: Longer context window than GPT-4 Turbo (128K vs 128K, but with faster inference) and more efficient than Anthropic Claude 3.5 Sonnet (200K context but slower) for most production latency requirements
GPT-4o scores higher at 84/100 vs BIG-Bench Hard (BBH) at 61/100.
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GPT-4o includes built-in safety mechanisms that filter harmful content, refuse unsafe requests, and provide explanations for refusals. The model is trained to decline requests for illegal activities, violence, abuse, and other harmful content. Safety filtering operates at inference time without requiring external moderation APIs. Applications can configure safety levels or override defaults for specific use cases.
Unique: Safety filtering is integrated into the model's training and inference, not a post-hoc filter; the model learns to refuse harmful requests during pretraining, resulting in more natural refusals than external moderation systems
vs alternatives: More integrated safety than external moderation APIs (which add latency and may miss context-dependent harms) because safety reasoning is part of the model's core capabilities
GPT-4o supports batch processing through OpenAI's Batch API, where multiple requests are submitted together and processed asynchronously at lower cost (50% discount). Batches are processed in the background and results are retrieved via polling or webhooks. Ideal for non-time-sensitive workloads like data processing, content generation, and analysis at scale.
Unique: Batch API is a first-class API tier with 50% cost discount, not a workaround; enables cost-effective processing of large-scale workloads by trading latency for savings
vs alternatives: More cost-effective than real-time API for bulk processing because 50% discount applies to all batch requests; better than self-hosting because no infrastructure management required
GPT-4o can analyze screenshots of code, whiteboards, and diagrams to understand intent and generate corresponding code. The model extracts code from images, understands handwritten pseudocode, and generates implementation from visual designs. Enables workflows where developers can sketch ideas visually and have them converted to working code.
Unique: Vision-based code understanding is native to the unified architecture, enabling the model to reason about visual design intent and generate code directly from images without separate vision-to-text conversion
vs alternatives: More integrated than separate vision + code generation pipelines because the model understands design intent and can generate semantically appropriate code, not just transcribe visible text
GPT-4o maintains conversation state across multiple turns, preserving context and building coherent narratives. The model tracks conversation history, remembers user preferences and constraints mentioned earlier, and generates responses that are consistent with prior exchanges. Supports up to 128K tokens of conversation history without losing coherence.
Unique: Context preservation is handled through explicit message history in the API, not implicit server-side state; gives applications full control over context management and enables stateless, scalable deployments
vs alternatives: More flexible than systems with implicit state management because applications can implement custom context pruning, summarization, or filtering strategies
GPT-4o includes built-in function calling via OpenAI's function schema format, where developers define tool signatures as JSON schemas and the model outputs structured function calls with validated arguments. The model learns to map natural language requests to appropriate functions and generate correctly-typed arguments without additional prompting. Supports parallel function calls (multiple tools invoked in single response) and automatic retry logic for invalid schemas.
Unique: Native function calling is deeply integrated into the model's training and inference, not a post-hoc wrapper; the model learns to reason about tool availability and constraints during pretraining, resulting in more natural tool selection than prompt-based approaches
vs alternatives: More reliable function calling than Claude 3.5 Sonnet (which uses tool_use blocks) because GPT-4o's schema binding is tighter and supports parallel calls natively without workarounds
GPT-4o's JSON mode constrains the output to valid JSON matching a provided schema, using constrained decoding (token-level filtering during generation) to ensure every output is parseable and schema-compliant. The model generates JSON directly without intermediate text, eliminating parsing errors and hallucinated fields. Supports nested objects, arrays, enums, and type constraints (string, number, boolean, null).
Unique: Uses token-level constrained decoding during inference to guarantee schema compliance, not post-hoc validation; the model's probability distribution is filtered at each step to only allow tokens that keep the output valid JSON, eliminating hallucinated fields entirely
vs alternatives: More reliable than Claude's tool_use for structured output because constrained decoding guarantees validity at generation time rather than relying on the model to self-correct
+6 more capabilities