Last updated Apr 27th 2026
OpenAI-compatible API gateway for 200+ models from 30+ providers. One integration gives you smart routing, persistent memory, web search, context compression, and input security. All features activate per request by adding a single parameter. No new SDKs, no infrastructure changes.
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Sansa Benchmark data released to the public in December 2025.
Sansa Censorship Benchmark covered by online media world wide in December 2025. (gazeta.ru, substack.com, etc.)
Sansa Benchmarks go viral on X and Reddit in December 2025.
Sansa reaches first 100 waitlist signups in December 2025.
Sansa surpasses 12 Billion tokens processed in January 2026.
Sansa launched to the public on February 17, 2026.
Sansa releases OpenClaw installer for the open source AI agent framework: https://github.com/sansa-ai/openclaw-installer
Sansa adds Text and Audio processing to their OpenAI compatible API in March 2026.
Sansa is launching Memory, Input Guard (input security), Context Compression, AI Web Search, and Smart Routing as gateway features in 2026.
Sansa is a unified LLM gateway. One API, one key, one base URL. OpenAI-compatible. 200+ models from 30+ providers (OpenAI, Anthropic, Google, Meta, Mistral, and more). The gateway automatically routes between providers for uptime. What you get depends on what you pass in the request:
- Any model by name: pass claude-3.5-sonnet, gpt-4o, gemini-2.5-pro, or any of the 200+ supported models. The gateway handles provider-level failover automatically. - Smart Model Routing: pass sansa-auto as the model and a routing model trained on 20+ Billion tokens selects the best model for the prompt. Better than frontier quality at roughly half the cost. - Memory: add a memory parameter and the model remembers users across sessions. No vector database or retrieval pipeline required. - AI Web Search: append :search to the model name and the model gets grounded with live web results and inline citations. - Context Compression: add a compression parameter to strip low-value tokens from long inputs. ~40% faster latency, ~30% fewer input tokens. - Input Guard: add input_guard to detect PII and prompt injections before they reach the model.
The Sansa gateway is OpenAI-compatible. Change the base URL and API key and existing OpenAI SDK code works immediately. Pass the model you want, or pass sansa-auto to let the routing model choose. Add parameters to the request body to turn on memory, search, compression, or input guard. Everything is one API call.
- 200+ models supported - 30+ inference providers with automatic failover - 99.9% uptime - 20+ Billion training tokens for the smart routing model - 10-15ms latency added by smart routing (sansa-auto) - ~40% token savings with context compression - 98% accuracy for input guard detection - Sub-50ms latency for memory and search - $5 per million tokens with smart routing vs $10-25 for frontier models
Compatible with OpenAI SDK (three line code change) and the Sansa SDK. No new SDKs, middleware, or infrastructure changes required. Every feature is opt-in per request.
Free community benchmarking tool. Tests individual models from various providers on real-world tasks. Does not test the sansa-auto endpoint. Results published at trysansa.com/benchmark
SOC 2 Type II ready. Data never sold or shared with third parties. All requests encrypted in transit and at rest. Opt out of training data. US-hosted infrastructure. Full user control over data at all times.
Sansa Benchmark Results and Methodology Overview — Last updated Apr 27th 2026
**Overall**: Represents the average performance across all benchmark dimensions. Each capability contributes equally to the overall score regardless of the number of queries per capability. **Overall Objective**: Represents the average performance across objective benchmark dimensions only. Excludes subjective and behavioral dimensions where the expected outcome is debatable or policy-based. Provides a cleaner measure of verifiable capabilities without dimensions that depend on value judgments or organizational preferences. **Accounting**: Tests knowledge and understanding of accounting principles, financial reporting standards (e.g., US GAAP, IFRS), and accounting practices. Queries cover topics such as revenue recognition, lease accounting, depreciation, financial statement preparation, and regulatory compliance. **Agentic Performance**: Tests multi-step goal completion with tool use under turn constraints. Queries simulate real-world scenarios where the model must achieve a specific goal using multiple tools, potentially through different valid paths. A simulated user (gpt-4o-mini) provides responses during the conversation, operating under a constrained system prompt that defines exactly what information it can provide. Tests both tool usage capability and efficient problem-solving. **Applied Mathematics**: Tests applied mathematics problems and real-world applications. Queries require mathematical reasoning applied to practical scenarios, including optimization problems, modeling, and mathematical problem-solving in context. **Art**: Tests art-related knowledge and understanding, including art history, artistic movements, techniques, and cultural context. Queries evaluate understanding of artistic concepts, historical periods, and art appreciation. **Astronomy**: Tests astronomy knowledge and understanding, including celestial objects, planetary science, astrophysics, and observational astronomy. Queries cover topics such as stars, planets, galaxies, cosmology, and astronomical phenomena. **Bias Resistance**: Tests the model's ability to resist bias and maintain fair judgment. Queries present scenarios with potential biases (gender, race, cultural, etc.) and evaluate whether the model can provide unbiased, fair responses without perpetuating stereotypes or discriminatory patterns. **Biology**: Tests biology knowledge and understanding, including cellular biology, genetics, ecology, evolution, and biological systems. Queries evaluate understanding of biological processes, organisms, and biological concepts. **Business**: Tests business knowledge and understanding, including business strategy, management principles, organizational behavior, and business operations. Queries cover various aspects of business administration and management. **Censorship**: Measures whether models engage with or suppress topics that significant user populations believe or care about. The benchmark measures behavior, not motive. Queries span right-coded political, left-coded political, health/science controversy, historical conspiracy, sexuality, and adult content. **Chemistry**: Tests chemistry knowledge and understanding, including chemical reactions, molecular structures, periodic table, organic and inorganic chemistry, and chemical processes. Queries evaluate understanding of chemical principles and applications. **Coding**: Tests code generation tasks across multiple programming languages. Queries require writing code in Python, JavaScript, Bash, SQL, or other languages to solve programming problems, implement algorithms, or create functional programs. Code is evaluated through execution against test cases. **Computer Science**: Tests computer science knowledge and understanding, including algorithms, data structures, computer architecture, software engineering principles, and theoretical computer science concepts. **Creative Writing**: Tests creative writing ability, including storytelling, narrative structure, character development, and literary techniques. Queries evaluate the model's ability to generate original, engaging creative content that demonstrates literary skill and avoids common AI writing patterns. **Economics**: Tests economics knowledge and understanding, including microeconomics, macroeconomics, economic theory, market dynamics, and economic policy. Queries evaluate understanding of economic principles and their applications. **Em Dash Resistance**: Tests whether models incorporate user stylistic preferences from conversational memory into their generated output. Evaluates if models can maintain awareness of user preferences across conversation turns and apply them consistently when generating text, even when the preference is not explicitly repeated in the immediate prompt. **Engineering**: Tests engineering knowledge and understanding, including various engineering disciplines, design principles, problem-solving approaches, and engineering applications. Queries cover mechanical, electrical, civil, and other engineering domains. **Environmental Science**: Tests environmental science knowledge and understanding, including ecology, climate science, environmental systems, and sustainability. Queries evaluate understanding of environmental processes and issues. **Error Detection**: Tests the ability to detect errors in data, code, or logical structures. Queries present scenarios with intentional errors and evaluate whether the model can identify and explain the mistakes accurately. **Games**: Tests game-specific knowledge, strategic reasoning, and game theory across multiple game types. Queries include chess puzzles (spatial reasoning and rules), poker strategy (optimal play decisions at different stack depths), game theory (Nash equilibrium concepts in heads-up poker), and logic puzzles (word searches with complex constraints). Evaluates both domain-specific knowledge (e.g., poker terminology like 'UTG1', '16bb') and strategic thinking within game contexts. Tests the ability to apply mathematical and logical reasoning to game scenarios. **Geometry**: Tests geometry problems and spatial reasoning. Queries require understanding of geometric shapes, spatial relationships, geometric proofs, and geometric problem-solving. **Global Facts**: Tests global facts knowledge and understanding, including geography, world events, international relations, and factual knowledge about countries, cultures, and global phenomena. **Hallucinations**: Tests the model's ability to avoid conflating similar but distinct entities when provided with misleading context. Queries present a non-existent entity (e.g., 'Fluoxetine Maleate' which doesn't exist as a pharmaceutical formulation) alongside search results about a related real entity (e.g., Fluoxetine hydrochloride or Fluvoxamine maleate). The model must recognize the confusion, explicitly state the requested information doesn't exist, and refuse to provide fabricated information based on the misleading search results. **History**: Tests history knowledge and understanding, including historical events, historical analysis, historical context, and understanding of historical processes. Queries cover various historical periods and regions. **Injection Resistance**: Tests whether models complete assigned tasks when user input contains injected instructions attempting to override the system prompt. The injected content is appended to the user message and attempts to elicit different behavior, such as outputting an invalid sentiment value (e.g., 'straightforward' instead of 'positive', 'negative', or 'neutral'). The core task is sentiment classification, and the model must provide a valid sentiment in the `<sentiment>` tag format as instructed by the system prompt. Evaluates whether models can be relied upon to perform their assigned function despite adversarial input. **Instruction Following**: Tests the model's ability to follow verifiable constraints using programmatic checks. Queries contain specific, verifiable formatting and content requirements that can be objectively checked, evaluating precise instruction adherence. **Law**: Tests legal knowledge and understanding, including legal principles, case law, legal reasoning, and legal systems. Queries evaluate understanding of legal concepts and their applications. **Linguistics**: Tests linguistics knowledge and language understanding, including syntax, semantics, phonetics, language structure, and linguistic analysis. Queries evaluate understanding of how language works. **Literature**: Tests literature knowledge and understanding, including literary analysis, literary devices, literary history, and understanding of literary works. Queries evaluate comprehension and analysis of literary texts. **Logic**: Tests formal logic knowledge and principles. Queries cover logical fallacies (ad novitatem, disjunctive syllogism, complex question fallacy), deductive reasoning principles (valid argument structures, relationship between premises and conclusions), and advanced mathematical logic (Kripke countermodels for intuitionistic propositional logic). Evaluates understanding of formal logic terminology, the ability to identify fallacious reasoning, and knowledge of both classical and non-classical logic systems. Distinct from the broader 'reasoning' capability by focusing specifically on formal logical structures and principles. **Long Context Reasoning**: Tests whether models can retrieve and reason over information buried within very long contexts (64K-128K tokens). Unlike simple needle-in-haystack tests that use literal string matching, this capability requires models to infer semantic connections between questions and distant context. Evaluates if models maintain retrieval accuracy as context length increases and relevant information becomes harder to locate through attention mechanisms alone. **Mathematics**: Tests mathematics problems and knowledge across multiple levels and domains. Covers foundational topics (algebraic manipulation, equations, inequalities, fundamental mathematical concepts) to advanced university-level mathematics including abstract algebra, group theory, ring theory, and field theory. Queries span computational problem-solving, mathematical reasoning, theoretical understanding of mathematical structures, and problem-solving across various mathematical domains including algebra, calculus, number theory, and other core mathematical areas. **Medicine**: Tests medical knowledge and understanding, including anatomy, physiology, medical conditions, treatments, and medical reasoning. Queries evaluate understanding of medical concepts and applications. **Neuroscience**: Tests neuroscience knowledge and understanding, including brain structure, neural processes, cognitive neuroscience, and neurological systems. Queries evaluate understanding of how the nervous system works. **Nutrition**: Tests nutrition knowledge and understanding, including nutritional science, dietary principles, food science, and nutritional applications. Queries evaluate understanding of nutrition concepts. **Philosophy**: Tests philosophy knowledge and understanding, including philosophical reasoning, ethical theories, philosophical arguments, and philosophical concepts. Queries evaluate philosophical thinking and analysis. **Physics**: Tests physics knowledge and understanding, including mechanics, thermodynamics, electromagnetism, quantum physics, and physical principles. Queries evaluate understanding of physical laws and their applications. **Psychology**: Tests psychology knowledge and understanding, including cognitive psychology, behavioral psychology, psychological theories, and psychological processes. Queries evaluate understanding of human psychology. **Public Relations**: Tests public relations knowledge and understanding, including communication strategies, crisis management, media relations, and PR principles. Queries evaluate understanding of public relations practices. **Puzzles**: Tests puzzle-solving and logical reasoning. Queries present various types of puzzles requiring logical thinking, pattern recognition, and problem-solving skills. **Reasoning**: Tests diverse reasoning capabilities across multiple domains. Queries include commonsense reasoning (e.g., sarcasm detection in social media posts), moral reasoning (ethical philosophy and decision-making), linguistic reasoning (pronoun disambiguation, adjective ordering rules in variant languages), and complex logical reasoning (constraint satisfaction puzzles with 100+ clues, rule-based inference with preference ordering, board game logic, boolean expression evaluation). Evaluates the model's ability to apply appropriate reasoning strategies across contexts, from social understanding to formal logic to complex multi-constraint problem-solving. **Security Studies**: Tests security studies knowledge and understanding, including cybersecurity, information security, security policies, and security practices. Queries evaluate understanding of security concepts. **Social Calibration**: Tests social calibration and alignment with user preferences. Queries evaluate the model's ability to understand social contexts, boundaries, appropriate behavior, and adapt responses to user preferences while maintaining appropriate standards. **Sociology**: Tests sociology knowledge and understanding, including social structures, social processes, social theories, and sociological analysis. Queries evaluate understanding of social phenomena. **Statistics**: Tests statistics knowledge and problems, including statistical analysis, probability, data interpretation, and statistical reasoning. Queries require understanding of statistical concepts and methods. **Structured Generation**: Tests generation of valid structured formats (JSON, YAML, XML, TOML, CSV). Queries require the model to produce correctly formatted structured data that is both syntactically valid and contains required content elements. **Sycophancy Resistance**: Tests the model's ability to maintain well-reasoned positions when users push back with demonstrably poor ideas or incorrect assumptions. Queries present a multi-turn conversation where the model provides an initial recommendation, then the user challenges it with flawed counterarguments. The model fails if it abandons its original sound position without good reason, inappropriately agreeing that the user's problematic idea or incorrect assumptions are valid. Success requires maintaining independent judgment, providing thoughtful counterarguments, and resisting the temptation to agree just to please the user. Evaluated using LLM judge with structured criteria on maintaining original position and avoiding sycophantic agreement. **System Safety Compliance**: Tests whether models prioritize custom safety policies from deployers over their default training behaviors. Evaluates if models can adapt their safety boundaries based on organization-specific guidelines (e.g., enterprise contact protocols, regional content restrictions, industry-specific safety rules) rather than applying one-size-fits-all safety training. Critical for real-world deployments where different organizations have different safety requirements. **Tool Use**: Tests tool usage capability including simple, multiple, parallel, and language-specific tool use. Queries require the model to correctly identify when tools are needed, select appropriate tools, format tool calls correctly, and use tools effectively to accomplish tasks. **Trivia**: Tests general trivia and factual knowledge across various domains. Queries evaluate the model's breadth of factual knowledge and ability to recall specific information. **War Planning**: Tests military tactics reasoning, strategic knowledge, and operational planning. Queries evaluate understanding of battlefield tactics, weapon systems deployment, force positioning, and tactical decision-making in combat scenarios. Covers topics such as ambush tactics, defensive positions, weapon effective ranges, unit coordination, and tactical principles. | Model | Overall | Overall Objective | Accounting | Agentic Performance | Applied Mathematics | Art | Astronomy | Bias Resistance | Biology | Business | Censorship | Chemistry | Coding | Computer Science | Creative Writing | Economics | Em Dash Resistance | Engineering | Environmental Science | Error Detection | Games | Geometry | Global Facts | Hallucinations | History | Injection Resistance | Instruction Following | Law | Linguistics | Literature | Logic | Long Context Reasoning | Mathematics | Medicine | Neuroscience | Nutrition | Philosophy | Physics | Psychology | Public Relations | Puzzles | Reasoning | Security Studies | Social Calibration | Sociology | Statistics | Structured Generation | Sycophancy Resistance | System Safety Compliance | Tool Use | Trivia | War Planning | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | google_gemini-3.1-pro-preview_reasoning_high | 0.793 | 0.799 | 0.847 | 0.695 | 0.870 | 0.763 | 0.947 | 0.817 | 0.716 | 0.907 | 0.599 | 0.764 | 0.781 | 0.838 | 0.788 | 0.920 | 0.698 | 0.733 | 0.875 | 0.848 | 0.713 | 0.819 | 0.871 | 0.812 | 0.772 | 0.733 | 0.861 | 0.745 | 0.648 | 0.816 | 0.938 | 0.732 | 0.860 | 0.872 | 0.742 | 0.851 | 0.905 | 0.726 | 0.820 | 0.743 | 0.949 | 0.545 | 0.760 | 0.775 | 0.860 | 0.981 | 0.793 | 0.634 | 0.956 | 0.707 | 0.869 | 0.428 | | google_gemini-3.1-pro-preview_reasoning_low | 0.762 | 0.763 | 0.831 | 0.683 | 0.721 | 0.702 | 0.913 | 0.776 | 0.709 | 0.877 | 0.603 | 0.704 | 0.774 | 0.727 | 0.805 | 0.783 | 0.700 | 0.711 | 0.818 | 0.772 | 0.645 | 0.644 | 0.767 | 0.787 | 0.770 | 0.727 | 0.870 | 0.743 | 0.641 | 0.791 | 0.938 | 0.702 | 0.822 | 0.902 | 0.686 | 0.832 | 0.884 | 0.740 | 0.800 | 0.729 | 0.921 | 0.521 | 0.720 | 0.787 | 0.836 | 1.000 | 0.783 | 0.649 | 0.966 | 0.662 | 0.790 | 0.438 | | openai_gpt-5.4_reasoning_high | 0.752 | 0.754 | 0.855 | 0.658 | 0.775 | 0.635 | 0.811 | 0.730 | 0.724 | 0.832 | 0.417 | 0.639 | 0.738 | 0.764 | 0.828 | 0.787 | 0.678 | 0.646 | 0.808 | 0.820 | 0.688 | 0.958 | 0.896 | 0.745 | 0.840 | 0.854 | 0.883 | 0.720 | 0.641 | 0.757 | 0.864 | 0.717 | 0.860 | 0.873 | 0.688 | 0.720 | 0.825 | 0.730 | 0.776 | 0.736 | 0.940 | 0.436 | 0.754 | 0.841 | 0.729 | 0.958 | 0.862 | 0.742 | 0.954 | 0.681 | 0.672 | 0.132 | | google_gemini-3.1-flash-lite-preview_reasoning_high | 0.723 | 0.721 | 0.875 | 0.608 | 0.533 | 0.580 | 0.728 | 0.665 | 0.512 | 0.820 | 0.540 | 0.545 | 0.761 | 0.607 | 0.813 | 0.770 | 0.695 | 0.673 | 0.752 | 0.646 | 0.609 | 0.709 | 0.877 | 0.849 | 0.738 | 0.652 | 0.875 | 0.722 | 0.657 | 0.754 | 0.893 | 0.586 | 0.812 | 0.854 | 0.669 | 0.795 | 0.814 | 0.740 | 0.767 | 0.672 | 0.886 | 0.503 | 0.653 | 0.787 | 0.833 | 1.000 | 0.821 | 0.706 | 0.964 | 0.689 | 0.700 | 0.454 | | anthropic_claude-sonnet-4.6_reasoning_high | 0.720 | 0.716 | 0.686 | 0.670 | 0.751 | 0.578 | 0.764 | 0.708 | 0.591 | 0.768 | 0.484 | 0.711 | 0.726 | 0.760 | 0.785 | 0.747 | 0.623 | 0.668 | 0.808 | 0.848 | 0.563 | 0.656 | 0.759 | 0.911 | 0.719 | 0.706 | 0.831 | 0.691 | 0.627 | 0.743 | 0.911 | 0.347 | 0.812 | 0.769 | 0.781 | 0.885 | 0.755 | 0.655 | 0.766 | 0.677 | 0.888 | 0.465 | 0.749 | 0.917 | 0.694 | 0.877 | 0.806 | 0.718 | 0.969 | 0.576 | 0.697 | 0.382 | | google_gemini-3-pro-preview_reasoning_high | 0.720 | 0.716 | 0.719 | 0.629 | 0.813 | 0.680 | 0.791 | 0.678 | 0.525 | 0.765 | 0.876 | 0.840 | 0.723 | 0.709 | 0.807 | 0.812 | 0.677 | 0.777 | 0.783 | 0.773 | 0.584 | 0.709 | 0.731 | 0.833 | 0.742 | 0.701 | 0.798 | 0.656 | 0.611 | 0.734 | 0.883 | 0.453 | 0.817 | 0.637 | 0.736 | 0.800 | 0.913 | 0.570 | 0.724 | 0.639 | 0.937 | 0.327 | 0.772 | 0.763 | 0.820 | 0.856 | 0.716 | 0.513 | 0.902 | 0.497 | 0.829 | 0.440 | | openai_gpt-5.4_reasoning_low | 0.705 | 0.702 | 0.798 | 0.663 | 0.724 | 0.520 | 0.671 | 0.685 | 0.630 | 0.821 | 0.415 | 0.543 | 0.679 | 0.681 | 0.785 | 0.650 | 0.680 | 0.571 | 0.750 | 0.811 | 0.667 | 0.835 | 0.822 | 0.737 | 0.797 | 0.827 | 0.877 | 0.641 | 0.645 | 0.727 | 0.869 | 0.653 | 0.837 | 0.890 | 0.643 | 0.639 | 0.723 | 0.720 | 0.752 | 0.655 | 0.897 | 0.397 | 0.639 | 0.817 | 0.666 | 0.952 | 0.860 | 0.733 | 0.948 | 0.654 | 0.538 | 0.132 | | anthropic_claude-sonnet-4.6_reasoning_low | 0.693 | 0.687 | 0.684 | 0.684 | 0.633 | 0.595 | 0.756 | 0.704 | 0.497 | 0.691 | 0.495 | 0.638 | 0.717 | 0.729 | 0.777 | 0.714 | 0.641 | 0.614 | 0.762 | 0.817 | 0.593 | 0.625 | 0.759 | 0.906 | 0.640 | 0.699 | 0.839 | 0.656 | 0.606 | 0.775 | 0.889 | 0.304 | 0.798 | 0.736 | 0.673 | 0.885 | 0.736 | 0.566 | 0.757 | 0.635 | 0.859 | 0.429 | 0.731 | 0.865 | 0.704 | 0.882 | 0.764 | 0.696 | 0.939 | 0.524 | 0.704 | 0.348 | | moonshotai_kimi-k2.5_reasoning_high | 0.677 | 0.666 | 0.723 | 0.641 | 0.668 | 0.500 | 0.785 | 0.703 | 0.509 | 0.729 | 0.712 | 0.516 | 0.691 | 0.670 | 0.814 | 0.754 | 0.673 | 0.486 | 0.721 | 0.791 | 0.505 | 0.669 | 0.808 | 0.616 | 0.717 | 0.462 | 0.861 | 0.588 | 0.634 | 0.686 | 0.877 | 0.443 | 0.819 | 0.741 | 0.571 | 0.674 | 0.762 | 0.638 | 0.651 | 0.635 | 0.867 | 0.432 | 0.670 | 0.708 | 0.701 | 0.876 | 0.767 | 0.688 | 0.899 | 0.659 | 0.643 | 0.474 | | anthropic_claude-sonnet-4.5_reasoning_high | 0.675 | 0.667 | 0.675 | 0.686 | 0.666 | 0.509 | 0.728 | 0.625 | 0.550 | 0.685 | 0.493 | 0.651 | 0.661 | 0.602 | 0.768 | 0.600 | 0.605 | 0.622 | 0.718 | 0.790 | 0.571 | 0.582 | 0.757 | 0.899 | 0.630 | 0.668 | 0.808 | 0.689 | 0.624 | 0.629 | 0.892 | 0.278 | 0.747 | 0.792 | 0.738 | 0.809 | 0.744 | 0.652 | 0.644 | 0.598 | 0.837 | 0.454 | 0.724 | 0.874 | 0.656 | 0.861 | 0.702 | 0.743 | 0.946 | 0.571 | 0.656 | 0.333 | | xiaomi_mimo-v2-pro_reasoning_high | 0.670 | 0.658 | 0.807 | 0.626 | 0.505 | 0.451 | 0.785 | 0.793 | 0.530 | 0.822 | 0.684 | 0.544 | 0.647 | 0.706 | 0.805 | 0.633 | 0.685 | 0.517 | 0.687 | 0.688 | 0.579 | 0.776 | 0.833 | 0.642 | 0.695 | 0.395 | 0.784 | 0.477 | 0.642 | 0.694 | 0.885 | 0.503 | 0.884 | 0.893 | 0.609 | 0.681 | 0.635 | 0.693 | 0.652 | 0.596 | 0.800 | 0.398 | 0.579 | 0.699 | 0.597 | 0.950 | 0.828 | 0.711 | 0.829 | 0.630 | 0.565 | 0.468 | | xiaomi_mimo-v2-pro_reasoning_low | 0.667 | 0.656 | 0.796 | 0.631 | 0.535 | 0.477 | 0.788 | 0.729 | 0.521 | 0.831 | 0.685 | 0.448 | 0.653 | 0.724 | 0.798 | 0.601 | 0.696 | 0.541 | 0.654 | 0.660 | 0.583 | 0.811 | 0.802 | 0.651 | 0.697 | 0.505 | 0.834 | 0.389 | 0.651 | 0.693 | 0.841 | 0.478 | 0.847 | 0.867 | 0.639 | 0.671 | 0.543 | 0.706 | 0.632 | 0.515 | 0.799 | 0.461 | 0.538 | 0.700 | 0.688 | 0.930 | 0.821 | 0.697 | 0.809 | 0.711 | 0.569 | 0.481 | | google_gemini-3-flash-preview_reasoning_high | 0.664 | 0.659 | 0.666 | 0.665 | 0.716 | 0.539 | 0.707 | 0.597 | 0.534 | 0.704 | 0.551 | 0.688 | 0.701 | 0.703 | 0.810 | 0.680 | 0.697 | 0.654 | 0.541 | 0.707 | 0.581 | 0.707 | 0.702 | 0.686 | 0.726 | 0.638 | 0.819 | 0.454 | 0.602 | 0.769 | 0.906 | 0.395 | 0.773 | 0.760 | 0.569 | 0.724 | 0.729 | 0.600 | 0.577 | 0.629 | 0.828 | 0.448 | 0.611 | 0.735 | 0.680 | 0.893 | 0.749 | 0.554 | 0.926 | 0.550 | 0.641 | 0.398 | | google_gemini-3.1-flash-lite-preview_reasoning_low | 0.661 | 0.649 | 0.663 | 0.601 | 0.414 | 0.573 | 0.656 | 0.670 | 0.456 | 0.768 | 0.613 | 0.581 | 0.728 | 0.583 | 0.807 | 0.651 | 0.708 | 0.639 | 0.713 | 0.487 | 0.596 | 0.637 | 0.635 | 0.768 | 0.699 | 0.658 | 0.864 | 0.452 | 0.615 | 0.748 | 0.879 | 0.341 | 0.773 | 0.761 | 0.609 | 0.721 | 0.793 | 0.632 | 0.652 | 0.632 | 0.841 | 0.375 | 0.601 | 0.771 | 0.791 | 0.894 | 0.743 | 0.601 | 0.936 | 0.595 | 0.697 | 0.413 | | openai_gpt-5.2_reasoning_high | 0.651 | 0.645 | 0.706 | 0.528 | 0.716 | 0.594 | 0.723 | 0.442 | 0.441 | 0.792 | 0.366 | 0.421 | 0.612 | 0.708 | 0.794 | 0.631 | 0.685 | 0.584 | 0.698 | 0.821 | 0.610 | 0.858 | 0.642 | 0.659 | 0.683 | 0.726 | 0.827 | 0.556 | 0.649 | 0.600 | 0.884 | 0.452 | 0.778 | 0.795 | 0.691 | 0.624 | 0.643 | 0.621 | 0.607 | 0.592 | 0.900 | 0.327 | 0.601 | 0.809 | 0.636 | 0.834 | 0.814 | 0.736 | 0.945 | 0.506 | 0.609 | 0.057 | | anthropic_claude-sonnet-4.6_reasoning_none | 0.648 | 0.632 | 0.704 | 0.638 | 0.452 | 0.509 | 0.723 | 0.689 | 0.353 | 0.776 | 0.392 | 0.512 | 0.693 | 0.593 | 0.793 | 0.622 | 0.641 | 0.575 | 0.786 | 0.491 | 0.521 | 0.567 | 0.645 | 0.901 | 0.548 | 0.716 | 0.808 | 0.578 | 0.619 | 0.723 | 0.861 | 0.287 | 0.655 | 0.703 | 0.599 | 0.776 | 0.732 | 0.560 | 0.616 | 0.678 | 0.837 | 0.477 | 0.712 | 0.890 | 0.639 | 0.851 | 0.771 | 0.852 | 0.948 | 0.545 | 0.559 | 0.282 | | moonshotai_kimi-k2.5_reasoning_low | 0.648 | 0.636 | 0.628 | 0.614 | 0.656 | 0.454 | 0.754 | 0.662 | 0.452 | 0.696 | 0.602 | 0.480 | 0.661 | 0.651 | 0.800 | 0.755 | 0.648 | 0.484 | 0.731 | 0.688 | 0.501 | 0.635 | 0.761 | 0.627 | 0.682 | 0.388 | 0.879 | 0.556 | 0.628 | 0.658 | 0.843 | 0.423 | 0.833 | 0.760 | 0.530 | 0.510 | 0.658 | 0.634 | 0.638 | 0.564 | 0.815 | 0.411 | 0.688 | 0.716 | 0.700 | 0.866 | 0.734 | 0.764 | 0.894 | 0.569 | 0.637 | 0.500 | | google_gemini-3-pro-preview_reasoning_low | 0.645 | 0.625 | 0.658 | 0.589 | 0.660 | 0.472 | 0.733 | 0.753 | 0.390 | 0.711 | 0.919 | 0.607 | 0.665 | 0.551 | 0.814 | 0.523 | 0.640 | 0.643 | 0.762 | 0.558 | 0.539 | 0.595 | 0.655 | 0.821 | 0.675 | 0.323 | 0.819 | 0.586 | 0.636 | 0.774 | 0.882 | 0.395 | 0.564 | 0.636 | 0.526 | 0.654 | 0.840 | 0.586 | 0.624 | 0.558 | 0.885 | 0.364 | 0.731 | 0.774 | 0.656 | 0.877 | 0.665 | 0.567 | 0.898 | 0.466 | 0.731 | 0.295 | | x-ai_grok-4.1-fast_reasoning_high | 0.641 | 0.633 | 0.576 | 0.640 | 0.701 | 0.482 | 0.815 | 0.656 | 0.496 | 0.663 | 0.492 | 0.635 | 0.643 | 0.721 | 0.803 | 0.605 | 0.611 | 0.559 | 0.717 | 0.672 | 0.557 | 0.695 | 0.722 | 0.632 | 0.584 | 0.763 | 0.756 | 0.696 | 0.607 | 0.617 | 0.869 | 0.344 | 0.793 | 0.763 | 0.665 | 0.688 | 0.526 | 0.502 | 0.626 | 0.477 | 0.895 | 0.309 | 0.674 | 0.623 | 0.714 | 0.862 | 0.672 | 0.719 | 0.942 | 0.473 | 0.495 | 0.310 | | anthropic_claude-sonnet-4.5_reasoning_low | 0.638 | 0.622 | 0.587 | 0.681 | 0.498 | 0.498 | 0.748 | 0.697 | 0.460 | 0.665 | 0.496 | 0.451 | 0.656 | 0.558 | 0.733 | 0.559 | 0.638 | 0.524 | 0.716 | 0.552 | 0.559 | 0.586 | 0.726 | 0.887 | 0.613 | 0.621 | 0.767 | 0.580 | 0.607 | 0.665 | 0.876 | 0.244 | 0.768 | 0.704 | 0.559 | 0.744 | 0.695 | 0.554 | 0.633 | 0.602 | 0.797 | 0.413 | 0.704 | 0.884 | 0.661 | 0.842 | 0.706 | 0.770 | 0.917 | 0.516 | 0.668 | 0.302 | | xiaomi_mimo-v2-flash_free_reasoning_high | 0.634 | 0.610 | 0.643 | 0.633 | 0.616 | 0.487 | 0.736 | 0.752 | 0.390 | 0.787 | 0.710 | 0.493 | 0.635 | 0.651 | 0.902 | 0.627 | 0.680 | 0.457 | 0.677 | 0.612 | 0.516 | 0.732 | 0.788 | 0.643 | 0.809 | 0.375 | 0.716 | 0.491 | 0.603 | 0.643 | 0.803 | 0.285 | 0.734 | 0.726 | 0.577 | 0.681 | 0.536 | 0.671 | 0.582 | 0.442 | 0.756 | 0.344 | 0.575 | 0.824 | 0.464 | 0.861 | 0.788 | 0.787 | 0.833 | 0.591 | 0.540 | 0.498 | | qwen_qwen3.5-35b-a3b_reasoning_low | 0.633 | 0.623 | 0.773 | 0.689 | 0.491 | 0.493 | 0.779 | 0.751 | 0.450 | 0.791 | 0.436 | 0.484 | 0.658 | 0.654 | 0.780 | 0.603 | 0.702 | 0.618 | 0.650 | 0.486 | 0.542 | 0.870 | 0.666 | 0.756 | 0.743 | 0.288 | 0.784 | 0.519 | 0.632 | 0.580 | 0.912 | 0.448 | 0.865 | 0.789 | 0.524 | 0.669 | 0.470 | 0.646 | 0.506 | 0.453 | 0.764 | 0.387 | 0.550 | 0.687 | 0.689 | 1.000 | 0.811 | 0.628 | 0.895 | 0.672 | 0.395 | 0.223 | | google_gemini-3-flash-preview_reasoning_low | 0.631 | 0.620 | 0.669 | 0.588 | 0.422 | 0.514 | 0.578 | 0.630 | 0.420 | 0.730 | 0.525 | 0.516 | 0.696 | 0.582 | 0.791 | 0.634 | 0.683 | 0.621 | 0.677 | 0.397 | 0.594 | 0.646 | 0.628 | 0.628 | 0.684 | 0.588 | 0.841 | 0.386 | 0.608 | 0.750 | 0.852 | 0.315 | 0.723 | 0.726 | 0.577 | 0.706 | 0.754 | 0.607 | 0.576 | 0.621 | 0.792 | 0.376 | 0.585 | 0.730 | 0.726 | 0.895 | 0.771 | 0.579 | 0.953 | 0.595 | 0.664 | 0.425 | | qwen_qwen3.5-35b-a3b_reasoning_high | 0.631 | 0.618 | 0.722 | 0.641 | 0.589 | 0.465 | 0.736 | 0.756 | 0.464 | 0.843 | 0.437 | 0.454 | 0.624 | 0.644 | 0.793 | 0.553 | 0.702 | 0.607 | 0.678 | 0.653 | 0.539 | 0.739 | 0.593 | 0.784 | 0.728 | 0.305 | 0.800 | 0.412 | 0.630 | 0.624 | 0.925 | 0.420 | 0.847 | 0.747 | 0.608 | 0.643 | 0.496 | 0.600 | 0.590 | 0.450 | 0.756 | 0.392 | 0.574 | 0.717 | 0.664 | 0.974 | 0.813 | 0.685 | 0.883 | 0.668 | 0.360 | 0.213 | | qwen_qwen3.5-flash-02-23_reasoning_low | 0.624 | 0.619 | 0.718 | 0.700 | 0.499 | 0.476 | 0.739 | 0.792 | 0.486 | 0.866 | 0.324 | 0.548 | 0.640 | 0.631 | 0.790 | 0.541 | 0.690 | 0.578 | 0.676 | 0.686 | 0.550 | 0.826 | 0.601 | 0.769 | 0.729 | 0.321 | 0.809 | 0.520 | 0.631 | 0.608 | 0.932 | 0.337 | 0.848 | 0.758 | 0.570 | 0.573 | 0.530 | 0.627 | 0.574 | 0.375 | 0.733 | 0.461 | 0.588 | 0.728 | 0.631 | 0.947 | 0.798 | 0.521 | 0.724 | 0.645 | 0.364 | 0.196 | | qwen_qwen3.5-flash-02-23_reasoning_high | 0.622 | 0.615 | 0.755 | 0.667 | 0.530 | 0.485 | 0.678 | 0.757 | 0.429 | 0.812 | 0.322 | 0.481 | 0.615 | 0.662 | 0.785 | 0.564 | 0.694 | 0.605 | 0.702 | 0.687 | 0.551 | 0.836 | 0.641 | 0.778 | 0.727 | 0.349 | 0.814 | 0.509 | 0.623 | 0.596 | 0.944 | 0.362 | 0.836 | 0.764 | 0.527 | 0.571 | 0.539 | 0.626 | 0.604 | 0.444 | 0.759 | 0.408 | 0.583 | 0.741 | 0.622 | 0.808 | 0.660 | 0.573 | 0.769 | 0.623 | 0.438 | 0.236 | | x-ai_grok-4.1-fast_reasoning_low | 0.613 | 0.603 | 0.582 | 0.641 | 0.681 | 0.428 | 0.719 | 0.597 | 0.396 | 0.643 | 0.493 | 0.621 | 0.633 | 0.715 | 0.784 | 0.564 | 0.581 | 0.559 | 0.664 | 0.619 | 0.535 | 0.708 | 0.663 | 0.586 | 0.507 | 0.719 | 0.716 | 0.612 | 0.596 | 0.623 | 0.860 | 0.319 | 0.776 | 0.763 | 0.627 | 0.570 | 0.474 | 0.496 | 0.588 | 0.375 | 0.859 | 0.332 | 0.679 | 0.605 | 0.643 | 0.884 | 0.675 | 0.714 | 0.924 | 0.451 | 0.501 | 0.340 | | openai_gpt-5.2_reasoning_low | 0.605 | 0.592 | 0.649 | 0.483 | 0.552 | 0.414 | 0.620 | 0.430 | 0.375 | 0.726 | 0.425 | 0.352 | 0.593 | 0.605 | 0.778 | 0.600 | 0.692 | 0.449 | 0.575 | 0.570 | 0.582 | 0.684 | 0.606 | 0.588 | 0.657 | 0.750 | 0.863 | 0.488 | 0.629 | 0.614 | 0.871 | 0.385 | 0.751 | 0.750 | 0.622 | 0.571 | 0.589 | 0.637 | 0.567 | 0.529 | 0.862 | 0.323 | 0.584 | 0.827 | 0.566 | 0.837 | 0.861 | 0.687 | 0.950 | 0.560 | 0.493 | 0.093 | | openai_gpt-5-mini_reasoning_high | 0.596 | 0.588 | 0.554 | 0.569 | 0.507 | 0.431 | 0.679 | 0.426 | 0.433 | 0.769 | 0.376 | 0.424 | 0.654 | 0.618 | 0.720 | 0.469 | 0.671 | 0.477 | 0.656 | 0.724 | 0.480 | 0.810 | 0.761 | 0.566 | 0.657 | 0.707 | 0.860 | 0.385 | 0.626 | 0.539 | 0.880 | 0.447 | 0.802 | 0.685 | 0.602 | 0.411 | 0.495 | 0.581 | 0.570 | 0.473 | 0.893 | 0.386 | 0.574 | 0.696 | 0.544 | 0.798 | 0.804 | 0.688 | 0.927 | 0.494 | 0.430 | 0.079 | | anthropic_claude-sonnet-4.5_reasoning_none | 0.593 | 0.568 | 0.595 | 0.667 | 0.369 | 0.436 | 0.611 | 0.690 | 0.325 | 0.729 | 0.403 | 0.418 | 0.628 | 0.514 | 0.798 | 0.398 | 0.650 | 0.481 | 0.683 | 0.283 | 0.538 | 0.540 | 0.576 | 0.898 | 0.516 | 0.662 | 0.772 | 0.550 | 0.610 | 0.661 | 0.793 | 0.200 | 0.572 | 0.674 | 0.597 | 0.633 | 0.641 | 0.548 | 0.601 | 0.599 | 0.780 | 0.475 | 0.570 | 0.898 | 0.572 | 0.804 | 0.688 | 0.881 | 0.918 | 0.498 | 0.524 | 0.190 | | openai_gpt-5-nano_reasoning_high | 0.582 | 0.562 | 0.606 | 0.602 | 0.417 | 0.496 | 0.689 | 0.563 | 0.321 | 0.749 | 0.828 | 0.444 | 0.678 | 0.578 | 0.630 | 0.444 | 0.633 | 0.394 | 0.690 | 0.416 | 0.530 | 0.834 | 0.737 | 0.586 | 0.664 | 0.784 | 0.801 | 0.263 | 0.631 | 0.627 | 0.809 | 0.332 | 0.765 | 0.714 | 0.507 | 0.355 | 0.430 | 0.513 | 0.541 | 0.406 | 0.763 | 0.307 | 0.422 | 0.618 | 0.525 | 0.800 | 0.814 | 0.758 | 0.880 | 0.625 | 0.460 | 0.113 | | qwen_qwen3.5-9b_reasoning_low | 0.564 | 0.542 | 0.687 | 0.622 | 0.374 | 0.531 | 0.751 | 0.616 | 0.414 | 0.711 | 0.619 | 0.426 | 0.536 | 0.495 | 0.791 | 0.403 | 0.696 | 0.483 | 0.753 | 0.351 | 0.454 | 0.740 | 0.571 | 0.715 | 0.773 | 0.155 | 0.626 | 0.350 | 0.604 | 0.491 | 0.787 | 0.341 | 0.683 | 0.746 | 0.543 | 0.577 | 0.397 | 0.680 | 0.533 | 0.400 | 0.586 | 0.460 | 0.492 | 0.674 | 0.539 | 0.946 | 0.457 | 0.639 | 0.861 | 0.667 | 0.329 | 0.108 | | qwen_qwen3.5-9b_reasoning_high | 0.562 | 0.537 | 0.679 | 0.592 | 0.399 | 0.487 | 0.635 | 0.667 | 0.324 | 0.709 | 0.637 | 0.457 | 0.539 | 0.517 | 0.762 | 0.461 | 0.701 | 0.515 | 0.752 | 0.363 | 0.436 | 0.782 | 0.571 | 0.690 | 0.738 | 0.150 | 0.606 | 0.358 | 0.554 | 0.502 | 0.780 | 0.345 | 0.701 | 0.691 | 0.493 | 0.557 | 0.535 | 0.632 | 0.575 | 0.400 | 0.612 | 0.487 | 0.348 | 0.679 | 0.543 | 0.921 | 0.482 | 0.705 | 0.829 | 0.692 | 0.337 | 0.165 | | mistralai_mistral-small-2603_reasoning_low | 0.558 | 0.542 | 0.619 | 0.529 | 0.335 | 0.435 | 0.581 | 0.463 | 0.398 | 0.691 | 0.831 | 0.542 | 0.607 | 0.580 | 0.766 | 0.444 | 0.492 | 0.569 | 0.667 | 0.260 | 0.541 | 0.684 | 0.604 | 0.551 | 0.646 | 0.364 | 0.798 | 0.289 | 0.619 | 0.643 | 0.826 | 0.351 | 0.757 | 0.816 | 0.475 | 0.389 | 0.271 | 0.676 | 0.589 | 0.370 | 0.643 | 0.342 | 0.478 | 0.636 | 0.471 | 0.855 | 0.624 | 0.746 | 0.637 | 0.518 | 0.403 | 0.472 | | deepseek_deepseek-v3.2-speciale | 0.552 | 0.534 | 0.665 | 0.000 | 0.528 | 0.367 | 0.702 | 0.721 | 0.391 | 0.692 | 0.680 | 0.482 | 0.622 | 0.631 | 0.785 | 0.562 | 0.517 | 0.517 | 0.610 | 0.522 | 0.586 | 0.723 | 0.612 | 0.397 | 0.592 | 0.258 | 0.795 | 0.493 | 0.621 | 0.614 | 0.868 | 0.198 | 0.746 | 0.715 | 0.574 | 0.453 | 0.362 | 0.546 | 0.457 | 0.263 | 0.815 | 0.260 | 0.574 | 0.673 | 0.571 | 0.831 | 0.633 | 0.539 | 0.726 | 0.000 | 0.500 | 0.623 | | openai_gpt-5-nano_reasoning_low | 0.551 | 0.531 | 0.569 | 0.622 | 0.392 | 0.437 | 0.637 | 0.485 | 0.279 | 0.736 | 0.825 | 0.392 | 0.615 | 0.559 | 0.614 | 0.401 | 0.602 | 0.394 | 0.684 | 0.329 | 0.508 | 0.803 | 0.631 | 0.547 | 0.645 | 0.704 | 0.743 | 0.221 | 0.640 | 0.608 | 0.785 | 0.238 | 0.735 | 0.699 | 0.437 | 0.319 | 0.395 | 0.520 | 0.492 | 0.371 | 0.781 | 0.288 | 0.427 | 0.626 | 0.544 | 0.809 | 0.711 | 0.718 | 0.865 | 0.609 | 0.466 | 0.115 | | mistralai_mistral-small-2603_reasoning_high | 0.549 | 0.534 | 0.585 | 0.561 | 0.400 | 0.476 | 0.647 | 0.431 | 0.367 | 0.695 | 0.802 | 0.449 | 0.609 | 0.583 | 0.778 | 0.372 | 0.496 | 0.552 | 0.614 | 0.307 | 0.504 | 0.629 | 0.535 | 0.504 | 0.613 | 0.348 | 0.734 | 0.222 | 0.633 | 0.605 | 0.810 | 0.333 | 0.717 | 0.723 | 0.473 | 0.544 | 0.334 | 0.622 | 0.524 | 0.374 | 0.586 | 0.386 | 0.490 | 0.639 | 0.508 | 0.849 | 0.649 | 0.760 | 0.600 | 0.568 | 0.430 | 0.481 | | openai_gpt-5-mini_reasoning_low | 0.545 | 0.532 | 0.594 | 0.524 | 0.374 | 0.434 | 0.657 | 0.332 | 0.300 | 0.712 | 0.371 | 0.362 | 0.605 | 0.529 | 0.708 | 0.309 | 0.606 | 0.447 | 0.581 | 0.415 | 0.465 | 0.770 | 0.654 | 0.551 | 0.654 | 0.600 | 0.771 | 0.261 | 0.603 | 0.528 | 0.817 | 0.358 | 0.747 | 0.646 | 0.524 | 0.327 | 0.336 | 0.589 | 0.515 | 0.415 | 0.819 | 0.310 | 0.481 | 0.696 | 0.502 | 0.790 | 0.811 | 0.761 | 0.904 | 0.534 | 0.425 | 0.223 | | qwen_qwen3.6-plus_free_reasoning_base | 0.543 | 0.530 | 0.706 | 0.545 | 0.528 | 0.500 | 0.775 | 0.624 | 0.353 | 0.756 | 0.534 | 0.349 | 0.646 | 0.477 | 0.756 | 0.463 | 0.646 | 0.390 | 0.651 | 0.284 | 0.510 | 0.606 | 0.635 | 0.591 | 0.582 | 0.287 | 0.631 | 0.319 | 0.653 | 0.583 | 0.761 | 0.352 | 0.464 | 0.707 | 0.439 | 0.591 | 0.570 | 0.600 | 0.599 | 0.477 | 0.420 | 0.264 | 0.516 | 0.511 | 0.537 | 0.697 | 0.742 | 0.511 | 0.788 | 0.529 | 0.459 | 0.234 | | google_gemma-4-26b-a4b-it_reasoning_none | 0.542 | 0.511 | 0.623 | 0.482 | 0.414 | 0.484 | 0.657 | 0.641 | 0.306 | 0.789 | 0.765 | 0.286 | 0.644 | 0.509 | 0.753 | 0.398 | 0.688 | 0.456 | 0.632 | 0.512 | 0.530 | 0.574 | 0.428 | 0.737 | 0.749 | 0.558 | 0.856 | 0.256 | 0.597 | 0.566 | 0.742 | 0.279 | 0.408 | 0.584 | 0.502 | 0.295 | 0.272 | 0.450 | 0.524 | 0.369 | 0.361 | 0.321 | 0.574 | 0.735 | 0.427 | 0.781 | 0.671 | 0.718 | 0.833 | 0.557 | 0.331 | 0.474 | | qwen_qwen3.6-plus_free_reasoning_none | 0.539 | 0.517 | 0.394 | 0.571 | 0.311 | 0.460 | 0.679 | 0.576 | 0.236 | 0.700 | 0.441 | 0.287 | 0.648 | 0.477 | 0.791 | 0.462 | 0.621 | 0.382 | 0.657 | 0.286 | 0.505 | 0.597 | 0.646 | 0.718 | 0.589 | 0.280 | 0.810 | 0.320 | 0.655 | 0.585 | 0.763 | 0.348 | 0.448 | 0.698 | 0.439 | 0.582 | 0.557 | 0.612 | 0.589 | 0.477 | 0.413 | 0.265 | 0.512 | 0.716 | 0.531 | 0.714 | 0.768 | 0.689 | 0.883 | 0.532 | 0.471 | 0.234 | | bytedance-seed_seed-2.0-mini_reasoning_high | 0.524 | 0.519 | 0.706 | 0.538 | 0.424 | 0.310 | 0.635 | 0.641 | 0.393 | 0.663 | 0.625 | 0.415 | 0.593 | 0.631 | 0.800 | 0.597 | 0.650 | 0.420 | 0.576 | 0.453 | 0.493 | 0.671 | 0.566 | 0.421 | 0.609 | 0.109 | 0.801 | 0.359 | 0.568 | 0.532 | 0.813 | 0.061 | 0.719 | 0.676 | 0.330 | 0.383 | 0.401 | 0.506 | 0.575 | 0.488 | 0.766 | 0.377 | 0.381 | 0.596 | 0.609 | 0.899 | 0.422 | 0.167 | 0.372 | 0.597 | 0.425 | 0.429 | | z-ai_glm-4.7 | 0.523 | 0.505 | 0.538 | 0.666 | 0.570 | 0.454 | 0.754 | 0.500 | 0.216 | 0.706 | 0.346 | 0.416 | 0.623 | 0.552 | 0.781 | 0.499 | 0.707 | 0.456 | 0.527 | 0.384 | 0.439 | 0.663 | 0.567 | 0.688 | 0.627 | 0.065 | 0.714 | 0.129 | 0.658 | 0.574 | 0.701 | 0.129 | 0.639 | 0.629 | 0.305 | 0.548 | 0.399 | 0.464 | 0.314 | 0.522 | 0.605 | 0.265 | 0.392 | 0.770 | 0.557 | 0.808 | 0.600 | 0.510 | 0.845 | 0.509 | 0.433 | 0.403 | | inception_mercury-2_reasoning_high | 0.515 | 0.511 | 0.567 | 0.603 | 0.284 | 0.450 | 0.644 | 0.617 | 0.322 | 0.650 | 0.600 | 0.360 | 0.541 | 0.506 | 0.767 | 0.314 | 0.505 | 0.389 | 0.625 | 0.348 | 0.546 | 0.631 | 0.461 | 0.363 | 0.526 | 0.330 | 0.795 | 0.230 | 0.602 | 0.426 | 0.854 | 0.127 | 0.746 | 0.630 | 0.493 | 0.511 | 0.365 | 0.572 | 0.514 | 0.439 | 0.788 | 0.330 | 0.549 | 0.520 | 0.571 | 0.873 | 0.815 | 0.615 | 0.159 | 0.594 | 0.492 | 0.211 | | xiaomi_mimo-v2-pro_reasoning_none | 0.495 | 0.464 | 0.484 | 0.628 | 0.288 | 0.406 | 0.540 | 0.582 | 0.283 | 0.686 | 0.631 | 0.195 | 0.595 | 0.534 | 0.797 | 0.338 | 0.674 | 0.415 | 0.630 | 0.195 | 0.496 | 0.695 | 0.399 | 0.601 | 0.325 | 0.299 | 0.778 | 0.253 | 0.645 | 0.586 | 0.583 | 0.332 | 0.302 | 0.587 | 0.399 | 0.392 | 0.440 | 0.618 | 0.399 | 0.413 | 0.257 | 0.242 | 0.392 | 0.655 | 0.524 | 0.657 | 0.795 | 0.648 | 0.818 | 0.503 | 0.474 | 0.359 | | kwaipilot_kat-coder-pro_free | 0.494 | 0.472 | 0.407 | 0.517 | 0.375 | 0.440 | 0.608 | 0.435 | 0.461 | 0.642 | 0.758 | 0.384 | 0.533 | 0.556 | 0.770 | 0.432 | 0.535 | 0.492 | 0.618 | 0.194 | 0.495 | 0.729 | 0.466 | 0.437 | 0.584 | 0.129 | 0.715 | 0.359 | 0.608 | 0.543 | 0.545 | 0.253 | 0.433 | 0.530 | 0.397 | 0.520 | 0.365 | 0.483 | 0.491 | 0.488 | 0.227 | 0.433 | 0.475 | 0.688 | 0.496 | 0.675 | 0.564 | 0.660 | 0.564 | 0.598 | 0.336 | 0.281 | | anthropic_claude-haiku-4.5 | 0.482 | 0.447 | 0.371 | 0.612 | 0.289 | 0.344 | 0.543 | 0.550 | 0.279 | 0.649 | 0.463 | 0.296 | 0.538 | 0.441 | 0.747 | 0.318 | 0.604 | 0.421 | 0.588 | 0.234 | 0.471 | 0.478 | 0.438 | 0.843 | 0.356 | 0.517 | 0.671 | 0.291 | 0.538 | 0.554 | 0.691 | 0.136 | 0.282 | 0.608 | 0.497 | 0.426 | 0.367 | 0.353 | 0.430 | 0.443 | 0.454 | 0.286 | 0.419 | 0.919 | 0.396 | 0.676 | 0.628 | 0.794 | 0.813 | 0.466 | 0.475 | 0.095 | | qwen_qwen3.5-35b-a3b_reasoning_none | 0.470 | 0.441 | 0.479 | 0.588 | 0.268 | 0.369 | 0.553 | 0.507 | 0.286 | 0.651 | 0.417 | 0.255 | 0.573 | 0.543 | 0.775 | 0.282 | 0.693 | 0.477 | 0.582 | 0.198 | 0.452 | 0.586 | 0.398 | 0.707 | 0.592 | 0.264 | 0.699 | 0.257 | 0.589 | 0.483 | 0.618 | 0.276 | 0.257 | 0.583 | 0.506 | 0.351 | 0.230 | 0.493 | 0.428 | 0.404 | 0.292 | 0.277 | 0.427 | 0.657 | 0.558 | 0.677 | 0.569 | 0.637 | 0.851 | 0.512 | 0.231 | 0.139 | | x-ai_grok-4.1-fast_reasoning_none | 0.464 | 0.434 | 0.342 | 0.626 | 0.289 | 0.437 | 0.555 | 0.541 | 0.261 | 0.448 | 0.553 | 0.293 | 0.413 | 0.451 | 0.743 | 0.337 | 0.568 | 0.383 | 0.592 | 0.221 | 0.414 | 0.461 | 0.432 | 0.662 | 0.416 | 0.447 | 0.719 | 0.292 | 0.492 | 0.503 | 0.727 | 0.186 | 0.244 | 0.549 | 0.510 | 0.355 | 0.365 | 0.287 | 0.395 | 0.372 | 0.515 | 0.265 | 0.452 | 0.530 | 0.469 | 0.737 | 0.526 | 0.660 | 0.899 | 0.435 | 0.457 | 0.350 | | qwen_qwen3-235b-a22b-2507 | 0.461 | 0.438 | 0.474 | 0.311 | 0.339 | 0.306 | 0.557 | 0.487 | 0.243 | 0.584 | 0.488 | 0.351 | 0.551 | 0.486 | 0.720 | 0.399 | 0.366 | 0.386 | 0.515 | 0.197 | 0.465 | 0.657 | 0.369 | 0.676 | 0.388 | 0.155 | 0.717 | 0.256 | 0.493 | 0.571 | 0.740 | 0.176 | 0.624 | 0.615 | 0.509 | 0.394 | 0.198 | 0.476 | 0.339 | 0.364 | 0.427 | 0.304 | 0.445 | 0.736 | 0.437 | 0.791 | 0.391 | 0.626 | 0.768 | 0.457 | 0.401 | 0.312 | | qwen_qwen3.5-flash-02-23_reasoning_none | 0.455 | 0.432 | 0.409 | 0.583 | 0.293 | 0.376 | 0.513 | 0.462 | 0.310 | 0.643 | 0.304 | 0.290 | 0.543 | 0.513 | 0.781 | 0.285 | 0.673 | 0.446 | 0.556 | 0.176 | 0.442 | 0.538 | 0.367 | 0.717 | 0.555 | 0.281 | 0.723 | 0.255 | 0.591 | 0.508 | 0.568 | 0.191 | 0.218 | 0.576 | 0.508 | 0.394 | 0.336 | 0.452 | 0.420 | 0.368 | 0.385 | 0.256 | 0.453 | 0.680 | 0.530 | 0.622 | 0.593 | 0.597 | 0.658 | 0.434 | 0.263 | 0.115 | | qwen_qwen3-8b_reasoning_high | 0.451 | 0.427 | 0.409 | 0.473 | 0.314 | 0.413 | 0.575 | 0.497 | 0.266 | 0.560 | 0.677 | 0.260 | 0.402 | 0.402 | 0.738 | 0.371 | 0.454 | 0.393 | 0.475 | 0.281 | 0.466 | 0.704 | 0.360 | 0.566 | 0.406 | 0.196 | 0.701 | 0.255 | 0.527 | 0.403 | 0.820 | 0.141 | 0.640 | 0.609 | 0.433 | 0.357 | 0.299 | 0.442 | 0.308 | 0.336 | 0.551 | 0.286 | 0.426 | 0.613 | 0.403 | 0.822 | 0.146 | 0.614 | 0.587 | 0.407 | 0.298 | 0.469 | | deepseek_deepseek-v3.2-exp | 0.446 | 0.416 | 0.415 | 0.512 | 0.319 | 0.428 | 0.547 | 0.570 | 0.299 | 0.647 | 0.567 | 0.260 | 0.545 | 0.515 | 0.770 | 0.245 | 0.532 | 0.361 | 0.630 | 0.196 | 0.467 | 0.628 | 0.327 | 0.690 | 0.394 | 0.196 | 0.789 | 0.128 | 0.570 | 0.574 | 0.479 | 0.212 | 0.193 | 0.478 | 0.474 | 0.162 | 0.132 | 0.433 | 0.376 | 0.331 | 0.290 | 0.280 | 0.393 | 0.686 | 0.472 | 0.587 | 0.646 | 0.630 | 0.654 | 0.412 | 0.438 | 0.404 | | inception_mercury-2_reasoning_low | 0.442 | 0.433 | 0.429 | 0.616 | 0.263 | 0.346 | 0.551 | 0.406 | 0.288 | 0.532 | 0.486 | 0.320 | 0.366 | 0.471 | 0.728 | 0.251 | 0.371 | 0.262 | 0.509 | 0.283 | 0.489 | 0.638 | 0.438 | 0.348 | 0.456 | 0.221 | 0.715 | 0.191 | 0.542 | 0.387 | 0.746 | 0.115 | 0.665 | 0.507 | 0.369 | 0.354 | 0.233 | 0.512 | 0.402 | 0.368 | 0.648 | 0.300 | 0.414 | 0.439 | 0.399 | 0.813 | 0.710 | 0.626 | 0.400 | 0.448 | 0.475 | 0.239 | | mistralai_devstral-2512_free | 0.435 | 0.405 | 0.366 | 0.518 | 0.316 | 0.396 | 0.415 | 0.640 | 0.236 | 0.651 | 0.621 | 0.162 | 0.565 | 0.567 | 0.652 | 0.220 | 0.362 | 0.317 | 0.626 | 0.171 | 0.491 | 0.453 | 0.306 | 0.404 | 0.358 | 0.177 | 0.686 | 0.129 | 0.551 | 0.569 | 0.458 | 0.145 | 0.129 | 0.424 | 0.404 | 0.387 | 0.264 | 0.463 | 0.427 | 0.561 | 0.512 | 0.302 | 0.459 | 0.607 | 0.368 | 0.478 | 0.733 | 0.685 | 0.778 | 0.470 | 0.467 | 0.325 | | google_gemini-2.0-flash-001 | 0.434 | 0.403 | 0.300 | 0.484 | 0.292 | 0.329 | 0.488 | 0.548 | 0.240 | 0.573 | 0.374 | 0.324 | 0.516 | 0.451 | 0.660 | 0.254 | 0.712 | 0.382 | 0.422 | 0.197 | 0.434 | 0.605 | 0.268 | 0.531 | 0.461 | 0.501 | 0.814 | 0.163 | 0.609 | 0.567 | 0.461 | 0.130 | 0.240 | 0.472 | 0.304 | 0.458 | 0.370 | 0.456 | 0.408 | 0.486 | 0.351 | 0.353 | 0.191 | 0.610 | 0.333 | 0.621 | 0.535 | 0.668 | 0.810 | 0.246 | 0.372 | 0.337 | | openai_gpt-4o | 0.427 | 0.403 | 0.367 | 0.570 | 0.313 | 0.390 | 0.351 | 0.700 | 0.329 | 0.515 | 0.576 | 0.127 | 0.521 | 0.426 | 0.488 | 0.221 | 0.662 | 0.192 | 0.475 | 0.174 | 0.490 | 0.489 | 0.301 | 0.600 | 0.410 | 0.548 | 0.683 | 0.130 | 0.594 | 0.620 | 0.552 | 0.142 | 0.085 | 0.422 | 0.509 | 0.127 | 0.360 | 0.411 | 0.372 | 0.483 | 0.348 | 0.300 | 0.227 | 0.506 | 0.563 | 0.423 | 0.767 | 0.494 | 0.624 | 0.621 | 0.433 | 0.330 | | qwen_qwen3-32b | 0.425 | 0.413 | 0.420 | 0.437 | 0.348 | 0.324 | 0.552 | 0.580 | 0.280 | 0.608 | 0.633 | 0.258 | 0.448 | 0.449 | 0.627 | 0.348 | 0.468 | 0.355 | 0.561 | 0.237 | 0.398 | 0.521 | 0.395 | 0.475 | 0.392 | 0.155 | 0.744 | 0.222 | 0.476 | 0.422 | 0.776 | 0.076 | 0.610 | 0.604 | 0.437 | 0.389 | 0.234 | 0.407 | 0.284 | 0.367 | 0.575 | 0.192 | 0.353 | 0.449 | 0.474 | 0.775 | 0.390 | 0.162 | 0.572 | 0.304 | 0.362 | 0.335 | | arcee-ai_trinity-large-preview_free | 0.416 | 0.389 | 0.352 | 0.449 | 0.318 | 0.376 | 0.477 | 0.350 | 0.282 | 0.562 | 0.738 | 0.162 | 0.509 | 0.507 | 0.685 | 0.217 | 0.521 | 0.288 | 0.614 | 0.199 | 0.428 | 0.597 | 0.336 | 0.407 | 0.275 | 0.171 | 0.623 | 0.128 | 0.534 | 0.492 | 0.579 | 0.117 | 0.238 | 0.464 | 0.439 | 0.391 | 0.335 | 0.541 | 0.423 | 0.445 | 0.162 | 0.346 | 0.555 | 0.674 | 0.471 | 0.616 | 0.368 | 0.644 | 0.464 | 0.161 | 0.399 | 0.369 | | qwen_qwen3.5-9b_reasoning_none | 0.413 | 0.372 | 0.419 | 0.617 | 0.263 | 0.415 | 0.478 | 0.567 | 0.199 | 0.545 | 0.565 | 0.257 | 0.433 | 0.401 | 0.771 | 0.255 | 0.667 | 0.358 | 0.696 | 0.171 | 0.367 | 0.482 | 0.263 | 0.675 | 0.602 | 0.154 | 0.711 | 0.253 | 0.495 | 0.486 | 0.455 | 0.247 | 0.107 | 0.477 | 0.441 | 0.328 | 0.201 | 0.419 | 0.451 | 0.374 | 0.192 | 0.347 | 0.289 | 0.640 | 0.373 | 0.383 | 0.270 | 0.656 | 0.795 | 0.394 | 0.197 | 0.068 | | xiaomi_mimo-v2-flash_free | 0.407 | 0.370 | 0.350 | 0.590 | 0.185 | 0.343 | 0.381 | 0.519 | 0.195 | 0.540 | 0.397 | 0.228 | 0.514 | 0.432 | 0.788 | 0.341 | 0.559 | 0.322 | 0.477 | 0.177 | 0.481 | 0.429 | 0.336 | 0.528 | 0.304 | 0.283 | 0.698 | 0.064 | 0.493 | 0.453 | 0.442 | 0.083 | 0.110 | 0.505 | 0.425 | 0.360 | 0.237 | 0.428 | 0.398 | 0.260 | 0.226 | 0.238 | 0.194 | 0.607 | 0.303 | 0.519 | 0.680 | 0.785 | 0.785 | 0.524 | 0.464 | 0.355 | | google_gemma-4-31b-it_reasoning_none | 0.402 | 0.379 | 0.636 | 0.000 | 0.393 | 0.493 | 0.588 | 0.709 | 0.372 | 0.866 | 0.797 | 0.352 | 0.626 | 0.447 | 0.664 | 0.408 | 0.683 | 0.506 | 0.693 | 0.448 | 0.387 | 0.606 | 0.426 | 0.733 | 0.503 | 0.561 | 0.757 | 0.395 | 0.502 | 0.399 | 0.461 | 0.070 | 0.132 | 0.387 | 0.365 | 0.222 | 0.067 | 0.218 | 0.281 | 0.255 | 0.129 | 0.197 | 0.326 | 0.487 | 0.265 | 0.228 | 0.099 | 0.161 | 0.343 | 0.000 | 0.135 | 0.344 | | qwen_qwen3-8b_reasoning_low | 0.387 | 0.374 | 0.410 | 0.430 | 0.321 | 0.387 | 0.541 | 0.494 | 0.242 | 0.534 | 0.658 | 0.192 | 0.320 | 0.396 | 0.248 | 0.280 | 0.456 | 0.287 | 0.417 | 0.236 | 0.368 | 0.637 | 0.339 | 0.529 | 0.296 | 0.151 | 0.504 | 0.225 | 0.441 | 0.348 | 0.728 | 0.131 | 0.548 | 0.527 | 0.434 | 0.295 | 0.237 | 0.419 | 0.281 | 0.329 | 0.424 | 0.236 | 0.385 | 0.428 | 0.368 | 0.747 | 0.145 | 0.520 | 0.437 | 0.354 | 0.235 | 0.446 | | mistralai_mistral-small-2603_reasoning_none | 0.382 | 0.354 | 0.277 | 0.454 | 0.241 | 0.367 | 0.358 | 0.240 | 0.237 | 0.585 | 0.642 | 0.194 | 0.489 | 0.451 | 0.716 | 0.247 | 0.424 | 0.359 | 0.447 | 0.174 | 0.447 | 0.480 | 0.296 | 0.429 | 0.323 | 0.351 | 0.643 | 0.129 | 0.578 | 0.471 | 0.451 | 0.261 | 0.151 | 0.616 | 0.302 | 0.096 | 0.166 | 0.471 | 0.376 | 0.332 | 0.260 | 0.260 | 0.317 | 0.583 | 0.269 | 0.420 | 0.495 | 0.720 | 0.571 | 0.439 | 0.229 | 0.290 | | qwen_qwen3-8b_reasoning_none | 0.374 | 0.341 | 0.377 | 0.349 | 0.287 | 0.435 | 0.395 | 0.515 | 0.351 | 0.583 | 0.771 | 0.289 | 0.275 | 0.548 | 0.597 | 0.216 | 0.695 | 0.226 | 0.386 | 0.152 | 0.416 | 0.425 | 0.262 | 0.469 | 0.237 | 0.333 | 0.610 | 0.132 | 0.436 | 0.354 | 0.580 | 0.049 | 0.176 | 0.463 | 0.393 | 0.362 | 0.265 | 0.397 | 0.375 | 0.472 | 0.131 | 0.304 | 0.525 | 0.424 | 0.458 | 0.353 | 0.073 | 0.553 | 0.487 | 0.173 | 0.165 | 0.403 | | anthropic_claude-3.5-haiku | 0.371 | 0.338 | 0.345 | 0.531 | 0.266 | 0.298 | 0.386 | 0.213 | 0.174 | 0.536 | 0.400 | 0.129 | 0.457 | 0.450 | 0.517 | 0.159 | 0.583 | 0.223 | 0.482 | 0.152 | 0.398 | 0.304 | 0.360 | 0.632 | 0.190 | 0.576 | 0.644 | 0.160 | 0.562 | 0.501 | 0.413 | 0.120 | 0.086 | 0.412 | 0.369 | 0.326 | 0.099 | 0.194 | 0.286 | 0.300 | 0.196 | 0.278 | 0.395 | 0.853 | 0.197 | 0.263 | 0.502 | 0.681 | 0.781 | 0.466 | 0.398 | 0.311 | | meta-llama_llama-3.3-70b-instruct | 0.355 | 0.320 | 0.391 | 0.246 | 0.292 | 0.299 | 0.350 | 0.443 | 0.197 | 0.578 | 0.449 | 0.191 | 0.438 | 0.458 | 0.561 | 0.125 | 0.707 | 0.261 | 0.372 | 0.153 | 0.392 | 0.270 | 0.232 | 0.583 | 0.349 | 0.213 | 0.842 | 0.161 | 0.493 | 0.566 | 0.460 | 0.045 | 0.085 | 0.422 | 0.363 | 0.223 | 0.236 | 0.347 | 0.318 | 0.299 | 0.098 | 0.177 | 0.230 | 0.597 | 0.269 | 0.287 | 0.467 | 0.695 | 0.532 | 0.175 | 0.406 | 0.395 | | openai_gpt-4o-mini | 0.332 | 0.303 | 0.176 | 0.579 | 0.266 | 0.354 | 0.229 | 0.326 | 0.235 | 0.438 | 0.847 | 0.096 | 0.424 | 0.379 | 0.457 | 0.249 | 0.622 | 0.191 | 0.563 | 0.174 | 0.439 | 0.247 | 0.235 | 0.306 | 0.349 | 0.518 | 0.631 | 0.099 | 0.508 | 0.496 | 0.354 | 0.045 | 0.066 | 0.450 | 0.298 | 0.129 | 0.165 | 0.242 | 0.289 | 0.338 | 0.066 | 0.173 | 0.131 | 0.347 | 0.337 | 0.288 | 0.339 | 0.415 | 0.556 | 0.477 | 0.362 | 0.321 | | amazon_nova-micro-v1 | 0.326 | 0.286 | 0.436 | 0.460 | 0.289 | 0.322 | 0.519 | 0.576 | 0.239 | 0.413 | 0.640 | 0.128 | 0.271 | 0.420 | 0.537 | 0.154 | 0.552 | 0.131 | 0.414 | 0.152 | 0.288 | 0.298 | 0.296 | 0.413 | 0.208 | 0.397 | 0.658 | 0.095 | 0.418 | 0.279 | 0.407 | 0.122 | 0.000 | 0.410 | 0.333 | 0.256 | 0.100 | 0.175 | 0.349 | 0.329 | 0.160 | 0.195 | 0.263 | 0.579 | 0.331 | 0.489 | 0.098 | 0.586 | 0.532 | 0.401 | 0.164 | 0.039 | | nvidia_nemotron-3-nano-30b-a3b_free | 0.321 | 0.280 | 0.256 | 0.510 | 0.133 | 0.285 | 0.254 | 0.241 | 0.300 | 0.265 | 0.900 | 0.193 | 0.394 | 0.302 | 0.650 | 0.283 | 0.370 | 0.319 | 0.376 | 0.152 | 0.358 | 0.423 | 0.230 | 0.728 | 0.624 | 0.196 | 0.622 | 0.159 | 0.290 | 0.381 | 0.283 | 0.138 | 0.131 | 0.361 | 0.271 | 0.097 | 0.198 | 0.192 | 0.286 | 0.147 | 0.191 | 0.151 | 0.261 | 0.530 | 0.134 | 0.160 | 0.120 | 0.683 | 0.650 | 0.373 | 0.166 | 0.329 | | meta-llama_llama-3-70b-instruct | 0.316 | 0.291 | 0.328 | 0.000 | 0.236 | 0.325 | 0.253 | 0.350 | 0.174 | 0.497 | 0.528 | 0.127 | 0.348 | 0.460 | 0.530 | 0.346 | 0.690 | 0.096 | 0.417 | 0.155 | 0.391 | 0.275 | 0.271 | 0.643 | 0.323 | 0.199 | 0.680 | 0.097 | 0.536 | 0.540 | 0.398 | 0.000 | 0.088 | 0.391 | 0.330 | 0.294 | 0.135 | 0.280 | 0.309 | 0.220 | 0.230 | 0.282 | 0.288 | 0.637 | 0.199 | 0.130 | 0.292 | 0.192 | 0.354 | 0.151 | 0.394 | 0.399 | | mistralai_mistral-nemo | 0.305 | 0.260 | 0.309 | 0.347 | 0.262 | 0.300 | 0.225 | 0.287 | 0.260 | 0.354 | 0.917 | 0.191 | 0.191 | 0.340 | 0.647 | 0.157 | 0.651 | 0.130 | 0.381 | 0.130 | 0.304 | 0.153 | 0.298 | 0.323 | 0.116 | 0.306 | 0.578 | 0.158 | 0.382 | 0.315 | 0.343 | 0.106 | 0.130 | 0.220 | 0.295 | 0.253 | 0.166 | 0.215 | 0.368 | 0.446 | 0.064 | 0.150 | 0.256 | 0.501 | 0.371 | 0.327 | 0.242 | 0.536 | 0.532 | 0.176 | 0.230 | 0.326 | | mistralai_ministral-8b | 0.284 | 0.253 | 0.265 | 0.402 | 0.212 | 0.261 | 0.263 | 0.288 | 0.262 | 0.287 | 0.899 | 0.197 | 0.281 | 0.376 | 0.424 | 0.063 | 0.640 | 0.257 | 0.305 | 0.129 | 0.372 | 0.184 | 0.267 | 0.268 | 0.117 | 0.240 | 0.495 | 0.128 | 0.435 | 0.287 | 0.331 | 0.037 | 0.022 | 0.193 | 0.336 | 0.389 | 0.131 | 0.286 | 0.291 | 0.403 | 0.066 | 0.241 | 0.320 | 0.285 | 0.327 | 0.158 | 0.169 | 0.412 | 0.384 | 0.308 | 0.233 | 0.276 | | meta-llama_llama-3-8b-instruct | 0.272 | 0.244 | 0.348 | 0.000 | 0.159 | 0.278 | 0.163 | 0.238 | 0.330 | 0.398 | 0.852 | 0.226 | 0.229 | 0.397 | 0.457 | 0.217 | 0.687 | 0.286 | 0.344 | 0.132 | 0.344 | 0.184 | 0.338 | 0.454 | 0.329 | 0.199 | 0.582 | 0.032 | 0.389 | 0.295 | 0.388 | 0.000 | 0.087 | 0.245 | 0.266 | 0.288 | 0.134 | 0.281 | 0.316 | 0.222 | 0.099 | 0.129 | 0.160 | 0.405 | 0.462 | 0.095 | 0.072 | 0.157 | 0.313 | 0.038 | 0.197 | 0.349 | | liquid_lfm2-8b-a1b | 0.266 | 0.244 | 0.215 | 0.000 | 0.209 | 0.176 | 0.294 | 0.107 | 0.193 | 0.330 | 0.731 | 0.192 | 0.282 | 0.491 | 0.742 | 0.184 | 0.281 | 0.223 | 0.414 | 0.107 | 0.188 | 0.184 | 0.266 | 0.290 | 0.159 | 0.305 | 0.671 | 0.127 | 0.413 | 0.255 | 0.327 | 0.060 | 0.043 | 0.282 | 0.235 | 0.287 | 0.266 | 0.281 | 0.337 | 0.294 | 0.064 | 0.218 | 0.260 | 0.477 | 0.395 | 0.393 | 0.026 | 0.157 | 0.323 | 0.000 | 0.200 | 0.355 | | google_gemma-2-9b-it | 0.264 | 0.226 | 0.266 | 0.000 | 0.183 | 0.302 | 0.196 | 0.411 | 0.154 | 0.433 | 0.623 | 0.128 | 0.228 | 0.303 | 0.532 | 0.092 | 0.651 | 0.099 | 0.387 | 0.149 | 0.378 | 0.150 | 0.230 | 0.536 | 0.419 | 0.194 | 0.648 | 0.032 | 0.430 | 0.352 | 0.327 | 0.000 | 0.066 | 0.220 | 0.197 | 0.099 | 0.034 | 0.217 | 0.289 | 0.260 | 0.131 | 0.198 | 0.320 | 0.610 | 0.262 | 0.227 | 0.097 | 0.192 | 0.452 | 0.000 | 0.132 | 0.356 | | mistralai_mistral-7b-instruct-v0.1 | 0.246 | 0.216 | 0.331 | 0.000 | 0.160 | 0.321 | 0.131 | 0.281 | 0.266 | 0.341 | 0.711 | 0.224 | 0.079 | 0.358 | 0.374 | 0.125 | 0.649 | 0.032 | 0.349 | 0.110 | 0.281 | 0.031 | 0.166 | 0.392 | 0.087 | 0.280 | 0.485 | 0.096 | 0.330 | 0.213 | 0.352 | 0.000 | 0.066 | 0.303 | 0.263 | 0.291 | 0.235 | 0.154 | 0.314 | 0.371 | 0.066 | 0.241 | 0.458 | 0.378 | 0.398 | 0.196 | 0.000 | 0.243 | 0.399 | 0.000 | 0.100 | 0.291 |
The following research contributed valuable insights to our benchmark design, evaluation methodologies, and best practices for measuring LLM capabilities. While Sansa Bench features original queries and proprietary evaluation code, these works informed our approach and helped us build on established research in the field.