By Jamie Munro in Question Answering —

Contextual QA

Question Answering with LLMs. Comparing the leading AI models side-by-side at Contextual QA.

Question Answering: Contextual QA
Question Answering: Contextual QA

Comparing the leading AI models:

Category: Question Answering

Subcategory: Contextual QA

Contents

  1. Introduction
  2. Contextual QA
  3. Prompts
    1. Historical Event Timeline Extraction
    2. Specific Date Retrieval
    3. Scientific Process Output Identification
    4. Historical Attribution
    5. Astronomical Composition Analysis
    6. Numerical Data Extraction
    7. Location Information Extraction
    8. Scientific Discovery Attribution
  4. Performance Verdict
  5. Budget Verdict
  6. Conclusion

Introduction

Comparing AI Models: A Practical Guide to LLM Performance

Looking to compare AI models and find the best artificial intelligence solution for your needs? This comprehensive guide evaluates leading large language models (LLMs) side-by-side, helping you make informed decisions about which AI assistant best suits your use case and budget.

We analyze two distinct tiers of AI models:

Budget-Focused Tier:

  • ChatGPT 4.0 Mini
  • Gemini 1.5 Flash
  • Claude 3.5 Haiku
  • Llama 3.1 8B

Performance-Focused Tier:

  • ChatGPT 4.0
  • Claude 3.5 Sonnet
  • Gemini 1.5 Pro
  • Llama 3.1 70B

By comparing AI models directly, you can better understand their relative strengths, limitations and optimal use cases. Our side-by-side comparisons examine real-world prompts across various tasks, from content creation to coding to analysis.

Choose the budget tier when:

  • Running high-volume, straightforward tasks
  • Working with basic prompts and general knowledge
  • Operating under cost constraints
  • Requiring faster response times

Select the performance tier when:

  • Handling complex, nuanced assignments
  • Needing advanced reasoning capabilities
  • Working with specialized knowledge domains
  • Requiring maximum accuracy and reliability

Through detailed AI model comparisons, we help you identify which LLM delivers the best balance of capability and cost for your specific needs.

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Contextual QA

Large Language Models excel at contextual question answering by leveraging their extensive training on diverse texts and ability to comprehend complex relationships between ideas. Unlike traditional search engines that match keywords, LLMs can understand the nuances of questions, extract relevant information from provided context, and formulate coherent, accurate responses that directly address the query at hand.

The models' strength in contextual QA stems from their transformer architecture, which allows them to maintain awareness of relationships between different parts of the text and identify the most pertinent information needed to answer specific questions. This capability makes them particularly valuable for tasks like document analysis, research assistance, and information extraction where understanding the broader context is crucial for providing accurate answers.

Modern LLMs can handle various types of contextual questions - from simple factual queries to complex analytical questions requiring synthesis of multiple pieces of information. They can also adapt their responses based on the level of detail requested and maintain consistency with the provided context, making them powerful tools for both casual users seeking quick answers and professionals requiring detailed analysis of specific documents or datasets.

Prompts

Historical Event Timeline Extraction

Extracts specific dates or time periods from historical texts. Useful for creating timelines, fact-checking, and historical research.

Prompt:

Based on the passage: 'The Industrial Revolution began in Britain in the late 18th century and brought significant changes to manufacturing processes.' When did the Industrial Revolution start?

Example

On December 7, 1941, at 7:48 a.m. Hawaiian time, the Imperial Japanese Navy Air Service launched a devastating surprise attack on the United States naval base at Pearl Harbor, located on Oahu Island, Hawaii. Over 350 Japanese aircraft, including fighters, bombers, and torpedo planes, descended from the skies in two waves, ultimately sinking four American battleships and damaging four others among the 18 total ships that were struck. The assault, which lasted just under two hours, claimed the lives of 2,403 Americans, including 68 civilians, and wounded another 1,178 people. The following day, December 8, President Franklin D. Roosevelt delivered his famous "Day of Infamy" speech to Congress, and the United States officially entered World War II.

Performance Results

Based on the passage: 'The Industrial Revolution began in Britain in the late 18th century and brought significant changes to manufacturing processes.' When did the Industrial Revolution start?

The Industrial Revolution started in Britain in the late 18th century.

Based on the first sentence of the provided passage, the Industrial Revolution began in Britain in the late 18th century.

However, most of the passage you provided is actually about the Pearl Harbor attack and not about the Industrial Revolution. The Pearl Harbor content appears to be unrelated to the question about the Industrial Revolution's start date.

To directly answer your question: The Industrial Revolution started in Britain in the late 1700s (late 18th century).

The passage about Pearl Harbor does not contain the answer. The first passage states the Industrial Revolution began in Britain in the late 18th century.

The question about when the Industrial Revolution started is based on the first passage. According to the passage, the Industrial Revolution began in Britain in the late 18th century.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

Based on the passage: 'The Industrial Revolution began in Britain in the late 18th century and brought significant changes to manufacturing processes.' When did the Industrial Revolution start?

The Industrial Revolution began in Britain in the late 18th century.

The provided text states that the Industrial Revolution began in Britain in the late 18th century.

Based on the given passages:

Industrial Revolution Start Date:

  • Late 18th century in Britain (around the 1780s-1790s)

Pearl Harbor Attack Date:

  • December 7, 1941 at 7:48 a.m. Hawaiian time

The passages provide different historical contexts - one about the Industrial Revolution and the other about the Pearl Harbor attack during World War II. The question specifically asks about the start of the Industrial Revolution, which according to the first passage, began in Britain in the late 18th century.

The two passages provide distinct information. Here are the answers to your question based on each passage:

  1. Based on the first passage: The Industrial Revolution began in Britain in the late 18th century. Therefore, the start of the Industrial Revolution is not specified as a date but a time period.

  2. Based on the second passage: The passage mentions a specific event, the surprise attack on Pearl Harbor, and indicates that it took place on December 7, 1941.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified that the Industrial Revolution began in Britain in the late 18th century. ChatGPT provided the most concise and direct answer, while Claude and Gemini both noted some confusion with apparently unrelated Pearl Harbor content. Llama delivered an accurate response with the fastest processing time (416ms), while Claude took the longest (2052ms). ChatGPT demonstrated the best balance of accuracy and concision. All models showed strong reading comprehension, though Claude and Gemini's responses suggest potential issues with the input context handling.

Budget Tier

All models correctly identified the Industrial Revolution's start in late 18th century Britain, but with varying levels of detail and efficiency. ChatGPT 4o Mini provided the most concise, accurate response in just 671ms. Gemini 1.5 Flash gave a similar answer but took significantly longer (6688ms). Claude 3.5 Haiku and Llama 3.1 8B included irrelevant information about Pearl Harbor, though Claude's response was better structured. For this simple factual query, ChatGPT 4o Mini demonstrated the best balance of speed and precision.

Specific Date Retrieval

Identifies and extracts specific dates from biographical or historical texts. Useful for building biographical databases and fact verification.

Prompt:

Given this text: 'Marie Curie won Nobel Prizes in both Physics and Chemistry. She won the Physics prize in 1903 and the Chemistry prize in 1911.' In which year did Marie Curie win the Nobel Prize in Chemistry?

Example

Marie Curie (1867-1934) was born in Warsaw, Poland. In 1891, she moved to Paris to study physics and mathematics at the Sorbonne. She married Pierre Curie in 1895, and together they discovered polonium and radium in 1898. In 1903, she became the first woman to win a Nobel Prize, sharing the Physics award with Pierre and Henri Becquerel for their work on radioactivity. After Pierre's death in 1906, she took over his position as professor at the Sorbonne, becoming the first female professor there. In 1911, she won a second Nobel Prize, this time in Chemistry, for her continued research on radioactive elements. During World War I, she developed mobile X-ray units and trained women to be X-ray operators. By her death in 1934, she had established the Radium Institute, revolutionized our understanding of radioactivity, and paved the way for women in science.

Performance Results

Given this text: 'Marie Curie won Nobel Prizes in both Physics and Chemistry. She won the Physics prize in 1903 and the Chemistry prize in 1911.' In which year did Marie Curie win the Nobel Prize in Chemistry?

Marie Curie won the Nobel Prize in Chemistry in the year 1911.

Marie Curie won the Nobel Prize in Chemistry in 1911.

This can be found directly stated in both the initial question text and the longer biographical passage. The prize was awarded for her continued research on radioactive elements.

Additional context: This was her second Nobel Prize, following her 1903 Nobel Prize in Physics which she shared with her husband Pierre Curie and Henri Becquerel.

Marie Curie won the Nobel Prize in Chemistry in 1911.

According to the text, Marie Curie won the Nobel Prize in Chemistry in 1911.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

Given this text: 'Marie Curie won Nobel Prizes in both Physics and Chemistry. She won the Physics prize in 1903 and the Chemistry prize in 1911.' In which year did Marie Curie win the Nobel Prize in Chemistry?

Marie Curie won the Nobel Prize in Chemistry in 1911.

Marie Curie won the Nobel Prize in Chemistry in 1911.

According to the text, Marie Curie won the Nobel Prize in Chemistry in 1911.

Marie Curie won the Nobel Prize in Chemistry in the year 1911.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified that Marie Curie won the Nobel Prize in Chemistry in 1911. ChatGPT, Gemini, and Llama provided concise, accurate answers without extra information. Claude provided additional context about both Nobel Prizes, which wasn't requested. In terms of response time, Llama was fastest at 359ms, while Claude was slowest at 2123ms. For this straightforward factual query, Llama demonstrated optimal performance with its quick, precise response. The other models were equally accurate but either took longer or included unnecessary information.

Budget Tier

All four models provided the correct answer of 1911 with high confidence, demonstrating strong factual extraction capabilities. Llama 3.1 8B was notably the fastest at 239ms, while Gemini 1.5 Flash was slowest at 4176ms. Claude's response included markdown formatting (1911), which wasn't necessary. The models differed slightly in response style but maintained essentially identical content. Performance variations were primarily in speed rather than accuracy, with all models successfully completing this straightforward information extraction task.

Scientific Process Output Identification

Extracts specific products or results from scientific process descriptions. Useful for science education and research summaries.

Prompt:

From the following excerpt: 'Photosynthesis is the process by which plants convert sunlight into chemical energy to produce glucose and oxygen.' What do plants produce through photosynthesis?

Example

Photosynthesis in plants converts light energy into chemical energy through a series of complex biochemical reactions. Carbon dioxide enters through tiny pores called stomata in the leaves, while water is absorbed through the roots and transported upward through specialized tissue. In the chloroplasts, chlorophyll molecules capture sunlight, splitting water molecules into hydrogen and oxygen. The hydrogen combines with carbon dioxide through the Calvin cycle to produce glucose, while oxygen is released as a byproduct through the stomata. This glucose serves as an energy source for the plant and can be converted into other organic compounds like cellulose and starch, which form the plant's structural components and energy storage.

Performance Results

From the following excerpt: 'Photosynthesis is the process by which plants convert sunlight into chemical energy to produce glucose and oxygen.' What do plants produce through photosynthesis?

Through photosynthesis, plants produce glucose and oxygen.

Based on the excerpt, plants produce two main products through photosynthesis:

Primary Products:

  1. Glucose (chemical energy/food source)
  2. Oxygen (released as a byproduct)

Secondary Products: The glucose can then be converted into:

  • Cellulose (structural material)
  • Starch (energy storage)

Note: These secondary products are derived from the glucose produced during photosynthesis and serve important functions in plant structure and energy storage.

Plants produce glucose and oxygen through photosynthesis.

According to the excerpt, through photosynthesis, plants produce:

  1. Glucose: serving as the primary energy source for the plant
  2. Oxygen: released as a byproduct through the stomata

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Budget Results

From the following excerpt: 'Photosynthesis is the process by which plants convert sunlight into chemical energy to produce glucose and oxygen.' What do plants produce through photosynthesis?

Through photosynthesis, plants produce glucose and oxygen.

Plants produce glucose and oxygen through photosynthesis. The glucose is used as an energy source and to build other organic compounds like cellulose and starch.

According to the excerpt, through photosynthesis, plants produce:

  1. Glucose - a primary energy source for the plant
  2. Oxygen - released as a byproduct through the stomata

Additionally, the excerpt notes that the glucose can be further converted into:

  • Cellulose (structural components)
  • Starch (energy storage)

According to the excerpt, through photosynthesis, plants produce:

  1. Glucose (serves as an energy source for the plant)
  2. Oxygen (released as a byproduct)

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

ChatGPT 4o, Gemini 1.5 Pro, and Llama 3.1 70B provided concise, accurate answers identifying glucose and oxygen as the products of photosynthesis. Claude 3.5 Sonnet went beyond the scope with additional details about secondary products. While technically accurate, this wasn't asked for in the prompt. Performance-wise, Llama 3.1 70B was fastest at 413ms, while Claude was slowest at 2433ms. All models demonstrated understanding of the basic concept, but Llama provided the best balance of detail and conciseness while maintaining optimal speed.

Budget Tier

Performance varied significantly across models. ChatGPT 4o Mini was fastest (573ms) but provided minimal detail. Llama 3.1 8B was also quick (257ms) with good accuracy and structure. Claude 3.5 Haiku gave the most comprehensive response, including additional context about glucose usage, though slower (1598ms). Gemini 1.5 Flash was slowest (12710ms) but provided accurate information with context about glucose utilization. All models correctly identified the two main products (glucose and oxygen), but differed in depth of explanation and response time.

Historical Attribution

Identifies specific historical entities responsible for achievements or constructions. Useful for historical research and educational content.

Prompt:

Context: 'The Great Wall of China was built over many centuries by different dynasties, with the most famous sections built during the Ming Dynasty (1368-1644).' Which dynasty built the most famous sections of the Great Wall?

Example

The Great Pyramid of Giza was built during the Fourth Dynasty of ancient Egypt, around 2560 BCE, under the command of Pharaoh Khufu. The colossal structure was meticulously constructed over a period of 20 years by skilled craftsmen and thousands of workers who transported massive limestone blocks from nearby quarries. Standing 481 feet tall upon completion, it remained the world's tallest man-made structure for nearly 4,000 years. The pyramid served as Khufu's tomb and contained elaborate chambers, passageways, and spiritual texts to guide the pharaoh's journey through the afterlife. Its precise architectural design and perfect alignment with the cardinal points demonstrate the advanced mathematical and astronomical knowledge possessed by ancient Egyptian builders.

Performance Results

Context: 'The Great Wall of China was built over many centuries by different dynasties, with the most famous sections built during the Ming Dynasty (1368-1644).' Which dynasty built the most famous sections of the Great Wall?

The most famous sections of the Great Wall of China were built during the Ming Dynasty (1368-1644).

Answer

The Ming Dynasty (1368-1644) built the most famous sections of the Great Wall of China.

Additional Context

While earlier dynasties constructed parts of the wall, the iconic stone and brick fortifications that most people recognize today were built during the Ming period. This extensive construction phase significantly enhanced the wall's defensive capabilities and created the impressive architectural features that have made it one of the world's most famous landmarks.

Note: The second paragraph in the context about the Great Pyramid of Giza is not relevant to answering this specific question about the Great Wall of China.

The Ming Dynasty built the most famous sections of the Great Wall of China.

The Ming Dynasty (1368-1644) built the most famous sections of the Great Wall of China.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

Context: 'The Great Wall of China was built over many centuries by different dynasties, with the most famous sections built during the Ming Dynasty (1368-1644).' Which dynasty built the most famous sections of the Great Wall?

The most famous sections of the Great Wall of China were built during the Ming Dynasty (1368-1644).

The Ming Dynasty built the most famous sections of the Great Wall of China.

Based on the context provided, the Ming Dynasty (1368-1644) built the most famous sections of the Great Wall of China.

The passage gives clear details about the Ming Dynasty's contribution to the Great Wall, specifically noting that the most renowned wall sections were constructed during this period. While the wall was built over many centuries by different dynasties, the Ming Dynasty's sections are particularly well-known and widely recognized today.

The Ming Dynasty (1368-1644) built the most famous sections of the Great Wall of China.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified the Ming Dynasty as builders of the most famous Great Wall sections. ChatGPT and Llama provided concise, accurate answers with dates. Claude gave the most detailed response, adding historical context, but took significantly longer (2673ms). Gemini offered the most minimal correct response. Llama was fastest (418ms) while maintaining accuracy. Overall, each model demonstrated strong factual comprehension, with the main differences being response length and generation speed. For this straightforward factual query, Llama's quick, precise response was most efficient.

Budget Tier

All models correctly identified the Ming Dynasty as builders of the most famous Great Wall sections. ChatGPT 4o Mini and Llama 3.1 8B provided concise, accurate responses with fastest processing times (589ms and 245ms respectively). Claude 3.5 Haiku gave a more detailed explanation with additional context but took longer (2037ms). Gemini 1.5 Flash delivered the simplest response but had the slowest processing time (7453ms). For this straightforward factual query, Llama 3.1 8B demonstrated the best balance of accuracy and speed.

Astronomical Composition Analysis

Extracts information about the physical composition of celestial bodies or features. Useful for astronomy education and research.

Prompt:

Reading passage: 'Saturn's rings are made mostly of water ice, with a small amount of rocky debris and dust.' What is the main composition of Saturn's rings?

Example

HD 209458b is a hot Jupiter exoplanet orbiting a G0V star, located approximately 153 light-years from Earth. The planet has a radius 1.38 times that of Jupiter and a mass 0.69 Jupiter masses. Its atmosphere is primarily composed of hydrogen and helium, with traces of sodium, carbon monoxide, and water vapor. Surface temperatures reach 1,450 Kelvin due to extreme solar radiation and proximity to its host star. The planet exhibits significant atmospheric escape, losing approximately 10,000 tons of hydrogen per second through its extended exosphere.

Performance Results

Reading passage: 'Saturn's rings are made mostly of water ice, with a small amount of rocky debris and dust.' What is the main composition of Saturn's rings?

The main composition of Saturn's rings is water ice.

Main Composition of Saturn's Rings

Answer: Saturn's rings are composed primarily of water ice

Supporting Details:

  • The rings contain smaller amounts of:
  • Rocky debris
  • Dust

Note: The second paragraph about HD 209458b is unrelated to the question about Saturn's rings.

The main composition of Saturn's rings is water ice.

The main composition of Saturn's rings is water ice.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

Reading passage: 'Saturn's rings are made mostly of water ice, with a small amount of rocky debris and dust.' What is the main composition of Saturn's rings?

The main composition of Saturn's rings is mostly water ice, with a small amount of rocky debris and dust.

The main composition of Saturn's rings is water ice.

From the first reading passage about Saturn's rings, the main composition is water ice, with small amounts of rocky debris and dust.

From the passage about HD 209458b, key details include:

  • Atmospheric composition: Primarily hydrogen and helium
  • Trace elements: Sodium, carbon monoxide, water vapor
  • Planet characteristics:
  • Radius: 1.38 × Jupiter
  • Mass: 0.69 Jupiter masses
  • Location: 153 light-years from Earth
  • Temperature: 1,450 Kelvin
  • Unique feature: Significant atmospheric escape (losing 10,000 tons of hydrogen per second)

According to the given passages:

  1. The main composition of Saturn's rings is water ice.
  2. The composition of HD 209458b's atmosphere is hydrogen and helium (primarily), with additional minor components of sodium, carbon monoxide, and water vapor.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified water ice as the main composition of Saturn's rings. ChatGPT and Llama provided concise, direct answers while Claude included unnecessary formatting and extra details. Gemini used bold formatting for emphasis. Response times varied significantly - Llama was fastest at 338ms, followed by ChatGPT (768ms) and Gemini (847ms). Claude was slowest at 1752ms, likely due to its verbose formatting. For this straightforward factual recall question, Llama and ChatGPT demonstrated optimal performance with quick, accurate responses without superfluous information.

Budget Tier

All models correctly identified water ice as the main composition of Saturn's rings. ChatGPT 4o Mini provided the most precise answer, matching the source text exactly. Claude and Llama included irrelevant information about HD 209458b, suggesting possible context confusion. Gemini gave the most concise response but omitted the secondary components. Response times varied significantly - Llama was fastest at 271ms, while Gemini was slowest at 7818ms. For this simple reading comprehension task, ChatGPT 4o Mini demonstrated the best balance of accuracy and relevance.

Numerical Data Extraction

Identifies and extracts specific numerical values or percentages from scientific texts. Useful for data collection and statistical analysis.

Prompt:

According to this text: 'The human brain consists of approximately 86 billion neurons and consumes about 20% of the body's total energy.' What percentage of the body's energy does the brain use?

Example

The human brain processes visual information approximately 60,000 times faster than text, with the average person able to identify images in as little as 13 milliseconds. Studies show that 90% of information transmitted to the brain is visual, while the retina contains about 126 million photoreceptor cells responsible for detecting light and color. During active viewing, human eyes make an average of 3-4 saccadic movements per second, and the optic nerve transmits visual signals to the brain at a remarkable speed of 250 mph (402 km/h).

Performance Results

According to this text: 'The human brain consists of approximately 86 billion neurons and consumes about 20% of the body's total energy.' What percentage of the body's energy does the brain use?

According to the text, the human brain consumes about 20% of the body's total energy.

According to the text, the human brain consumes 20% of the body's total energy.

Additional visual processing facts from the text:

  • Processes visual info ~60,000 times faster than text
  • Can identify images in just 13 milliseconds
  • 90% of information sent to brain is visual
  • Retina has 126 million photoreceptor cells
  • Eyes make 3-4 saccadic movements per second
  • Optic nerve transmits signals at 250 mph (402 km/h)

The text states the brain uses approximately 20% of the body's total energy.

The brain does not use a specific percentage of the body's energy according to the provided text snippet. The initial information about the brain using 20% of the body's energy is given in the initial line but the detailed text snippet does not contain this information, however it was stated at the beginning that 'The human brain consumes about 20% of the body's total energy' The brain uses 20% of the body's total energy.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

According to this text: 'The human brain consists of approximately 86 billion neurons and consumes about 20% of the body's total energy.' What percentage of the body's energy does the brain use?

According to the text, the human brain consumes about 20% of the body's total energy.

The text states that the brain consumes about 20% of the body's total energy.

According to the text, the human brain consumes 20% of the body's total energy.

The passage provides this specific detail about the brain's energy consumption: "The human brain consists of approximately 86 billion neurons and consumes about 20% of the body's total energy."

According to the given text, the human brain consumes about 20% of the body's total energy.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified that the brain uses 20% of the body's total energy, but with varying approaches. ChatGPT and Gemini provided direct, concise answers. Claude gave the correct answer but added irrelevant visual processing facts not present in the source text. Llama initially hesitated before confirming the correct percentage, showing some confusion in its reasoning process. Gemini was most efficient (964ms), followed by ChatGPT (1075ms), while Claude was significantly slower (12340ms). For this simple factual extraction task, Gemini and ChatGPT demonstrated optimal performance with clean, accurate responses.

Budget Tier

All models correctly extracted the 20% figure from the text. Llama 3.1 8B was notably fastest at 316ms, while Gemini 1.5 Flash was slowest at 2445ms. Claude provided the most comprehensive response by including the full context and formatting. ChatGPT Mini and Llama gave concise, accurate answers. While all models performed well on this straightforward extraction task, the differences in response time and verbosity highlight their distinct characteristics. For simple fact extraction, the faster Llama model may be preferable unless additional context is needed.

Location Information Extraction

Identifies specific locations from historical or biographical texts. Useful for creating geographic databases and historical mapping.

Prompt:

Based on: 'The Wright brothers made their first successful flight on December 17, 1903, near Kitty Hawk, North Carolina.' Where did the Wright brothers make their first successful flight?

Example

On April 14, 1865, inside Ford's Theatre on 10th Street in Washington, D.C., President Abraham Lincoln was attending a performance of "Our American Cousin" when John Wilkes Booth entered the presidential box at approximately 10:15 p.m. Booth, a well-known actor and Confederate sympathizer, fired a single shot from his derringer pistol into the back of Lincoln's head. The wounded president was carried across the street to the Petersen House, where he died at 7:22 a.m. the following morning, becoming the first American president to be assassinated.

Performance Results

Based on: 'The Wright brothers made their first successful flight on December 17, 1903, near Kitty Hawk, North Carolina.' Where did the Wright brothers make their first successful flight?

The Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

The Wright brothers made their first successful flight near Kitty Hawk, North Carolina on December 17, 1903.

Note: The second paragraph about Lincoln's assassination, while historically significant, is not relevant to the question about the Wright brothers' first flight location.

The passage you provided describes the assassination of Abraham Lincoln, not the Wright brothers' first flight. According to the first sentence you gave, the Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

The Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

Based on: 'The Wright brothers made their first successful flight on December 17, 1903, near Kitty Hawk, North Carolina.' Where did the Wright brothers make their first successful flight?

The Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

The Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

Based on the first passage, the Wright brothers made their first successful flight near Kitty Hawk, North Carolina.

The second passage provides details about President Lincoln's assassination, but does not relate to the location of the Wright brothers' flight.

The information given does not directly ask for the location of the first successful flight of the Wright brothers which is Kitty Hawk, North Carolina but it does include it.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified Kitty Hawk, North Carolina as the location of the Wright brothers' first flight. ChatGPT and Llama provided concise, accurate answers without additional commentary. Claude and Gemini included unnecessary explanations and references to unrelated content. Llama was fastest at 299ms, while Gemini was slowest at 1458ms. In terms of response quality, ChatGPT and Llama performed best by providing direct, accurate answers without superfluous information.

Budget Tier

All models correctly identified Kitty Hawk, North Carolina as the location of the Wright brothers' first flight. Llama 3.1 8B was fastest (243ms) but provided an unnecessarily complex response. ChatGPT 4o Mini and Gemini 1.5 Flash gave identical, concise answers ideal for this question type. Claude 3.5 Haiku's response included irrelevant information about a second passage not present in the prompt. In terms of response quality vs. speed, ChatGPT 4o Mini offered the best balance with a clear, accurate answer in 545ms.

Scientific Discovery Attribution

Identifies scientists or researchers responsible for specific scientific discoveries. Useful for science history research and education.

Prompt:

From the passage: 'DNA was first isolated by Friedrich Miescher in 1869, but its double helix structure wasn't discovered until 1953 by Watson and Crick.' Who first isolated DNA?

Example

In 1953, James Watson and Francis Crick unveiled the double helix structure of DNA, building upon crucial X-ray crystallography work by Rosalind Franklin at King's College London. Franklin's "Photo 51" provided critical evidence of DNA's helical nature, while earlier research by Erwin Chargaff had established the base-pairing rules that proved essential to understanding DNA's structure. Maurice Wilkins, also at King's College, shared data with Watson and Crick that helped confirm their model. Linus Pauling, though initially proposing an incorrect triple-helix model, had developed techniques for studying molecular structures that influenced their work. The discovery revolutionized molecular biology and earned Watson, Crick, and Wilkins the 1962 Nobel Prize, though Franklin, who died in 1958, was not eligible for the posthumous award.

Performance Results

From the passage: 'DNA was first isolated by Friedrich Miescher in 1869, but its double helix structure wasn't discovered until 1953 by Watson and Crick.' Who first isolated DNA?

Friedrich Miescher first isolated DNA in 1869.

According to the passage, Friedrich Miescher first isolated DNA in 1869, long before the discovery of its double helix structure by Watson and Crick in 1953.

This early isolation of DNA by Miescher was a crucial first step that laid the groundwork for the later groundbreaking structural discoveries made by Watson, Crick, and their contemporaries.

Friedrich Miescher first isolated DNA in 1869.

Friedrich Miescher was the first to isolate DNA. He did it in 1869.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

From the passage: 'DNA was first isolated by Friedrich Miescher in 1869, but its double helix structure wasn't discovered until 1953 by Watson and Crick.' Who first isolated DNA?

The first person to isolate DNA was Friedrich Miescher in 1869.

Friedrich Miescher first isolated DNA.

According to the passage, Friedrich Miescher first isolated DNA in 1869, which predates the famous 1953 discovery of DNA's double helix structure by Watson and Crick.

According to the passage, Friedrich Miescher first isolated DNA in 1869. He is mentioned as isolating DNA before its double helix structure was discovered.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models correctly identified Friedrich Miescher as the first person to isolate DNA in 1869. Llama 3.1 70B provided the fastest response at 338ms, with a concise but complete answer. Gemini 1.5 Pro and ChatGPT 4o gave similarly brief, accurate responses. Claude 3.5 Sonnet took the longest (1783ms) and provided additional context about Watson and Crick's later work, which wasn't asked for in the prompt. While all models were accurate, the more focused responses from Llama, Gemini and ChatGPT better addressed the specific question.

Budget Tier

All models correctly identified Friedrich Miescher as the first person to isolate DNA in 1869. Llama 3.1 8B provided the fastest response at 257ms while maintaining good detail. Claude provided additional context about Watson and Crick's later discovery. Gemini 1.5 Flash was notably slower at 9152ms while giving the most concise answer. ChatGPT 4o Mini and Claude delivered balanced responses in terms of speed and detail. All models demonstrated strong reading comprehension and accurate information extraction from the given passage.

Performance Verdict

Based on the series of analyses comparing ChatGPT 4, Claude 3.5 Sonnet, Gemini 1.5 Pro, and Llama 3.1 70B on contextual question answering tasks, here's how each model performed:

Llama 3.1 70B:

  • Consistently fastest response times (300-420ms)
  • Highly accurate and concise answers
  • Excellent at staying focused on the specific question
  • Occasional minor hesitation in reasoning
  • Best performance for straightforward factual queries

ChatGPT 4:

  • Consistently accurate responses
  • Excellent balance of precision and concision
  • Moderate response times
  • Strong ability to provide relevant information without excess
  • High reliability across different question types

Claude 3.5 Sonnet:

  • Consistently slowest response times (1700-12000ms)
  • Tendency to provide unnecessary additional context
  • Sometimes included irrelevant formatting
  • Very accurate but often verbose
  • Occasional context handling issues

Gemini 1.5 Pro:

  • Generally quick response times
  • Accurate answers with occasional formatting choices
  • Some context handling issues
  • Good balance of information density
  • Sometimes included unnecessary details

Winner: Llama 3.1 70B

Llama 3.1 70B emerges as the winner due to its exceptional combination of speed, accuracy, and concision. While all models demonstrated strong factual comprehension, Llama consistently provided the optimal balance of response quality and processing efficiency. ChatGPT 4 comes in as a close second, showing excellent reliability and precision but with slower response times. Gemini 1.5 Pro performed well but occasionally added unnecessary elements, while Claude 3.5 Sonnet, despite high accuracy, was consistently the slowest and most verbose.

Budget Verdict

Based on the analyses of multiple contextual QA prompts, here's how the models compared:

ChatGPT 4o Mini:

  • Consistently fast response times (500-700ms average)
  • Excellent precision in answers
  • Strong ability to provide relevant information without excess detail
  • Best at balancing speed and accuracy
  • Most reliable for straightforward factual queries

Claude 3.5 Haiku:

  • Moderate response times (1500-2000ms average)
  • Provides comprehensive answers with additional context
  • Occasionally includes irrelevant information
  • Strong in detailed explanations
  • Sometimes adds unnecessary formatting

Gemini 1.5 Flash:

  • Slowest response times (4000-12000ms average)
  • Accurate but often minimal answers
  • Consistent in factual extraction
  • Good at maintaining focus on the specific question
  • Performance limited by slow processing speed

Llama 3.1 8B:

  • Fastest response times (240-320ms average)
  • Generally accurate responses
  • Sometimes includes unnecessary complexity
  • Occasional context confusion
  • Excellent for simple fact extraction

Winner: ChatGPT 4o Mini

ChatGPT 4o Mini consistently demonstrated the best balance of speed, accuracy, and response quality across all prompts. While Llama 3.1 8B was faster, and Claude 3.5 Haiku often provided more detailed responses, ChatGPT 4o Mini delivered the most reliable and efficient performance for contextual QA tasks. Its ability to maintain focus while providing precise answers at reasonable speeds makes it the standout choice for this specific use case.

Conclusion

Conclusion

The comprehensive analysis of both performance and budget tiers reveals distinct patterns in how different AI models handle contextual question answering tasks. While all models demonstrated strong fundamental capabilities, clear differences emerged in speed, accuracy, and response style.

In the performance tier, Llama 3.1 70B distinguished itself through exceptional processing speed without sacrificing accuracy, consistently delivering precise answers in under 420ms. ChatGPT 4.0 showed remarkable reliability and precision, though with slower response times. Both Claude 3.5 Sonnet and Gemini 1.5 Pro, while highly capable, showed tendencies toward verbose responses and occasional context handling issues.

For the budget tier, ChatGPT 4o Mini emerged as the clear leader, offering an optimal balance of speed and accuracy that closely approached performance-tier quality. Llama 3.1 8B demonstrated impressive speed but occasionally struggled with response complexity, while Claude 3.5 Haiku and Gemini 1.5 Flash showed stronger accuracy but significantly slower processing times.

These findings suggest that for organizations prioritizing speed and efficiency in contextual QA tasks, Llama 3.1 70B is the premier choice in the performance tier, while ChatGPT 4o Mini offers the best value in the budget category. However, use cases requiring more detailed analysis or comprehensive context might benefit from Claude or Gemini's more thorough approach, despite their slower processing times.

The results underscore the importance of matching specific use case requirements with model characteristics, as each demonstrates distinct strengths that may prove valuable in different scenarios.

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