{ "metadata": { "model": "devstral-small-2", "short_name": "devstral", "timestamp": "2026-02-07T19:31:17.096811+00:00", "temperature": 0.2, "router_url": "http://localhost:7070" }, "results": [ { "category": "factual", "name": "thermodynamics_laws", "prompt": "Explain the laws of thermodynamics. For each law, state its number (zeroth through third) and the core principle in one sentence.", "response": "1. **Zeroth Law**: If two systems are each in thermal equilibrium with a third, they are in thermal equilibrium with each other.\n2. **First Law**: Energy cannot be created or destroyed, only transferred or converted from one form to another.\n3. **Second Law**: In any energy transfer or transformation, the total entropy of a closed system always increases over time.\n4. **Third Law**: As the temperature of a system approaches absolute zero, the entropy approaches a minimum constant value.", "ttft_ms": 36.73, "total_time_ms": 1757.15, "prompt_tokens": 47, "completion_tokens": 102, "gen_tokens_per_sec": 59.31, "prompt_tokens_per_sec": 1626.58, "vram_peak_mb": 21715.0, "hallucination": { "facts_checked": 5, "facts_correct": 5, "facts_missing": 0, "facts_hallucinated": 0, "hallucination_rate": 0.0, "details": [ { "fact": "Zeroth law: thermal equilibrium is transitive", "status": "correct" }, { "fact": "First law: energy conservation", "status": "correct" }, { "fact": "Second law: entropy increases", "status": "correct" }, { "fact": "Third law: absolute zero", "status": "correct" }, { "fact": "No fabricated laws", "status": "correct" } ] }, "quality_score": 5 }, { "category": "factual", "name": "solar_system_planets", "prompt": "List the 8 planets of our solar system in order from the Sun. Which is the largest and which is the smallest?", "response": "The 8 planets in order from the Sun are:\n\n1. Mercury\n2. Venus\n3. Earth\n4. Mars\n5. Jupiter\n6. Saturn\n7. Uranus\n8. Neptune\n\nThe largest planet is Jupiter, and the smallest is Mercury.", "ttft_ms": 46.33, "total_time_ms": 998.22, "prompt_tokens": 26, "completion_tokens": 57, "gen_tokens_per_sec": 59.87, "prompt_tokens_per_sec": 1009.32, "vram_peak_mb": 21715.0, "hallucination": { "facts_checked": 11, "facts_correct": 11, "facts_missing": 0, "facts_hallucinated": 0, "hallucination_rate": 0.0, "details": [ { "fact": "Mercury is listed", "status": "correct" }, { "fact": "Venus is listed", "status": "correct" }, { "fact": "Earth is listed", "status": "correct" }, { "fact": "Mars is listed", "status": "correct" }, { "fact": "Jupiter is listed", "status": "correct" }, { "fact": "Saturn is listed", "status": "correct" }, { "fact": "Uranus is listed", "status": "correct" }, { "fact": "Neptune is listed", "status": "correct" }, { "fact": "Largest is Jupiter", "status": "correct" }, { "fact": "Smallest is Mercury", "status": "correct" }, { "fact": "Pluto NOT listed as planet", "status": "correct" } ] }, "quality_score": 5 }, { "category": "factual", "name": "programming_language_origins", "prompt": "When were Python, Java, and Rust first publicly released? Who created each language? Give the year and creator for each.", "response": "- **Python**: 1991, Guido van Rossum\n- **Java**: 1995, James Gosling\n- **Rust**: 2010, Graydon Hoare", "ttft_ms": 42.5, "total_time_ms": 796.17, "prompt_tokens": 26, "completion_tokens": 45, "gen_tokens_per_sec": 59.71, "prompt_tokens_per_sec": 1042.29, "vram_peak_mb": 21715.0, "hallucination": { "facts_checked": 6, "facts_correct": 6, "facts_missing": 0, "facts_hallucinated": 0, "hallucination_rate": 0.0, "details": [ { "fact": "Python creator: Guido van Rossum", "status": "correct" }, { "fact": "Python year: 1991", "status": "correct" }, { "fact": "Java creator: James Gosling", "status": "correct" }, { "fact": "Java year: 1995", "status": "correct" }, { "fact": "Rust creator: Graydon Hoare", "status": "correct" }, { "fact": "Rust year: 2010 or 2015", "status": "correct" } ] }, "quality_score": 5 }, { "category": "reasoning", "name": "syllogism", "prompt": "All roses are flowers. Some flowers fade quickly. Can we logically conclude that some roses fade quickly? Explain your reasoning step by step.", "response": "No, we cannot logically conclude that some roses fade quickly.\n\nHere's the reasoning:\n\n1. The statement \"All roses are flowers\" means that the set of roses is a subset of the set of flowers.\n2. The statement \"Some flowers fade quickly\" means that there is at least one flower that fades quickly.\n3. However, this flower that fades quickly could be any flower, not necessarily a rose. It could be a different type of flower that is not a rose.\n4. Therefore, we cannot conclude that some roses fade quickly based on the given information.", "ttft_ms": 42.09, "total_time_ms": 2028.79, "prompt_tokens": 28, "completion_tokens": 118, "gen_tokens_per_sec": 59.4, "prompt_tokens_per_sec": 1083.8, "vram_peak_mb": 21715.0, "hallucination": null, "quality_score": 5 }, { "category": "reasoning", "name": "math_word_problem", "prompt": "A train leaves station A at 9:00 AM traveling at 80 km/h. Another train leaves station B (240 km away) at 9:30 AM traveling toward A at 100 km/h. At what time do they meet? Show your work.", "response": "Let's solve the problem step by step.\n\n1. **Define the variables:**\n - Let \\( t \\) be the time in hours after 9:00 AM when the trains meet.\n\n2. **Distance covered by Train A:**\n - Train A leaves at 9:00 AM and travels at 80 km/h.\n - Distance covered by Train A: \\( 80t \\) km.\n\n3. **Distance covered by Train B:**\n - Train B leaves at 9:30 AM, so it has been traveling for \\( t - 0.5 \\) hours when they meet.\n - Train B travels at 100 km/h.\n - Distance covered by Train B: \\( 100(t - 0.5) \\) km.\n\n4. **Total distance between stations A and B:**\n - The total distance is 240 km.\n - The sum of the distances covered by both trains should equal 240 km:\n \\[\n 80t + 100(t - 0.5) = 240\n \\]\n\n5. **Solve the equation:**\n \\[\n 80t + 100t - 50 = 240\n \\]\n \\[\n 180t - 50 = 240\n \\]\n \\[\n 180t = 290\n \\]\n \\[\n t = \\frac{290}{180} \\approx 1.6111 \\text{ hours}\n \\]\n\n6. **Convert \\( t \\) to hours and minutes:**\n - \\( 0.6111 \\) hours is approximately 36.6667 minutes.\n - So, \\( t \\approx 1 \\) hour and \\( 36.6667 \\) minutes.\n\n7. **Determine the meeting time:**\n - Since \\( t \\) is the time after 9:00 AM, the meeting time is approximately 9:00 AM + 1 hour and 36.6667 minutes, which is around 10:36 AM and 40 seconds.\n\nSo, the trains meet at approximately **10:36 AM and 40 seconds**.", "ttft_ms": 49.7, "total_time_ms": 8443.97, "prompt_tokens": 62, "completion_tokens": 496, "gen_tokens_per_sec": 59.1, "prompt_tokens_per_sec": 2124.52, "vram_peak_mb": 21715.0, "hallucination": null, "quality_score": 5 }, { "category": "reasoning", "name": "constraint_puzzle", "prompt": "Five houses in a row are painted different colors: red, blue, green, yellow, white. The red house is immediately to the left of the blue house. The green house is somewhere to the right of the white house. The yellow house is not next to the green house. The white house is at one of the ends. What is the order of colors from left to right?", "response": "The order of colors from left to right is: white, red, blue, green, yellow.", "ttft_ms": 95.33, "total_time_ms": 434.05, "prompt_tokens": 79, "completion_tokens": 21, "gen_tokens_per_sec": 61.93, "prompt_tokens_per_sec": 2420.49, "vram_peak_mb": 21715.0, "hallucination": null, "quality_score": 2 }, { "category": "code", "name": "fizzbuzz_variant", "prompt": "Write a Python function `fizzbuzz(n: int) -> list[str]` that returns a list of strings for numbers 1 to n. For multiples of 3 return 'Fizz', multiples of 5 return 'Buzz', multiples of both return 'FizzBuzz', otherwise the number as string. Include type hints and a docstring.", "response": "Here's a clean, typed Python function with a docstring that implements the FizzBuzz problem:\n\n```python\ndef fizzbuzz(n: int) -> list[str]:\n \"\"\"\n Generate a FizzBuzz sequence from 1 to n.\n\n For each number in the range 1 to n:\n - If the number is divisible by both 3 and 5, return 'FizzBuzz'\n - If the number is divisible by 3, return 'Fizz'\n - If the number is divisible by 5, return 'Buzz'\n - Otherwise, return the number as a string\n\n Args:\n n: The upper bound of the sequence (inclusive)\n\n Returns:\n A list of strings representing the FizzBuzz sequence\n\n Examples:\n >>> fizzbuzz(5)\n ['1', '2', 'Fizz', '4', 'Buzz']\n >>> fizzbuzz(15)\n ['1', '2', 'Fizz', '4', 'Buzz', 'Fizz', '7', '8', 'Fizz', 'Buzz', '11', 'Fizz', '13', '14', 'FizzBuzz']\n \"\"\"\n result: list[str] = []\n for i in range(1, n + 1):\n if i % 15 == 0:\n result.append(\"FizzBuzz\")\n elif i % 3 == 0:\n result.append(\"Fizz\")\n elif i % 5 == 0:\n result.append(\"Buzz\")\n else:\n result.append(str(i))\n return result\n```\n\nKey features of this implementation:\n1. Proper type hints (`n: int` and `-> list[str]`)\n2. Comprehensive docstring with description, args, returns, and examples\n3. Clean logic that checks for multiples of 15 first (more efficient than checking 3 and 5 separately)\n4. List comprehension alternative (though the current implementation is more readable for this case)\n5. Proper variable naming and type annotation for the result list\n\nThe function handles all edge cases correctly and returns the expected output for any positive integer n.", "ttft_ms": 40.78, "total_time_ms": 7933.38, "prompt_tokens": 91, "completion_tokens": 463, "gen_tokens_per_sec": 58.69, "prompt_tokens_per_sec": 2622.48, "vram_peak_mb": 21717.0, "hallucination": null, "quality_score": 5 }, { "category": "code", "name": "binary_search", "prompt": "Implement a generic binary search function in Python: `def binary_search(arr: list[int], target: int) -> int` that returns the index of target, or -1 if not found. Handle edge cases. Include type hints and docstring.", "response": "Here's a clean, typed implementation of a generic binary search function in Python with proper type hints and docstring:\n\n```python\ndef binary_search(arr: list[int], target: int) -> int:\n \"\"\"\n Perform binary search on a sorted list of integers to find the target value.\n\n Args:\n arr: A sorted list of integers in ascending order.\n target: The integer value to search for in the list.\n\n Returns:\n The index of the target value if found, otherwise -1.\n\n Raises:\n ValueError: If the input list is not sorted in ascending order.\n \"\"\"\n # Edge case: empty list\n if not arr:\n return -1\n\n # Verify the list is sorted (optional, but good practice)\n if arr != sorted(arr):\n raise ValueError(\"Input list must be sorted in ascending order\")\n\n left, right = 0, len(arr) - 1\n\n while left <= right:\n mid = (left + right) // 2\n\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n\n return -1\n```\n\n### Key Features:\n1. **Type Hints**: Uses `list[int]` for the input array and `int` for both the target and return value.\n2. **Docstring**: Clearly documents the purpose, arguments, return value, and possible exceptions.\n3. **Edge Cases**:\n - Handles empty input list by returning -1 immediately.\n - Optionally verifies the input list is sorted (raises `ValueError` if not).\n4. **Efficiency**: Maintains O(log n) time complexity typical of binary search.\n5. **Clean Implementation**: Uses standard binary search algorithm with clear variable names.\n\n### Example Usage:\n```python\nsorted_list = [1, 3, 5, 7, 9]\nprint(binary_search(sorted_list, 5)) # Output: 2\nprint(binary_search(sorted_list, 2)) # Output: -1\nprint(binary_search([], 1)) # Output: -1\n```", "ttft_ms": 99.37, "total_time_ms": 7956.23, "prompt_tokens": 52, "completion_tokens": 462, "gen_tokens_per_sec": 58.82, "prompt_tokens_per_sec": 1718.04, "vram_peak_mb": 21717.0, "hallucination": null, "quality_score": 4 }, { "category": "code", "name": "bug_detection", "prompt": "Find the bug in this Python code and explain the fix:\n\n```python\ndef merge_sorted(a: list[int], b: list[int]) -> list[int]:\n result = []\n i = j = 0\n while i < len(a) and j < len(b):\n if a[i] <= b[j]:\n result.append(a[i])\n i += 1\n else:\n result.append(b[j])\n j += 1\n return result\n```", "response": "The bug in this code is that it doesn't handle remaining elements in either list after one of the lists is exhausted. The current implementation only merges elements while both lists have elements left, but doesn't append the remaining elements from either list.\n\nHere's the fixed version with an explanation:\n\n```python\ndef merge_sorted(a: list[int], b: list[int]) -> list[int]:\n result = []\n i = j = 0\n while i < len(a) and j < len(b):\n if a[i] <= b[j]:\n result.append(a[i])\n i += 1\n else:\n result.append(b[j])\n j += 1\n\n # Add remaining elements from a\n while i < len(a):\n result.append(a[i])\n i += 1\n\n # Add remaining elements from b\n while j < len(b):\n result.append(b[j])\n j += 1\n\n return result\n```\n\nThe fix adds two additional while loops that handle the remaining elements:\n1. After the main loop, if there are any elements left in list `a`, they are all appended to the result (since they're already sorted and larger than all elements in `b` that we've processed)\n2. Similarly, if there are any elements left in list `b`, they are all appended to the result\n\nThis ensures that all elements from both input lists are included in the final merged result, maintaining the sorted order.\n\nFor example, with the original code:\n```python\nmerge_sorted([1, 3, 5], [2, 4, 6])\n```\nwould return `[1, 2, 3, 4]` (missing the 5 and 6), while the fixed version correctly returns `[1, 2, 3, 4, 5, 6]`.", "ttft_ms": 53.93, "total_time_ms": 6617.71, "prompt_tokens": 116, "completion_tokens": 384, "gen_tokens_per_sec": 58.51, "prompt_tokens_per_sec": 2739.79, "vram_peak_mb": 21721.0, "hallucination": null, "quality_score": 5 }, { "category": "tool_use", "name": "json_extraction", "prompt": "Extract structured data from this text:\n\n\"John Smith, age 34, works at Acme Corp as a Senior Engineer since March 2021. His email is john.smith@acme.com and he manages a team of 5 people.\"\n\nReturn JSON with keys: name, age, company, title, start_date, email, team_size", "response": "```json\n{\n \"name\": \"John Smith\",\n \"age\": 34,\n \"company\": \"Acme Corp\",\n \"title\": \"Senior Engineer\",\n \"start_date\": \"March 2021\",\n \"email\": \"john.smith@acme.com\",\n \"team_size\": 5\n}\n```", "ttft_ms": 48.62, "total_time_ms": 1318.93, "prompt_tokens": 94, "completion_tokens": 75, "gen_tokens_per_sec": 59.02, "prompt_tokens_per_sec": 2668.1, "vram_peak_mb": 21721.0, "hallucination": null, "quality_score": 4 }, { "category": "tool_use", "name": "function_calling", "prompt": "What's the weather like in Tokyo?", "response": "```json\n{\"function\": \"get_weather\", \"arguments\": {\"location\": \"Tokyo\", \"unit\": \"celsius\"}}\n```", "ttft_ms": 49.27, "total_time_ms": 549.73, "prompt_tokens": 72, "completion_tokens": 30, "gen_tokens_per_sec": 59.94, "prompt_tokens_per_sec": 2282.31, "vram_peak_mb": 21721.0, "hallucination": null, "quality_score": 4 }, { "category": "writing", "name": "summarization", "prompt": "Summarize the following in exactly 3 bullet points:\n\nThe transformer architecture, introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al., revolutionized natural language processing. It replaced recurrent neural networks with self-attention mechanisms, allowing parallel processing of sequences. The key innovation was the multi-head attention mechanism that lets the model focus on different parts of the input simultaneously. Transformers enabled models like BERT, GPT, and T5 that achieved state-of-the-art results across NLP tasks. The architecture's scalability led to the development of large language models with billions of parameters.", "response": "- Introduced self-attention mechanisms to replace RNNs, enabling parallel sequence processing.\n- Key innovation: multi-head attention for simultaneous focus on different input parts.\n- Led to state-of-the-art NLP models (e.g., BERT, GPT) and scalable large language models.", "ttft_ms": 107.81, "total_time_ms": 1123.85, "prompt_tokens": 142, "completion_tokens": 60, "gen_tokens_per_sec": 59.05, "prompt_tokens_per_sec": 2758.46, "vram_peak_mb": 21721.0, "hallucination": null, "quality_score": 5 }, { "category": "writing", "name": "technical_explanation", "prompt": "Explain what a Docker container is to someone who has never used one. Use an analogy. Keep it under 150 words.", "response": "Think of a Docker container like a **portable lunchbox**. Inside, you pack everything you need—your sandwich (app code), drink (dependencies), and utensils (libraries)—so you can eat it anywhere, whether at home or school.\n\nA container is a lightweight, isolated package that holds your application and all its dependencies, ensuring it runs the same way on any machine with Docker. Just like your lunchbox keeps your food separate from others', a container keeps your app separate from the host system and other containers.\n\nNo need to worry about mixing ingredients (conflicts) or missing items (dependencies). It’s self-contained, portable, and consistent.", "ttft_ms": 53.31, "total_time_ms": 2396.34, "prompt_tokens": 29, "completion_tokens": 137, "gen_tokens_per_sec": 58.46, "prompt_tokens_per_sec": 1092.73, "vram_peak_mb": 21721.0, "hallucination": null, "quality_score": 5 } ], "load_ms": 17047.31, "summary": { "load_ms": 17047.31, "avg_ttft_ms": 58.91, "avg_gen_tokens_per_sec": 59.37, "total_facts_checked": 22, "total_facts_hallucinated": 0, "hallucination_rate": 0.0 } }