{ "results": [ { "model": "muse-glimmer:17gb", "tasks": { "strict_json": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:04.188148493Z", "message": { "role": "assistant", "content": "{\"status\":\"ok\",\"count\":3,\"items\":[\"ruby\",\"sql\",\"go\"]}", "thinking": "Return exactly this JSON object with no markdown or commentary: {\"status\":\"ok\",\"count\":3,\"items\":[\"ruby\",\"sql\",\"go\"]}\n\nWe need return exactly this JSON object with no markdown or commentary. So output the JSON exactly.\n\nProbably just print that string. No markdown. Ensure no extra newline? Probably okay.\n\nReturn exactly this JSON object. So output:\n\n{\"status\":\"ok\",\"count\":3,\"items\":[\"ruby\",\"sql\",\"go\"]}\n\nNo code fences." }, "done": true, "done_reason": "stop", "total_duration": 3402690009, "load_duration": 193540886, "prompt_eval_count": 61, "prompt_eval_duration": 118846000, "eval_count": 123, "eval_duration": 3077603000, "wall_seconds": 3.403100108029321, "decode_tps": 39.96616847592104, "prompt_tps": 513.2692728404827, "score": 1 }, "math": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:11.795539925Z", "message": { "role": "assistant", "content": "Original price = $80\n\n1. Markup 20%\n$80 \u00d7 1.20 = $96.00\n\n2. Discount 25% on marked price\n$96 \u00d7 (1 - 0.25) = $96 \u00d7 0.75 = $72.00\n\n3. Sales tax 10% on discounted price\n$72 \u00d7 1.10 = $79.20\n\nFinal price \u2248 **$79.20**, i.e. about $0.80 less than the original $80.", "thinking": "An $80 item is marked up 20%, then discounted 25%, then 10% sales tax is added. What is the final price? Show concise working.\n\nAn $80 item is marked up 20%, then discounted 25%, then 10% sales tax is added.\n\nStart 80\nMarkup 20%: 80 *1.20 = 96\nDiscount 25%: 96 *0.75 = 72\nSales tax 10%: 72 *1.10 = 79.2\n\nFinal price $79.20\n\nIf tax added after discount? Yes.\n\nConcise working.\n\nPossibly order matters. Could be 80*1.2=96, *0.75=72, *1.1=79.2\n\nAnswer $79.20\n\nIf want show steps.\n\nDone.\n\n" }, "done": true, "done_reason": "stop", "total_duration": 7606959056, "load_duration": 187709815, "prompt_eval_count": 67, "prompt_eval_duration": 124193000, "eval_count": 289, "eval_duration": 7285601000, "wall_seconds": 7.6073440670152195, "decode_tps": 39.66728345403489, "prompt_tps": 539.4829016128123, "score": 1 }, "ruby_code": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:23.365466332Z", "message": { "role": "assistant", "content": "```ruby\ndef merge_intervals(intervals)\n return [] if intervals.nil? || intervals.empty?\n\n sorted = intervals.map { |a, b| [a, b] }.sort_by(&:first)\n merged = []\n\n sorted.each do |start, finish|\n if merged.empty? || start > merged.last[1] + 1\n merged << [start, finish]\n else\n merged.last[1] = [merged.last[1], finish].max\n end\n end\n\n merged\nend\n```", "thinking": "Write a Ruby method merge_intervals(intervals) that merges overlapping and touching integer intervals. Input may be unsorted. Return only Ruby code.\n\nWe need return only Ruby code. Probably define method.\n\nMerge overlapping and touching integer intervals. Touching means [1,2] and [3,4]? Touching? Usually touching = end == start? Or end+1? Integer intervals: touching maybe [1,2] and [3,4] are touching? Might be 2 and 3 adjacent. Could interpret touching as end >= start? Let's assume overlapping or touching means end >= next_start? If intervals inclusive. Typically merge if start <= current_end +1? Hmm.\n\nProblem says overlapping and touching integer intervals. Touching integer intervals: [1,2] and [3,3] touch? 2+1=3. Might want merge. Let's assume touching means current_end >= next_start -1? Or next_start <= current_end +? Safer to merge if start <= current_end +1? Many definitions.\n\nI'll implement merge if start <= current_end + 1? Actually overlapping: start <= current_end. Touching: start == current_end +1. So condition start <= current_end +1.\n\nLet's do that.\n\nReturn merged intervals as array of [start,end].\n\nProvide method.\n\nOnly Ruby code. No explanation.\n\nProbably:\n\ndef merge_intervals(intervals)\n return [] if intervals.empty?\n sorted = intervals.sort_by(&:first)\n merged = [sorted.first]\n ...\nend\n\nReturn only code.\n\nEnsure output format.\n\nLet's produce final." }, "done": true, "done_reason": "stop", "total_duration": 11569421193, "load_duration": 178781452, "prompt_eval_count": 62, "prompt_eval_duration": 122985000, "eval_count": 446, "eval_duration": 11255307000, "wall_seconds": 11.569926408003084, "decode_tps": 39.62575165652967, "prompt_tps": 504.1265194942473, "grader": { "code": "def merge_intervals(intervals)\n return [] if intervals.nil? || intervals.empty?\n\n sorted = intervals.map { |a, b| [a, b] }.sort_by(&:first)\n merged = []\n\n sorted.each do |start, finish|\n if merged.empty? || start > merged.last[1] + 1\n merged << [start, finish]\n else\n merged.last[1] = [merged.last[1], finish].max\n end\n end\n\n merged\nend", "stdout": "", "stderr": "-:19:in '
': c (RuntimeError)\n", "exit": 1 }, "score": 0 }, "sqlite": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:35.410392921Z", "message": { "role": "assistant", "content": "```sql\nSELECT id, assignee\nFROM (\n SELECT id,\n assignee,\n ROW_NUMBER() OVER (\n PARTITION BY assignee\n ORDER BY created_at ASC, id ASC\n ) AS rn\n FROM tickets\n WHERE status = 'open'\n)\nWHERE rn = 1;\n```", "thinking": "Given SQLite table tickets(id INTEGER, assignee TEXT, status TEXT, created_at TEXT), return the id and assignee of the oldest open ticket per assignee. Break equal created_at ties by lowest id. Exclude assignees with no open ticket. Return only executable SQLite SQL.\n\nWe need oldest open ticket per assignee. Open ticket? status? Probably status = 'open' ? Could be not closed? Assume status = 'open'. Might be status != 'closed'? Usually open means status = 'open'. We'll assume status = 'open'.\n\nNeed id and assignee of oldest open ticket per assignee. Break ties by lowest id.\n\nReturn only executable SQLite SQL.\n\nWe can use ROW_NUMBER() window function. SQLite supports window functions from 3.25.\n\nQuery:\nSELECT id, assignee FROM (\n SELECT id, assignee, created_at,\n ROW_NUMBER() OVER (PARTITION BY assignee ORDER BY created_at ASC, id ASC) as rn\n FROM tickets\n WHERE status = 'open'\n) WHERE rn =1;\n\nIf open means status != 'closed'? Could be status = 'open'. Might need status = 'open' or status <> 'closed'? Probably open.\n\nAlternative using GROUP BY with MIN(created_at). But tie break by lowest id needs more.\n\nCould use correlated subquery.\n\nReturn only executable SQL.\n\nProbably want:\n\nSELECT t.id, t.assignee\nFROM tickets t\nWHERE t.status = 'open'\n AND t.created_at = (\n SELECT MIN(created_at) FROM tickets WHERE assignee = t.assignee AND status='open'\n )\n...\n\nBut tie break.\n\nBetter use window.\n\nProvide final SQL.\n\nIf they want exclude assignees with no open ticket, already filtered.\n\nReturn only executable SQLite SQL.\n\nOutput query.\n\nMaybe also allow status IN ('open','in_progress')? Hmm.\n\nAssume status = 'open'.\n\nProvide code block.\n\nLet's output just SQL.\n\n" }, "done": true, "done_reason": "stop", "total_duration": 12019522143, "load_duration": 189977212, "prompt_eval_count": 90, "prompt_eval_duration": 125037000, "eval_count": 462, "eval_duration": 11688343000, "wall_seconds": 12.020085387979634, "decode_tps": 39.52656077940218, "prompt_tps": 719.7869430648527, "grader": { "sql": "SELECT id, assignee\nFROM (\n SELECT id,\n assignee,\n ROW_NUMBER() OVER (\n PARTITION BY assignee\n ORDER BY created_at ASC, id ASC\n ) AS rn\n FROM tickets\n WHERE status = 'open'\n)\nWHERE rn = 1;", "rows": [ [ 2, "amy" ], [ 4, "bob" ] ] }, "score": 1 }, "constrained_summary": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:43.538118946Z", "message": { "role": "assistant", "content": "- Release moved to Friday\n- Median latency fell from 220ms to 140ms\n- Rollback requires the feature flag named fast_path", "thinking": "Summarize these facts in exactly 3 bullet points, at most 8 words per bullet. Include every fact: The release moved to Friday. Median latency fell from 220ms to 140ms. Rollback requires the feature flag named fast_path.\n\nNeed exactly 3 bullet points, at most 8 words per bullet. Include every fact.\n\nFacts:\n1. The release moved to Friday.\n2. Median latency fell from 220ms to 140ms.\n3. Rollback requires the feature flag named fast_path.\n\nNeed 3 bullet points. Each <=8 words.\n\nBullet 1: Release moved to Friday. That's 4 words? Release(1) moved2 to3 Friday4. Good.\n\nBullet 2: Median latency fell from 220ms to 140ms. Count words: Median1 latency2 fell3 from4 220ms5 to6 140ms7. Good <=8.\n\nBullet 3: Rollback requires the feature flag named fast_path. Count: Rollback1 requires2 the3 feature4 flag5 named6 fast_path7. Good.\n\nExactly 3 bullet points. Use bullet format like - or \u2022\n\nEnsure include every fact. Yes.\n\nOutput exactly 3 bullet points. Probably each on new line.\n\nMake sure at most 8 words per bullet. OK.\n\nReturn summary.\n\n" }, "done": true, "done_reason": "stop", "total_duration": 8126605079, "load_duration": 185574035, "prompt_eval_count": 84, "prompt_eval_duration": 123220000, "eval_count": 309, "eval_duration": 7800516000, "wall_seconds": 8.127081399026792, "decode_tps": 39.61276407868402, "prompt_tps": 681.7075150137965, "score": 1 }, "tool_call": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:46.088390739Z", "message": { "role": "assistant", "content": "", "thinking": "Use the primary lookup tool to find order 731. Do not guess.\n\nWe need to call primary_order_lookup with order_id 731.", "tool_calls": [ { "id": "vk7aBn1TfQDugvUhDtPkzrJDzN0lMXuZ", "function": { "index": 0, "name": "primary_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 2549724751, "load_duration": 195365325, "prompt_eval_count": 460, "prompt_eval_duration": 319853000, "eval_count": 81, "eval_duration": 2017605000, "wall_seconds": 2.550199406046886, "decode_tps": 40.1466094701391, "prompt_tps": 1438.160655050914, "score": 1 }, "tool_recovery": { "first": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:48.372525922Z", "message": { "role": "assistant", "content": "", "thinking": "Use the primary lookup tool to find order 731. Do not guess.\n\nWe need to call primary_order_lookup with order_id 731.", "tool_calls": [ { "id": "GFNcCJJxA2OoBfiDzhWvN4s6dJPy9OaH", "function": { "index": 0, "name": "primary_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 2283474922, "load_duration": 187079748, "prompt_eval_count": 460, "prompt_eval_duration": 71782000, "eval_count": 81, "eval_duration": 2016591000, "wall_seconds": 2.2839669510140084, "decode_tps": 40.16679634095362, "prompt_tps": 6408.2917723106075 }, "second": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:52.405262218Z", "message": { "role": "assistant", "content": "", "thinking": "Use the primary lookup tool to find order 731. Do not guess.\n\nWe need use primary lookup tool to find order 731. Do not guess. Primary failed. Error says primary database unavailable. Use backup lookup.\n\nWe should follow instruction: Use primary lookup tool... but it failed. Probably need to use backup. The user said do not guess. We can try backup_order_lookup.\n\nPolicy: we must use tools. Primary failed, so use backup.", "tool_calls": [ { "id": "jEtL9vEp6Q5t6rPtKjMAKLrgxe6YriD0", "function": { "index": 0, "name": "backup_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 4032309707, "load_duration": 189841086, "prompt_eval_count": 540, "prompt_eval_duration": 173968000, "eval_count": 145, "eval_duration": 3657274000, "wall_seconds": 4.032809063966852, "decode_tps": 39.64701578279341, "prompt_tps": 3104.019129954934 }, "score": 1 }, "long_context_needle": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:58.324133401Z", "message": { "role": "assistant", "content": "MUSE-7429-KOALA", "thinking": "BEGIN RECORDS ... record2600=MUSE-7429-KOALA ... What is exact value of record2600? Reply with only the value.\n\nWe need output only value. The value is MUSE-7429-KOALA\n\nProbably just that string.\n\nReply with only the value.\n\n" }, "done": true, "done_reason": "stop", "total_duration": 5918072030, "load_duration": 182145630, "prompt_eval_count": 4067, "prompt_eval_duration": 3651157000, "eval_count": 78, "eval_duration": 2039128000, "wall_seconds": 5.918613819987513, "decode_tps": 38.25164482072729, "prompt_tps": 1113.8934863661025, "score": 1 } }, "cold_probe": { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:04:43.672292659Z", "message": { "role": "assistant", "content": "OK", "thinking": "Reply exactly: OK\n\nWe need reply exactly: OK\n\nProbably just output OK. No extra whitespace? Exactly: OK\n\nShould we include newline? Probably just OK.\n\nFollow instruction." }, "done": true, "done_reason": "stop", "total_duration": 8126143684, "load_duration": 6718828391, "prompt_eval_count": 37, "prompt_eval_duration": 144087000, "eval_count": 48, "eval_duration": 1253920000, "wall_seconds": 8.126701364002656, "decode_tps": 38.279954064055126, "prompt_tps": 256.78930090847894 }, "warm_speed": [ { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:04:52.42092383Z", "message": { "role": "assistant", "content": "", "thinking": "In exactly 100 words, explain why database indexes speed up reads but can slow writes.\n\nNeed exactly 100 words. Count words.\n\nWe need explain why indexes speed up reads but can slow writes. Exactly 100 words.\n\nLet's craft ~100 words then count.\n\nDraft:\n\n\"Database indexes act like a book's index, mapping column values to row locations. Instead of scanning every row, the engine follows the index tree to find matching rows in logarithmic time. This makes lookups, joins and filters dramatically faster for read-heavy workloads.\n\nWrites suffer because the index must stay consistent. On insert, update or delete, the database modifies not only the table but also every relevant index structure, adding or removing entries and rebalancing B-trees. These extra writes increase I/O, CPU and lock contention. Therefore indexes trade write overhead for faster reads.\"\n\nNow count words.\n\nDatabase(1) indexes2 act3 like4 a5 book's6 index,7 mapping8 column9 values10 to11 row12 locations.13 Instead14 of15 scanning16 every17 row,18 the19 engine20 follows21 the22 index23 tree24 to25 find26 matching27 rows28 in29 logarithmic30 time.31 This32 makes33 lookups,34 joins35 and36 filters37 dramatically38 faster39 for40 read-heavy41 workloads.42\n\nWrites43 suffer44 because45 the46 index47 must48 stay49 consistent.50 On51 insert,52 update53 or54 delete,55 the56 database57 modifies58 not59 only60 the61 table62 but63 also" }, "done": true, "done_reason": "length", "total_duration": 8747995762, "load_duration": 250246002, "prompt_eval_count": 51, "prompt_eval_duration": 145383000, "eval_count": 320, "eval_duration": 8334549999, "wall_seconds": 8.74860664102016, "decode_tps": 38.394394423021566, "prompt_tps": 350.79754854419014 }, { "model": "muse-glimmer:17gb", "created_at": "2026-08-10T12:05:00.784987023Z", "message": { "role": "assistant", "content": "", "thinking": "In exactly 100 words, explain why database indexes speed up reads but can slow writes.\n\nNeed exactly 100 words. Count words.\n\nLet's craft 100 words.\n\nWe need explain why indexes speed up reads but can slow writes.\n\nCount words carefully.\n\nDraft:\n\n\"Database indexes act like a book's index, mapping values to physical locations. Instead of scanning every row, the engine follows the index tree to find matching records in logarithmic time. This makes lookups, joins and filters dramatically faster for read-heavy workloads.\n\nWrites suffer because the index must stay consistent. On insert, update or delete, the database modifies not only the table but also every relevant index structure. That means extra disk writes, page splits and lock contention. Maintaining sorted order and balance adds CPU and I/O overhead.\n\nThus indexes trade write performance for faster reads.\"\n\nNow count words.\n\nDatabase(1) indexes2 act3 like4 a5 book's6 index,7 mapping8 values9 to10 physical11 locations.12 Instead13 of14 scanning15 every16 row,17 the18 engine19 follows20 the21 index22 tree23 to24 find25 matching26 records27 in28 logarithmic29 time.30 This31 makes32 lookups,33 joins34 and35 filters36 dramatically37 faster38 for39 read-heavy40 workloads.41\n\nWrites42 suffer43 because44 the45 index46 must47 stay48 consistent.49 On50 insert,51 update52 or53 delete,54 the55 database56 modifies57 not58 only59 the60 table61 but62 also63 every64 relevant" }, "done": true, "done_reason": "length", "total_duration": 8363408251, "load_duration": 188822770, "prompt_eval_count": 51, "prompt_eval_duration": 62021000, "eval_count": 320, "eval_duration": 8099824000, "wall_seconds": 8.363896001013927, "decode_tps": 39.50703126388919, "prompt_tps": 822.3021234743071 } ] }, { "model": "gemma4-26b-agentbench:latest", "tasks": { "strict_json": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:34.289803441Z", "message": { "role": "assistant", "content": "{\"status\":\"ok\",\"count\":3,\"items\":[\"ruby\",\"sql\",\"go\"]}" }, "done": true, "done_reason": "stop", "total_duration": 6736461537, "load_duration": 245628131, "prompt_eval_count": 41, "prompt_eval_duration": 6335842000, "eval_count": 18, "eval_duration": 140228000, "wall_seconds": 6.737056283047423, "decode_tps": 128.36238126479734, "prompt_tps": 6.471120965453368, "score": 1 }, "math": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:35.407840567Z", "message": { "role": "assistant", "content": "1. **Markup (20%):** \n $80 \\times 1.20 = \\$96$\n\n2. **Discount (25%):** \n $96 \\times 0.75 = \\$72$\n\n3. **Sales Tax (10%):** \n $72 \\times 1.10 = \\$79.20$\n\n**Final Price: $79.20**" }, "done": true, "done_reason": "stop", "total_duration": 1117372243, "load_duration": 264919261, "prompt_eval_count": 51, "prompt_eval_duration": 62081000, "eval_count": 103, "eval_duration": 783714000, "wall_seconds": 1.1178428379935212, "decode_tps": 131.42549450437278, "prompt_tps": 821.5073855124757, "score": 1 }, "ruby_code": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:37.189976764Z", "message": { "role": "assistant", "content": "```ruby\ndef merge_intervals(intervals)\n return [] if intervals.empty?\n\n # Sort intervals by their start value\n sorted = intervals.sort_by { |interval| interval[0] }\n \n merged = [sorted[0]]\n\n sorted[1..-1].each do |current|\n last_merged = merged.last\n\n # Check if current interval overlaps or touches the last merged interval\n # (last_merged[1] >= current[0] handles both overlapping and touching)\n if last_merged[1] >= current[0]\n # Update the end of the last interval to the maximum end value seen so far\n last_merged[1] = [last_merged[1], current[1]].max\n else\n merged << current\n end\n end\n\n merged\nend\n```" }, "done": true, "done_reason": "stop", "total_duration": 1778168136, "load_duration": 249123703, "prompt_eval_count": 42, "prompt_eval_duration": 59133000, "eval_count": 190, "eval_duration": 1456512000, "wall_seconds": 1.7786930670263246, "decode_tps": 130.44863344757886, "prompt_tps": 710.2633047536908, "grader": { "code": "def merge_intervals(intervals)\n return [] if intervals.empty?\n\n # Sort intervals by their start value\n sorted = intervals.sort_by { |interval| interval[0] }\n \n merged = [sorted[0]]\n\n sorted[1..-1].each do |current|\n last_merged = merged.last\n\n # Check if current interval overlaps or touches the last merged interval\n # (last_merged[1] >= current[0] handles both overlapping and touching)\n if last_merged[1] >= current[0]\n # Update the end of the last interval to the maximum end value seen so far\n last_merged[1] = [last_merged[1], current[1]].max\n else\n merged << current\n end\n end\n\n merged\nend", "stdout": "PASS\n", "stderr": "", "exit": 0 }, "score": 1 }, "sqlite": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:43.815558525Z", "message": { "role": "assistant", "content": "```sql\nSELECT id, assignee\nFROM (\n SELECT \n id, \n assignee,\n ROW_NUMBER() OVER (\n PARTITION BY assignee \n ORDER BY created_at ASC, id ASC\n ) as rank\n FROM tickets\n WHERE status != 'closed' -- Assuming 'open' is defined by not being closed; adjust if status is explicitly 'open'\n AND status = 'open' -- Based on prompt \"open ticket\"\n) t\nWHERE rank = 1;\n```" }, "done": true, "done_reason": "stop", "total_duration": 6582004403, "load_duration": 263075990, "prompt_eval_count": 70, "prompt_eval_duration": 5376315000, "eval_count": 117, "eval_duration": 924800000, "wall_seconds": 6.5825705580064096, "decode_tps": 126.51384083044984, "prompt_tps": 13.020070438581072, "grader": { "sql": "SELECT id, assignee\nFROM (\n SELECT \n id, \n assignee,\n ROW_NUMBER() OVER (\n PARTITION BY assignee \n ORDER BY created_at ASC, id ASC\n ) as rank\n FROM tickets\n WHERE status != 'closed' -- Assuming 'open' is defined by not being closed; adjust if status is explicitly 'open'\n AND status = 'open' -- Based on prompt \"open ticket\"\n) t\nWHERE rank = 1;", "rows": [ [ 2, "amy" ], [ 4, "bob" ] ] }, "score": 1 }, "constrained_summary": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:44.39837632Z", "message": { "role": "assistant", "content": "* Release moved to Friday.\n* Median latency dropped to 140ms.\n* Rollback uses fast_path feature flag." }, "done": true, "done_reason": "stop", "total_duration": 577084659, "load_duration": 254717375, "prompt_eval_count": 69, "prompt_eval_duration": 80850000, "eval_count": 30, "eval_duration": 225137000, "wall_seconds": 0.5777305060182698, "decode_tps": 133.2521975508246, "prompt_tps": 853.4322820037105, "score": 1 }, "tool_call": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:44.881998627Z", "message": { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ujfxvgu9", "function": { "index": 0, "name": "primary_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 483132059, "load_duration": 243485665, "prompt_eval_count": 134, "prompt_eval_duration": 86024000, "eval_count": 19, "eval_duration": 141704000, "wall_seconds": 0.4835097889881581, "decode_tps": 134.08231242590188, "prompt_tps": 1557.7048265600297, "score": 1 }, "tool_recovery": { "first": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:45.324775163Z", "message": { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_o2pi1d93", "function": { "index": 0, "name": "primary_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 442343272, "load_duration": 259208867, "prompt_eval_count": 134, "prompt_eval_duration": 40211000, "eval_count": 19, "eval_duration": 141421000, "wall_seconds": 0.44271271501202136, "decode_tps": 134.35062685174054, "prompt_tps": 3332.421476710353 }, "second": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:45.840017428Z", "message": { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_or8f7yfq", "function": { "index": 0, "name": "backup_order_lookup", "arguments": { "order_id": 731 } } } ] }, "done": true, "done_reason": "stop", "total_duration": 514819106, "load_duration": 267911738, "prompt_eval_count": 174, "prompt_eval_duration": 74704000, "eval_count": 23, "eval_duration": 170194000, "wall_seconds": 0.5152681600302458, "decode_tps": 135.13989917388392, "prompt_tps": 2329.192546583851 }, "score": 1 }, "long_context_needle": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:53.558721713Z", "message": { "role": "assistant", "content": "MUSE-7429-KOALA" }, "done": true, "done_reason": "stop", "total_duration": 7718006705, "load_duration": 261733575, "prompt_eval_count": 5832, "prompt_eval_duration": 7324846000, "eval_count": 11, "eval_duration": 85521000, "wall_seconds": 7.718460411997512, "decode_tps": 128.62337905309806, "prompt_tps": 796.1942135029187, "score": 1 } }, "cold_probe": { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:25.153858107Z", "message": { "role": "assistant", "content": "OK" }, "done": true, "done_reason": "stop", "total_duration": 5538525355, "load_duration": 5476412443, "prompt_eval_count": 17, "prompt_eval_duration": 49740000, "eval_count": 2, "eval_duration": 11066000, "wall_seconds": 5.539323816017713, "decode_tps": 180.7337791433219, "prompt_tps": 341.7772416566144 }, "warm_speed": [ { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:26.371584391Z", "message": { "role": "assistant", "content": "Database indexes function like a book\u2019s index, allowing the engine to locate specific rows without scanning every single page. By creating a sorted pointer structure, the system bypasses costly full-table scans, drastically accelerating read queries and data retrieval speeds. However, this efficiency comes at a cost during write operations. Every time a row is inserted, updated, or deleted, the database must also modify all associated indexes to maintain structural integrity and correct sorting. This extra computational overhead and additional disk I/O required for index maintenance create latency, meaning more frequent writes can significantly degrade overall database performance." }, "done": true, "done_reason": "stop", "total_duration": 1216650897, "load_duration": 245835691, "prompt_eval_count": 33, "prompt_eval_duration": 59299000, "eval_count": 118, "eval_duration": 906693000, "wall_seconds": 1.2210397819872014, "decode_tps": 130.1432789268253, "prompt_tps": 556.5017959830689 }, { "model": "gemma4-26b-agentbench:latest", "created_at": "2026-08-10T12:06:27.552672265Z", "message": { "role": "assistant", "content": "Database indexes function like a book\u2019s index, allowing the engine to locate specific data without scanning every row. By providing a sorted pointer to data locations, indexes significantly accelerate read queries and search operations, reducing disk I/O. However, this efficiency comes at a cost during write operations. Every time a record is inserted, updated, or deleted, the database must also modify all associated indexes to maintain structural integrity and correct sorting. This extra computational overhead and additional disk writes create latency. Consequently, while indexes optimize retrieval speed, they inherently introduce performance penalties during data modification processes." }, "done": true, "done_reason": "stop", "total_duration": 1176498538, "load_duration": 241805601, "prompt_eval_count": 33, "prompt_eval_duration": 27928000, "eval_count": 116, "eval_duration": 893635000, "wall_seconds": 1.1771876790444367, "decode_tps": 129.8069122180756, "prompt_tps": 1181.6098539100544 } ] } ], "summary": [ { "model": "muse-glimmer:17gb", "score": 7, "max": 8, "scores": { "strict_json": 1, "math": 1, "ruby_code": 0, "sqlite": 1, "constrained_summary": 1, "tool_call": 1, "tool_recovery": 1, "long_context_needle": 1 }, "cold_wall_s": 8.13, "cold_load_s": 6.72, "warm_decode_tps": [ 38.39, 39.51 ], "warm_wall_s": [ 8.75, 8.36 ] }, { "model": "gemma4-26b-agentbench:latest", "score": 8, "max": 8, "scores": { "strict_json": 1, "math": 1, "ruby_code": 1, "sqlite": 1, "constrained_summary": 1, "tool_call": 1, "tool_recovery": 1, "long_context_needle": 1 }, "cold_wall_s": 5.54, "cold_load_s": 5.48, "warm_decode_tps": [ 130.14, 129.81 ], "warm_wall_s": [ 1.22, 1.18 ] } ] }