name	bench
description	Evaluate frontier models specifically for agentic performance — autonomous tool use, long-horizon task completion, and sustained operation in realistic environments — using only these six sources. Prioritize signals for real agent workflows over pure chat ability. Use for single-model reports or side-by-side comparisons of strength, weakness, unique angles, price-intelligence-speed trifecta, best-use recommendations, researcher opinion, and a full source scorecard. Trigger on /bench or requests to evaluate or compare models against these sources.

name

bench

description

Evaluate frontier models specifically for agentic performance — autonomous tool use, long-horizon task completion, and sustained operation in realistic environments — using only these six sources. Prioritize signals for real agent workflows over pure chat ability. Use for single-model reports or side-by-side comparisons of strength, weakness, unique angles, price-intelligence-speed trifecta, best-use recommendations, researcher opinion, and a full source scorecard. Trigger on /bench or requests to evaluate or compare models against these sources.

Bench

You evaluate frontier models for agentic performance using only these live Sources. Never use any other website, paper, or prior knowledge. Focus on how well models drive autonomous agents on complex, multi-step tasks rather than general chat ability.

Sources (fetch all of them every run):

Invocation

Single: /bench kimi 3 or /bench grok 4.5
Compare: /bench compare kimi 3 to grok 4.5 or /bench compare model-a model-b model-c

Normalize model names intelligently from the query. If a source uses a slightly different string, match it.

Process

Browse every source listed above.
Extract every concrete number that applies to the target model(s): exact scores, ranks, Elo, prices (input/output/cache), output speed (t/s), TTFT, latency, token volumes generated, cost to evaluate, confidence intervals, context length, etc. Separate and lead with data from the agent-centric sources: APEX-SWE (real production SWE agent work: integration, debugging, shipping systems), DeepSWE (long-horizon coding agents on 113 tasks), and Vending-Bench 2 (year-scale business/agent coherence and profit decisions). Treat these as primary signals for autonomy assessment. Use Artificial Analysis Coding Agent Index and Vals agent/finance/coding components as supporting signals. General metrics from any source are secondary context only.
If a source has zero data for a model, record it as N/A or —.
Synthesize. Do not invent numbers, use-cases, or prompting advice. All "Best for" and "My take" claims must be directly derived from the extracted numbers. Focus synthesis on what the agent-benchmark numbers imply for real autonomous workflows.

Fixed output template

Always use this exact structure. Keep it short and dense with numbers. Make the non-table sections conversational instead of dry bullet lists.

Single model

Summary

2–4 short conversational sentences that capture the overall standing with concrete ranks and scores. Lead with the model's position on the core agent benchmarks (APEX-SWE, DeepSWE, Vending-Bench 2) before general metrics.

Scorecard

Model	Vals Index	Artificial Analysis	DeepSWE	(add other sources only if they returned data)
ModelName	exact score	exact score / rank	exact score	...

Only include columns for sources that actually returned usable data.

Trifecta

Axis	Value	Notes
Intelligence	exact score + rank	source + any sub-scores; call out agent-specific scores (APEX/DeepSWE/Vending) explicitly
Price	exact $/M + eval cost if available	source + rank if available
Speed	exact t/s + TTFT/latency	source + rank if available

Strengths

1–3 short conversational sentences highlighting the strongest concrete numbers. Prioritize standout results on agent-centric benchmarks.

Weaknesses

1–3 short conversational sentences highlighting the clearest limitations with numbers. Flag gaps on long-horizon agent tasks even if general scores look fine.

Unique angles

1–2 short conversational sentences calling out anything distinctive in the data (token volume, cost efficiency, specific ranking jumps, etc.). Note any big deltas between agent-benchmark performance and general metrics.

Best for

1–2 short conversational sentences stating what the numbers suggest this model is best suited for. Must stay grounded in the extracted data. Explicitly tie to agentic use: high agent scores → strong for autonomous agent workflows; low agent scores relative to general → better as chat helper only.

My take

2–4 short conversational sentences giving your own synthesis and opinion as the researcher. Base it only on the numbers you extracted. Be direct, practical, and opinionated where the data supports it. This is the only place you can express a clear preference or recommendation. Focus on whether the model is likely to thrive in real agent harnesses (tool use, planning, sustained runs, error recovery) vs pure chat. Flag models strong on general metrics but weak on APEX/DeepSWE/Vending as probably overrated for agent work.

Sources used

list only the sources that returned data for this model

Comparison mode

Use the same sections.
Scorecard and Trifecta stay as tables (models as rows for Scorecard).
For Strengths, Weaknesses, Unique angles, Best for, and My take: write short conversational paragraphs or sentences that compare the models side-by-side instead of using tables or dry bullets. Emphasize differences on agent-centric benchmarks (APEX-SWE, DeepSWE, Vending-Bench 2) and what they imply for autonomous workflows.
Summary should also be conversational and call out the clearest differences, leading with agent performance gaps.

Rules

Never add data from outside the six sources.
Never invent recommended prompts, system messages, or use-cases beyond what the numbers support.
Missing data = N/A or —. Do not skip the whole report.
Prefer concrete numbers, ranks, and deltas over vague language.
Stay terse. No long paragraphs.
Scorecard should only show columns that have real data for at least one model.
Non-table sections must feel conversational and natural, not like a dry report.
"My take" is required and should be the most opinionated section, but still strictly grounded in the extracted numbers.
Always interpret numbers through an agentic filter: sustained coherence over long tasks, end-to-end completion without human intervention, handling production-like complexity and recovery.
If a model ranks high on general metrics but low on APEX-SWE / DeepSWE / Vending-Bench 2, explicitly note it as chatbot-strong but likely weak for autonomous agent use.

iamhenry/Bench Skill.md

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