Run Any Model
We used Run Any Model to research its own market

Questions, comparisons and evidence

These tables come from saved answers collected on October 5–6, 2026. No models were asked again to clean up the names or recalculate the comparisons.

Download the analysis data (JSON), including response IDs, question text, each response's extracted product IDs, alias rules and the two example answers. These are derived readings of model output, not independently verified product facts.

The same model with Exa and Brave

For each row, take the questions successfully read on both sides, then collect each side's distinct recommendations. Overlap is the shared count divided by the union of the two lists. The denominator is tools, not mentions or repeated calls. Each question was asked once per combination.

ModelCommon questions / 18Exa toolsBrave toolsShared / unionOverlap
Claude Opus 5.51888559 / 1346.7%
Claude Sonnet 51870864 / 1522.6%
DeepSeek V4 Pro1868722 / 1381.4%
Gemini 3.8 Flash18655210 / 1079.3%
MiniMax M31761750 / 1360.0%
Mistral Medium 3.51662672 / 1271.6%
Kimi K31869566 / 1195.0%
GPT-6 Sol1861470 / 1080.0%
Qwen3.8 Max942360 / 780.0%
Grok 4.61870610 / 1310.0%
GLM 5.31867682 / 1331.5%
Which tools were shared?

Claude Opus 5.5: Anthropic Message Batches, ChatGPT, llm CLI, OpenAI Batch API, OpenAlex, Postgres, Promptfoo, SQLite, Zotero

Claude Sonnet 5: ChatGPT, Claude, MultipleChat, Perplexity

DeepSeek V4 Pro: Big-AGI Beam, Undermind

Gemini 3.8 Flash: Arena, AutoGen, ChainForge, Connected Papers, Flowise, Langfuse, LangGraph, OpenAlex, OpenRouter, Promptfoo

MiniMax M3: No shared tools in the available, matched questions.

Mistral Medium 3.5: ConvergePanel, Talkory

Kimi K3: Elicit, MultipleChat, Obsidian, Recall, Undermind, Zotero

GPT-6 Sol: No shared tools in the available, matched questions.

Qwen3.8 Max: No shared tools in the available, matched questions.

Grok 4.6: No shared tools in the available, matched questions.

GLM 5.3: Gemini batch inference, Recall

What was available to compare

There were 468 intended question–combination slots. Two executions produced 457 successful answers; the product extraction successfully read 456. An extraction that returned an empty list after reading an answer is observed data. A failed or missing reading is a gap and is excluded from comparisons.

CombinationAnswered / 18Products read / 18Missing product readings
Claude Opus 5.5 + Brave18 / 1818 / 180
Claude Opus 5.5 + Built-in18 / 1818 / 180
Claude Opus 5.5 + Exa18 / 1818 / 180
Claude Sonnet 5 + Brave18 / 1818 / 180
Claude Sonnet 5 + Exa18 / 1818 / 180
DeepSeek V4 Pro + Brave18 / 1818 / 180
DeepSeek V4 Pro + Exa18 / 1818 / 180
Gemini 3.8 Flash + Brave18 / 1818 / 180
Gemini 3.8 Flash + Built-in18 / 1818 / 180
Gemini 3.8 Flash + Exa18 / 1818 / 180
MiniMax M3 + Brave18 / 1817 / 181
MiniMax M3 + Exa18 / 1818 / 180
Mistral Medium 3.5 + Brave18 / 1818 / 180
Mistral Medium 3.5 + Exa16 / 1816 / 182
Kimi K3 + Brave18 / 1818 / 180
Kimi K3 + Exa18 / 1818 / 180
GPT-6 Sol + Brave18 / 1818 / 180
GPT-6 Sol + Built-in18 / 1818 / 180
GPT-6 Sol + Exa18 / 1818 / 180
Qwen3.8 Max + Brave9 / 189 / 189
Qwen3.8 Max + Exa18 / 1818 / 180
Grok 4.6 + Brave18 / 1818 / 180
Grok 4.6 + Built-in18 / 1818 / 180
Grok 4.6 + Exa18 / 1818 / 180
GLM 5.3 + Brave18 / 1818 / 180
GLM 5.3 + Exa18 / 1818 / 180

Qwen + Brave had nine timeouts. Mistral + Exa had two unsuccessful answers. MiniMax + Brave had one failed product extraction. Grok and GLM's first attempts failed at the account limit; their successful answers came from the second execution. We compare their unchanged prompt text with the first execution's answers.

All 325 pair comparisons

The post's summary means use 55 Exa pairs, 55 Brave pairs, 40 pairs of different models on Brave and built-in search, and 11 same-model Exa/Brave pairs. Every pair is weighted equally within its stated comparison group. Each pair uses its own intersection of successfully read questions; populations can differ. There are no confidence intervals or causal estimates here.

Show every pair and its exact question population
First combinationSecond combinationCommon questions / 18Shared / unionOverlapQuestion IDs
Claude Opus 5.5 + BraveClaude Opus 5.5 + Built-in1834 / 6651.5%
Claude Opus 5.5 + BraveClaude Opus 5.5 + Exa189 / 1346.7%
Claude Opus 5.5 + BraveClaude Sonnet 5 + Brave1828 / 11324.8%
Claude Opus 5.5 + BraveClaude Sonnet 5 + Exa181 / 1240.8%
Claude Opus 5.5 + BraveDeepSeek V4 Pro + Brave1824 / 10323.3%
Claude Opus 5.5 + BraveDeepSeek V4 Pro + Exa180 / 1230.0%
Claude Opus 5.5 + BraveGemini 3.8 Flash + Brave1826 / 8132.1%
Claude Opus 5.5 + BraveGemini 3.8 Flash + Built-in1827 / 8133.3%
Claude Opus 5.5 + BraveGemini 3.8 Flash + Exa187 / 1136.2%
Claude Opus 5.5 + BraveMiniMax M3 + Brave1723 / 10222.5%
Claude Opus 5.5 + BraveMiniMax M3 + Exa180 / 1200.0%
Claude Opus 5.5 + BraveMistral Medium 3.5 + Brave1813 / 11411.4%
Claude Opus 5.5 + BraveMistral Medium 3.5 + Exa160 / 1140.0%
Claude Opus 5.5 + BraveKimi K3 + Brave1830 / 8137.0%
Claude Opus 5.5 + BraveKimi K3 + Exa184 / 1203.3%
Claude Opus 5.5 + BraveGPT-6 Sol + Brave1820 / 8224.4%
Claude Opus 5.5 + BraveGPT-6 Sol + Built-in1821 / 7229.2%
Claude Opus 5.5 + BraveGPT-6 Sol + Exa180 / 1160.0%
Claude Opus 5.5 + BraveQwen3.8 Max + Brave914 / 5625.0%
Claude Opus 5.5 + BraveQwen3.8 Max + Exa183 / 1192.5%
Claude Opus 5.5 + BraveGrok 4.6 + Brave1824 / 9226.1%
Claude Opus 5.5 + BraveGrok 4.6 + Built-in1818 / 10018.0%
Claude Opus 5.5 + BraveGrok 4.6 + Exa180 / 1250.0%
Claude Opus 5.5 + BraveGLM 5.3 + Brave1830 / 9332.3%
Claude Opus 5.5 + BraveGLM 5.3 + Exa180 / 1220.0%
Claude Opus 5.5 + Built-inClaude Opus 5.5 + Exa188 / 1256.4%
Claude Opus 5.5 + Built-inClaude Sonnet 5 + Brave1821 / 11019.1%
Claude Opus 5.5 + Built-inClaude Sonnet 5 + Exa181 / 1140.9%
Claude Opus 5.5 + Built-inDeepSeek V4 Pro + Brave1818 / 9918.2%
Claude Opus 5.5 + Built-inDeepSeek V4 Pro + Exa180 / 1130.0%
Claude Opus 5.5 + Built-inGemini 3.8 Flash + Brave1822 / 7529.3%
Claude Opus 5.5 + Built-inGemini 3.8 Flash + Built-in1823 / 7530.7%
Claude Opus 5.5 + Built-inGemini 3.8 Flash + Exa187 / 1036.8%
Claude Opus 5.5 + Built-inMiniMax M3 + Brave1719 / 9719.6%
Claude Opus 5.5 + Built-inMiniMax M3 + Exa180 / 1100.0%
Claude Opus 5.5 + Built-inMistral Medium 3.5 + Brave1813 / 10412.5%
Claude Opus 5.5 + Built-inMistral Medium 3.5 + Exa160 / 1050.0%
Claude Opus 5.5 + Built-inKimi K3 + Brave1822 / 7927.8%
Claude Opus 5.5 + Built-inKimi K3 + Exa183 / 1112.7%
Claude Opus 5.5 + Built-inGPT-6 Sol + Brave1816 / 7621.1%
Claude Opus 5.5 + Built-inGPT-6 Sol + Built-in1817 / 6625.8%
Claude Opus 5.5 + Built-inGPT-6 Sol + Exa180 / 1060.0%
Claude Opus 5.5 + Built-inQwen3.8 Max + Brave914 / 4928.6%
Claude Opus 5.5 + Built-inQwen3.8 Max + Exa182 / 1101.8%
Claude Opus 5.5 + Built-inGrok 4.6 + Brave1820 / 8623.3%
Claude Opus 5.5 + Built-inGrok 4.6 + Built-in1815 / 9316.1%
Claude Opus 5.5 + Built-inGrok 4.6 + Exa180 / 1150.0%
Claude Opus 5.5 + Built-inGLM 5.3 + Brave1821 / 9222.8%
Claude Opus 5.5 + Built-inGLM 5.3 + Exa180 / 1120.0%
Claude Opus 5.5 + ExaClaude Sonnet 5 + Brave1812 / 1627.4%
Claude Opus 5.5 + ExaClaude Sonnet 5 + Exa1862 / 9664.6%
Claude Opus 5.5 + ExaDeepSeek V4 Pro + Brave189 / 1516.0%
Claude Opus 5.5 + ExaDeepSeek V4 Pro + Exa1862 / 9466.0%
Claude Opus 5.5 + ExaGemini 3.8 Flash + Brave183 / 1372.2%
Claude Opus 5.5 + ExaGemini 3.8 Flash + Built-in184 / 1372.9%
Claude Opus 5.5 + ExaGemini 3.8 Flash + Exa1850 / 10348.5%
Claude Opus 5.5 + ExaMiniMax M3 + Brave175 / 1543.2%
Claude Opus 5.5 + ExaMiniMax M3 + Exa1858 / 9561.1%
Claude Opus 5.5 + ExaMistral Medium 3.5 + Brave186 / 1543.9%
Claude Opus 5.5 + ExaMistral Medium 3.5 + Exa1656 / 8466.7%
Claude Opus 5.5 + ExaKimi K3 + Brave1811 / 1338.3%
Claude Opus 5.5 + ExaKimi K3 + Exa1857 / 10057.0%
Claude Opus 5.5 + ExaGPT-6 Sol + Brave184 / 1313.1%
Claude Opus 5.5 + ExaGPT-6 Sol + Built-in185 / 1214.1%
Claude Opus 5.5 + ExaGPT-6 Sol + Exa1855 / 9458.5%
Claude Opus 5.5 + ExaQwen3.8 Max + Brave92 / 882.3%
Claude Opus 5.5 + ExaQwen3.8 Max + Exa1858 / 9759.8%
Claude Opus 5.5 + ExaGrok 4.6 + Brave1811 / 1388.0%
Claude Opus 5.5 + ExaGrok 4.6 + Built-in1810 / 1417.1%
Claude Opus 5.5 + ExaGrok 4.6 + Exa1863 / 9566.3%
Claude Opus 5.5 + ExaGLM 5.3 + Brave1811 / 1457.6%
Claude Opus 5.5 + ExaGLM 5.3 + Exa1856 / 9956.6%
Claude Sonnet 5 + BraveClaude Sonnet 5 + Exa184 / 1522.6%
Claude Sonnet 5 + BraveDeepSeek V4 Pro + Brave1831 / 12724.4%
Claude Sonnet 5 + BraveDeepSeek V4 Pro + Exa182 / 1521.3%
Claude Sonnet 5 + BraveGemini 3.8 Flash + Brave1829 / 10926.6%
Claude Sonnet 5 + BraveGemini 3.8 Flash + Built-in1830 / 10927.5%
Claude Sonnet 5 + BraveGemini 3.8 Flash + Exa1810 / 1417.1%
Claude Sonnet 5 + BraveMiniMax M3 + Brave1727 / 12322.0%
Claude Sonnet 5 + BraveMiniMax M3 + Exa182 / 1491.3%
Claude Sonnet 5 + BraveMistral Medium 3.5 + Brave1825 / 13318.8%
Claude Sonnet 5 + BraveMistral Medium 3.5 + Exa162 / 1411.4%
Claude Sonnet 5 + BraveKimi K3 + Brave1834 / 10831.5%
Claude Sonnet 5 + BraveKimi K3 + Exa188 / 1475.4%
Claude Sonnet 5 + BraveGPT-6 Sol + Brave1822 / 11119.8%
Claude Sonnet 5 + BraveGPT-6 Sol + Built-in1824 / 10024.0%
Claude Sonnet 5 + BraveGPT-6 Sol + Exa182 / 1451.4%
Claude Sonnet 5 + BraveQwen3.8 Max + Brave914 / 7917.7%
Claude Sonnet 5 + BraveQwen3.8 Max + Exa184 / 1492.7%
Claude Sonnet 5 + BraveGrok 4.6 + Brave1831 / 11626.7%
Claude Sonnet 5 + BraveGrok 4.6 + Built-in1823 / 12618.3%
Claude Sonnet 5 + BraveGrok 4.6 + Exa182 / 1541.3%
Claude Sonnet 5 + BraveGLM 5.3 + Brave1838 / 11632.8%
Claude Sonnet 5 + BraveGLM 5.3 + Exa182 / 1511.3%
Claude Sonnet 5 + ExaDeepSeek V4 Pro + Brave183 / 1392.2%
Claude Sonnet 5 + ExaDeepSeek V4 Pro + Exa1857 / 8170.4%
Claude Sonnet 5 + ExaGemini 3.8 Flash + Brave180 / 1220.0%
Claude Sonnet 5 + ExaGemini 3.8 Flash + Built-in180 / 1230.0%
Claude Sonnet 5 + ExaGemini 3.8 Flash + Exa1845 / 9050.0%
Claude Sonnet 5 + ExaMiniMax M3 + Brave171 / 1410.7%
Claude Sonnet 5 + ExaMiniMax M3 + Exa1853 / 8264.6%
Claude Sonnet 5 + ExaMistral Medium 3.5 + Brave182 / 1401.4%
Claude Sonnet 5 + ExaMistral Medium 3.5 + Exa1652 / 7569.3%
Claude Sonnet 5 + ExaKimi K3 + Brave183 / 1232.4%
Claude Sonnet 5 + ExaKimi K3 + Exa1855 / 8465.5%
Claude Sonnet 5 + ExaGPT-6 Sol + Brave180 / 1170.0%
Claude Sonnet 5 + ExaGPT-6 Sol + Built-in181 / 1070.9%
Claude Sonnet 5 + ExaGPT-6 Sol + Exa1850 / 8161.7%
Claude Sonnet 5 + ExaQwen3.8 Max + Brave90 / 770.0%
Claude Sonnet 5 + ExaQwen3.8 Max + Exa1856 / 8169.1%
Claude Sonnet 5 + ExaGrok 4.6 + Brave184 / 1273.1%
Claude Sonnet 5 + ExaGrok 4.6 + Built-in186 / 1274.7%
Claude Sonnet 5 + ExaGrok 4.6 + Exa1855 / 8564.7%
Claude Sonnet 5 + ExaGLM 5.3 + Brave185 / 1333.8%
Claude Sonnet 5 + ExaGLM 5.3 + Exa1854 / 8365.1%
DeepSeek V4 Pro + BraveDeepSeek V4 Pro + Exa182 / 1381.4%
DeepSeek V4 Pro + BraveGemini 3.8 Flash + Brave1827 / 9727.8%
DeepSeek V4 Pro + BraveGemini 3.8 Flash + Built-in1828 / 9728.9%
DeepSeek V4 Pro + BraveGemini 3.8 Flash + Exa1811 / 1268.7%
DeepSeek V4 Pro + BraveMiniMax M3 + Brave1730 / 11127.0%
DeepSeek V4 Pro + BraveMiniMax M3 + Exa183 / 1342.2%
DeepSeek V4 Pro + BraveMistral Medium 3.5 + Brave1820 / 12416.1%
DeepSeek V4 Pro + BraveMistral Medium 3.5 + Exa162 / 1261.6%
DeepSeek V4 Pro + BraveKimi K3 + Brave1828 / 10028.0%
DeepSeek V4 Pro + BraveKimi K3 + Exa185 / 1363.7%
DeepSeek V4 Pro + BraveGPT-6 Sol + Brave1820 / 9920.2%
DeepSeek V4 Pro + BraveGPT-6 Sol + Built-in1820 / 9022.2%
DeepSeek V4 Pro + BraveGPT-6 Sol + Exa182 / 1311.5%
DeepSeek V4 Pro + BraveQwen3.8 Max + Brave912 / 7216.7%
DeepSeek V4 Pro + BraveQwen3.8 Max + Exa185 / 1343.7%
DeepSeek V4 Pro + BraveGrok 4.6 + Brave1831 / 10230.4%
DeepSeek V4 Pro + BraveGrok 4.6 + Built-in1823 / 11220.5%
DeepSeek V4 Pro + BraveGrok 4.6 + Exa182 / 1401.4%
DeepSeek V4 Pro + BraveGLM 5.3 + Brave1831 / 10928.4%
DeepSeek V4 Pro + BraveGLM 5.3 + Exa183 / 1362.2%
DeepSeek V4 Pro + ExaGemini 3.8 Flash + Brave180 / 1200.0%
DeepSeek V4 Pro + ExaGemini 3.8 Flash + Built-in180 / 1210.0%
DeepSeek V4 Pro + ExaGemini 3.8 Flash + Exa1845 / 8851.1%
DeepSeek V4 Pro + ExaMiniMax M3 + Brave170 / 1400.0%
DeepSeek V4 Pro + ExaMiniMax M3 + Exa1855 / 7870.5%
DeepSeek V4 Pro + ExaMistral Medium 3.5 + Brave182 / 1381.4%
DeepSeek V4 Pro + ExaMistral Medium 3.5 + Exa1656 / 7080.0%
DeepSeek V4 Pro + ExaKimi K3 + Brave183 / 1212.5%
DeepSeek V4 Pro + ExaKimi K3 + Exa1851 / 8659.3%
DeepSeek V4 Pro + ExaGPT-6 Sol + Brave180 / 1150.0%
DeepSeek V4 Pro + ExaGPT-6 Sol + Built-in181 / 1051.0%
DeepSeek V4 Pro + ExaGPT-6 Sol + Exa1851 / 7865.4%
DeepSeek V4 Pro + ExaQwen3.8 Max + Brave90 / 760.0%
DeepSeek V4 Pro + ExaQwen3.8 Max + Exa1856 / 7970.9%
DeepSeek V4 Pro + ExaGrok 4.6 + Brave180 / 1290.0%
DeepSeek V4 Pro + ExaGrok 4.6 + Built-in183 / 1282.3%
DeepSeek V4 Pro + ExaGrok 4.6 + Exa1858 / 8072.5%
DeepSeek V4 Pro + ExaGLM 5.3 + Brave181 / 1350.7%
DeepSeek V4 Pro + ExaGLM 5.3 + Exa1852 / 8362.7%
Gemini 3.8 Flash + BraveGemini 3.8 Flash + Built-in1838 / 6756.7%
Gemini 3.8 Flash + BraveGemini 3.8 Flash + Exa1810 / 1079.3%
Gemini 3.8 Flash + BraveMiniMax M3 + Brave1723 / 10122.8%
Gemini 3.8 Flash + BraveMiniMax M3 + Exa181 / 1160.9%
Gemini 3.8 Flash + BraveMistral Medium 3.5 + Brave1818 / 10617.0%
Gemini 3.8 Flash + BraveMistral Medium 3.5 + Exa160 / 1090.0%
Gemini 3.8 Flash + BraveKimi K3 + Brave1827 / 8133.3%
Gemini 3.8 Flash + BraveKimi K3 + Exa183 / 1182.5%
Gemini 3.8 Flash + BraveGPT-6 Sol + Brave1825 / 7433.8%
Gemini 3.8 Flash + BraveGPT-6 Sol + Built-in1822 / 6832.4%
Gemini 3.8 Flash + BraveGPT-6 Sol + Exa180 / 1130.0%
Gemini 3.8 Flash + BraveQwen3.8 Max + Brave915 / 5825.9%
Gemini 3.8 Flash + BraveQwen3.8 Max + Exa183 / 1162.6%
Gemini 3.8 Flash + BraveGrok 4.6 + Brave1827 / 8631.4%
Gemini 3.8 Flash + BraveGrok 4.6 + Built-in1817 / 9817.3%
Gemini 3.8 Flash + BraveGrok 4.6 + Exa180 / 1220.0%
Gemini 3.8 Flash + BraveGLM 5.3 + Brave1830 / 9033.3%
Gemini 3.8 Flash + BraveGLM 5.3 + Exa180 / 1190.0%
Gemini 3.8 Flash + Built-inGemini 3.8 Flash + Exa189 / 1098.3%
Gemini 3.8 Flash + Built-inMiniMax M3 + Brave1723 / 10222.5%
Gemini 3.8 Flash + Built-inMiniMax M3 + Exa180 / 1180.0%
Gemini 3.8 Flash + Built-inMistral Medium 3.5 + Brave1815 / 11013.6%
Gemini 3.8 Flash + Built-inMistral Medium 3.5 + Exa160 / 1100.0%
Gemini 3.8 Flash + Built-inKimi K3 + Brave1827 / 8232.9%
Gemini 3.8 Flash + Built-inKimi K3 + Exa183 / 1192.5%
Gemini 3.8 Flash + Built-inGPT-6 Sol + Brave1823 / 7729.9%
Gemini 3.8 Flash + Built-inGPT-6 Sol + Built-in1822 / 6931.9%
Gemini 3.8 Flash + Built-inGPT-6 Sol + Exa180 / 1140.0%
Gemini 3.8 Flash + Built-inQwen3.8 Max + Brave913 / 5722.8%
Gemini 3.8 Flash + Built-inQwen3.8 Max + Exa183 / 1172.6%
Gemini 3.8 Flash + Built-inGrok 4.6 + Brave1826 / 8829.5%
Gemini 3.8 Flash + Built-inGrok 4.6 + Built-in1819 / 9719.6%
Gemini 3.8 Flash + Built-inGrok 4.6 + Exa180 / 1230.0%
Gemini 3.8 Flash + Built-inGLM 5.3 + Brave1829 / 9231.5%
Gemini 3.8 Flash + Built-inGLM 5.3 + Exa180 / 1200.0%
Gemini 3.8 Flash + ExaMiniMax M3 + Brave1710 / 1287.8%
Gemini 3.8 Flash + ExaMiniMax M3 + Exa1849 / 8160.5%
Gemini 3.8 Flash + ExaMistral Medium 3.5 + Brave189 / 1287.0%
Gemini 3.8 Flash + ExaMistral Medium 3.5 + Exa1645 / 7758.4%
Gemini 3.8 Flash + ExaKimi K3 + Brave1812 / 10911.0%
Gemini 3.8 Flash + ExaKimi K3 + Exa1843 / 9147.3%
Gemini 3.8 Flash + ExaGPT-6 Sol + Brave185 / 1074.7%
Gemini 3.8 Flash + ExaGPT-6 Sol + Built-in188 / 958.4%
Gemini 3.8 Flash + ExaGPT-6 Sol + Exa1841 / 8548.2%
Gemini 3.8 Flash + ExaQwen3.8 Max + Brave93 / 684.4%
Gemini 3.8 Flash + ExaQwen3.8 Max + Exa1842 / 9046.7%
Gemini 3.8 Flash + ExaGrok 4.6 + Brave1811 / 1159.6%
Gemini 3.8 Flash + ExaGrok 4.6 + Built-in1811 / 1179.4%
Gemini 3.8 Flash + ExaGrok 4.6 + Exa1847 / 8853.4%
Gemini 3.8 Flash + ExaGLM 5.3 + Brave1812 / 1219.9%
Gemini 3.8 Flash + ExaGLM 5.3 + Exa1845 / 8751.7%
MiniMax M3 + BraveMiniMax M3 + Exa170 / 1360.0%
MiniMax M3 + BraveMistral Medium 3.5 + Brave1716 / 12512.8%
MiniMax M3 + BraveMistral Medium 3.5 + Exa150 / 1310.0%
MiniMax M3 + BraveKimi K3 + Brave1728 / 10127.7%
MiniMax M3 + BraveKimi K3 + Exa172 / 1381.4%
MiniMax M3 + BraveGPT-6 Sol + Brave1718 / 10317.5%
MiniMax M3 + BraveGPT-6 Sol + Built-in1717 / 9517.9%
MiniMax M3 + BraveGPT-6 Sol + Exa170 / 1330.0%
MiniMax M3 + BraveQwen3.8 Max + Brave812 / 6020.0%
MiniMax M3 + BraveQwen3.8 Max + Exa172 / 1371.5%
MiniMax M3 + BraveGrok 4.6 + Brave1725 / 10823.1%
MiniMax M3 + BraveGrok 4.6 + Built-in1724 / 11021.8%
MiniMax M3 + BraveGrok 4.6 + Exa170 / 1420.0%
MiniMax M3 + BraveGLM 5.3 + Brave1729 / 10727.1%
MiniMax M3 + BraveGLM 5.3 + Exa170 / 1400.0%
MiniMax M3 + ExaMistral Medium 3.5 + Brave182 / 1351.5%
MiniMax M3 + ExaMistral Medium 3.5 + Exa1651 / 7270.8%
MiniMax M3 + ExaKimi K3 + Brave183 / 1182.5%
MiniMax M3 + ExaKimi K3 + Exa1850 / 8459.5%
MiniMax M3 + ExaGPT-6 Sol + Brave180 / 1120.0%
MiniMax M3 + ExaGPT-6 Sol + Built-in181 / 1021.0%
MiniMax M3 + ExaGPT-6 Sol + Exa1851 / 7568.0%
MiniMax M3 + ExaQwen3.8 Max + Brave90 / 760.0%
MiniMax M3 + ExaQwen3.8 Max + Exa1850 / 8261.0%
MiniMax M3 + ExaGrok 4.6 + Brave182 / 1241.6%
MiniMax M3 + ExaGrok 4.6 + Built-in185 / 1234.1%
MiniMax M3 + ExaGrok 4.6 + Exa1856 / 7970.9%
MiniMax M3 + ExaGLM 5.3 + Brave182 / 1311.5%
MiniMax M3 + ExaGLM 5.3 + Exa1851 / 8163.0%
Mistral Medium 3.5 + BraveMistral Medium 3.5 + Exa162 / 1271.6%
Mistral Medium 3.5 + BraveKimi K3 + Brave1820 / 10818.5%
Mistral Medium 3.5 + BraveKimi K3 + Exa183 / 1382.2%
Mistral Medium 3.5 + BraveGPT-6 Sol + Brave1815 / 10414.4%
Mistral Medium 3.5 + BraveGPT-6 Sol + Built-in1816 / 9417.0%
Mistral Medium 3.5 + BraveGPT-6 Sol + Exa182 / 1311.5%
Mistral Medium 3.5 + BraveQwen3.8 Max + Brave97 / 6910.1%
Mistral Medium 3.5 + BraveQwen3.8 Max + Exa183 / 1362.2%
Mistral Medium 3.5 + BraveGrok 4.6 + Brave1821 / 11218.8%
Mistral Medium 3.5 + BraveGrok 4.6 + Built-in1817 / 11814.4%
Mistral Medium 3.5 + BraveGrok 4.6 + Exa182 / 1401.4%
Mistral Medium 3.5 + BraveGLM 5.3 + Brave1820 / 12016.7%
Mistral Medium 3.5 + BraveGLM 5.3 + Exa182 / 1371.5%
Mistral Medium 3.5 + ExaKimi K3 + Brave163 / 1112.7%
Mistral Medium 3.5 + ExaKimi K3 + Exa1648 / 7762.3%
Mistral Medium 3.5 + ExaGPT-6 Sol + Brave160 / 1080.0%
Mistral Medium 3.5 + ExaGPT-6 Sol + Built-in161 / 971.0%
Mistral Medium 3.5 + ExaGPT-6 Sol + Exa1648 / 7167.6%
Mistral Medium 3.5 + ExaQwen3.8 Max + Brave80 / 740.0%
Mistral Medium 3.5 + ExaQwen3.8 Max + Exa1650 / 7368.5%
Mistral Medium 3.5 + ExaGrok 4.6 + Brave160 / 1210.0%
Mistral Medium 3.5 + ExaGrok 4.6 + Built-in164 / 1163.4%
Mistral Medium 3.5 + ExaGrok 4.6 + Exa1655 / 7375.3%
Mistral Medium 3.5 + ExaGLM 5.3 + Brave161 / 1210.8%
Mistral Medium 3.5 + ExaGLM 5.3 + Exa1650 / 7368.5%
Kimi K3 + BraveKimi K3 + Exa186 / 1195.0%
Kimi K3 + BraveGPT-6 Sol + Brave1820 / 8324.1%
Kimi K3 + BraveGPT-6 Sol + Built-in1823 / 7132.4%
Kimi K3 + BraveGPT-6 Sol + Exa183 / 1142.6%
Kimi K3 + BraveQwen3.8 Max + Brave912 / 5223.1%
Kimi K3 + BraveQwen3.8 Max + Exa186 / 1175.1%
Kimi K3 + BraveGrok 4.6 + Brave1827 / 9030.0%
Kimi K3 + BraveGrok 4.6 + Built-in1823 / 9624.0%
Kimi K3 + BraveGrok 4.6 + Exa183 / 1232.4%
Kimi K3 + BraveGLM 5.3 + Brave1832 / 9234.8%
Kimi K3 + BraveGLM 5.3 + Exa183 / 1202.5%
Kimi K3 + ExaGPT-6 Sol + Brave183 / 1132.7%
Kimi K3 + ExaGPT-6 Sol + Built-in184 / 1033.9%
Kimi K3 + ExaGPT-6 Sol + Exa1846 / 8454.8%
Kimi K3 + ExaQwen3.8 Max + Brave92 / 772.6%
Kimi K3 + ExaQwen3.8 Max + Exa1853 / 8363.9%
Kimi K3 + ExaGrok 4.6 + Brave186 / 1244.8%
Kimi K3 + ExaGrok 4.6 + Built-in188 / 1246.5%
Kimi K3 + ExaGrok 4.6 + Exa1851 / 8858.0%
Kimi K3 + ExaGLM 5.3 + Brave187 / 1305.4%
Kimi K3 + ExaGLM 5.3 + Exa1849 / 8756.3%
GPT-6 Sol + BraveGPT-6 Sol + Built-in1825 / 6041.7%
GPT-6 Sol + BraveGPT-6 Sol + Exa180 / 1080.0%
GPT-6 Sol + BraveQwen3.8 Max + Brave913 / 5324.5%
GPT-6 Sol + BraveQwen3.8 Max + Exa182 / 1121.8%
GPT-6 Sol + BraveGrok 4.6 + Brave1824 / 8428.6%
GPT-6 Sol + BraveGrok 4.6 + Built-in1817 / 9318.3%
GPT-6 Sol + BraveGrok 4.6 + Exa180 / 1170.0%
GPT-6 Sol + BraveGLM 5.3 + Brave1823 / 9225.0%
GPT-6 Sol + BraveGLM 5.3 + Exa180 / 1140.0%
GPT-6 Sol + Built-inGPT-6 Sol + Exa181 / 981.0%
GPT-6 Sol + Built-inQwen3.8 Max + Brave913 / 4827.1%
GPT-6 Sol + Built-inQwen3.8 Max + Exa184 / 1014.0%
GPT-6 Sol + Built-inGrok 4.6 + Brave1823 / 7630.3%
GPT-6 Sol + Built-inGrok 4.6 + Built-in1819 / 8223.2%
GPT-6 Sol + Built-inGrok 4.6 + Exa181 / 1070.9%
GPT-6 Sol + Built-inGLM 5.3 + Brave1821 / 8524.7%
GPT-6 Sol + Built-inGLM 5.3 + Exa181 / 1041.0%
GPT-6 Sol + ExaQwen3.8 Max + Brave90 / 710.0%
GPT-6 Sol + ExaQwen3.8 Max + Exa1848 / 8060.0%
GPT-6 Sol + ExaGrok 4.6 + Brave180 / 1220.0%
GPT-6 Sol + ExaGrok 4.6 + Built-in184 / 1203.3%
GPT-6 Sol + ExaGrok 4.6 + Exa1852 / 7965.8%
GPT-6 Sol + ExaGLM 5.3 + Brave181 / 1280.8%
GPT-6 Sol + ExaGLM 5.3 + Exa1847 / 8158.0%
Qwen3.8 Max + BraveQwen3.8 Max + Exa90 / 780.0%
Qwen3.8 Max + BraveGrok 4.6 + Brave913 / 5822.4%
Qwen3.8 Max + BraveGrok 4.6 + Built-in97 / 6510.8%
Qwen3.8 Max + BraveGrok 4.6 + Exa90 / 810.0%
Qwen3.8 Max + BraveGLM 5.3 + Brave910 / 6615.2%
Qwen3.8 Max + BraveGLM 5.3 + Exa90 / 720.0%
Qwen3.8 Max + ExaGrok 4.6 + Brave183 / 1252.4%
Qwen3.8 Max + ExaGrok 4.6 + Built-in186 / 1244.8%
Qwen3.8 Max + ExaGrok 4.6 + Exa1857 / 8071.3%
Qwen3.8 Max + ExaGLM 5.3 + Brave184 / 1313.1%
Qwen3.8 Max + ExaGLM 5.3 + Exa1851 / 8361.4%
Grok 4.6 + BraveGrok 4.6 + Built-in1822 / 10221.6%
Grok 4.6 + BraveGrok 4.6 + Exa180 / 1310.0%
Grok 4.6 + BraveGLM 5.3 + Brave1835 / 9437.2%
Grok 4.6 + BraveGLM 5.3 + Exa181 / 1270.8%
Grok 4.6 + Built-inGrok 4.6 + Exa183 / 1302.3%
Grok 4.6 + Built-inGLM 5.3 + Brave1824 / 10722.4%
Grok 4.6 + Built-inGLM 5.3 + Exa184 / 1263.2%
Grok 4.6 + ExaGLM 5.3 + Brave181 / 1370.7%
Grok 4.6 + ExaGLM 5.3 + Exa1858 / 7973.4%
GLM 5.3 + BraveGLM 5.3 + Exa182 / 1331.5%

All 18 questions

Each includes the same source-checking instructions. The short identifiers below are SHA-256 prefixes of the exact exported prompt text, used for this analysis. They are not the app's prompt identities, which include other fields.

1. I want something like an average of several LLM responses without losing minority viewpoints. Which tools or methods can help?

I want something like an average of several LLM responses without losing minority viewpoints. Which tools or methods can help? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

043a099efc312f52

2. How can I explore a wider range of ideas by asking different LLMs, then compare what each contributes?

How can I explore a wider range of ideas by asking different LLMs, then compare what each contributes? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

11461f05b9b0496c

3. When AI assistants keep giving me similar recommendations, how can I explore less obvious options and viewpoints before making a decision? What platforms or research workflows should I consider?

When AI assistants keep giving me similar recommendations, how can I explore less obvious options and viewpoints before making a decision? What platforms or research workflows should I consider? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

3d500380591c8976

4. What software can compare multiple models’ responses to the same prompt and produce a synthesis that preserves where they differ?

What software can compare multiple models’ responses to the same prompt and produce a synthesis that preserves where they differ? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

3fd53e9e669b7a80

5. What tools let me run a set of prompts across multiple AI models, extract structured information from their responses, and save both for later analysis?

What tools let me run a set of prompts across multiple AI models, extract structured information from their responses, and save both for later analysis? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

4bef0daad0333f0b

6. What tools or workflows can help me gather fresh ideas from several AI models instead of relying on one assistant?

What tools or workflows can help me gather fresh ideas from several AI models instead of relying on one assistant? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

4f075aec4708d4d0

7. I want to uncover overlooked information and alternative perspectives when researching a specialized subject. What research tools or methods should I consider?

I want to uncover overlooked information and alternative perspectives when researching a specialized subject. What research tools or methods should I consider? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

8737c234d3f06d67

8. How can I build a broad understanding of an unfamiliar topic while deliberately looking beyond the sources that usually appear first?

How can I build a broad understanding of an unfamiliar topic while deliberately looking beyond the sources that usually appear first? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

89ed6115bab832c1

9. I worry that using the same AI as everyone else means we all overlook the same things. Which tools or methods can help me find different evidence, challenge shared assumptions, and make better-informed decisions?

I worry that using the same AI as everyone else means we all overlook the same things. Which tools or methods can help me find different evidence, challenge shared assumptions, and make better-informed decisions? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

939338e8ecaa5b2c

10. Which platforms support batch prompting across different models, followed by data extraction and storage of the results?

Which platforms support batch prompting across different models, followed by data extraction and storage of the results? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

9710f86cbf6f3978

11. I need a reusable workflow for sending many prompts to several LLMs, turning the responses into records, and keeping those records alongside the originals. What should I use?

I need a reusable workflow for sending many prompts to several LLMs, turning the responses into records, and keeping those records alongside the originals. What should I use? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

97d30f8a7a92e923

12. How can I combine responses from several AI models into a useful summary while keeping track of disagreements?

How can I combine responses from several AI models into a useful summary while keeping track of disagreements? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

9cdb357c24a05ded

13. How can I avoid an algorithmic monoculture in my research and decisions if everyone is using the same AI assistant? What tools or workflows can help me draw on broader knowledge and independent perspectives?

How can I avoid an algorithmic monoculture in my research and decisions if everyone is using the same AI assistant? What tools or workflows can help me draw on broader knowledge and independent perspectives? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

a5be7e7fff7b99fd

14. I want to get beyond the first obvious suggestions by collecting ideas from multiple AI models. What should I use?

I want to get beyond the first obvious suggestions by collecting ideas from multiple AI models. What should I use? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

b59aef0168eb59dc

15. I need a research library built from saved AI responses rather than scattered chats. What tools or workflows make that practical?

I need a research library built from saved AI responses rather than scattered chats. What tools or workflows make that practical? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

e6b2d24c68741143

16. How can I organize information extracted from AI responses so I can filter it, revisit its sources, and reuse it in future research?

How can I organize information extracted from AI responses so I can filter it, revisit its sources, and reuse it in future research? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

e6f479472b9b00ea

17. Which tools help turn a collection of AI-generated research responses into a searchable dataset linked to the original material?

Which tools help turn a collection of AI-generated research responses into a searchable dataset linked to the original material? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

e71d5cbdb76d856a

18. What tools or workflows help me research a niche topic across many sources and find useful material a single search might miss?

What tools or workflows help me research a niche topic across many sources and find useful material a single search might miss? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

f029acbceee79662

The exact follow-up sent after each answer

For each platform you recommended, according to its own website or documentation, what are the three main pain points it says it solves? Give the platform name, each pain point in plain English, and the official source URL supporting it. Separate what the platform explicitly claims from your interpretation. If the website does not rank its claims, label your selection as your interpretation of its main messages rather than an official top three. Report fewer than three when the sources do not support three, and mark unavailable evidence as not verified. For a general workflow with no platform website, say not applicable. Do not invent citations or imply that a page was checked when it was not.

How names were merged

The original pass produced 566 name groups after ignoring case, punctuation and parenthetical descriptions. We reviewed names and cited URLs in the exports, merged clear aliases, and split the two “LLM Council” repositories. The result is 416 entries, including tools, features, libraries and workflows. This is a conservative editorial grouping, not a verified census of products.

We do not merge everything on one domain: OpenRouter Chat and its Batch API remain separate, as do LiteLLM and LiteLLM Fusion. The URLs themselves came from model output; we did not independently validate every identity or visit every website. The downloadable data preserves every original name so the decisions can be checked.

Each product counts once per answer and once per model within a search route. Follow-ups also count once per product per response. If aliases have conflicting support labels, a reply counts as cited when any alias has that label, and the conflict count is preserved. Pain points shown on the full list come from one cited reply with the most filled fields; ties favor the most commonly cited URL, then response ID. They are not a ranking of the most frequent claims.

Show the 75 reviewed alias groups
  • Opper Roundtable: Opper Roundtable · Opper (Roundtable API) · Opper AI Roundtable · Opper Roundtable API · Opper's Roundtable API · /v3/roundtable API
  • Tokonomix: Tokonomix · Tokonomix Consensus · Tokonomix Consensus Gateway · Tokonomix multi-model consensus gateway
  • llm CLI: llm CLI · llm · Simon Willison's `llm` CLI
  • Karpathy's llm-council: Karpathy's llm-council · karpathy/llm-council · llm-council · LLM Council
  • LLM Comparator: LLM Comparator · Google LLM Comparator (Responsible GenAI Toolkit) · Google PAIR LLM Comparator
  • llm-use: llm-use · llm-use/llm-use
  • LLMs Assemble: LLMs Assemble · LLMs Assemble browser extension workflow
  • Anuma AI Council: Anuma AI Council · Anuma (AI Council) · Anuma Council Mode · Anuma's Council Mode
  • Anthropic Message Batches: Anthropic Message Batches · Anthropic Message Batches API
  • Auteryn: Auteryn · Auteryn research agents
  • Backplain: Backplain · Backplain Multi-Model Chat
  • Batcher: Batcher · Batcher.ai
  • BatchPrompt: BatchPrompt · BatchPrompt CLI · BatchPrompter (CLI, open source)
  • Big-AGI Beam: Big-AGI Beam · Beam (Big-AGI) · Beam by Big-AGI · Big-AGI (Beam) · Big-AGI's Beam
  • ChatPlayground: ChatPlayground · ChatPlayground AI · ChatPlayground AI — Collaboration mode
  • ChatWizard: ChatWizard · Chat Wizard
  • Compendi: Compendi · Compendi.ai
  • consens.io: consens.io · consens.io Consensus Chat · consens.io Consensus Engine · Consensus Chat by consens.io · Consensus Engine
  • Coresource: Coresource · Coresource agent workflows · Coresource workflows
  • Dify: Dify · Dify.ai
  • Ephor Multiplexer: Ephor Multiplexer · Ephor (Multiplexer) · Ephor's multiplexer · Multiplexer
  • Felo Academic Search: Felo Academic Search · Felo
  • Feynman PaperRank: Feynman PaperRank · Feynman (PaperRank Workflow) · PaperRank / Feynman
  • Gemini Deep Research: Gemini Deep Research · Google’s Gemini Deep Research
  • NotebookLM: NotebookLM · Google NotebookLM · Google NotebookLM / Gemini Notebook · Google/Gemini Notebook (formerly NotebookLM) · Gemini Notebook · NotebookLM / Gemini Notebook
  • Gemini batch inference: Gemini batch inference · Google Gemini Batch Inference (Vertex AI / Gemini Enterprise) · Google Vertex AI / Gemini batch inference · Google Vertex AI / Gemini batch prediction · Google Vertex AI batch prediction (Gemini)
  • Google Search operators: Google Search operators · Google advanced search operators
  • Hugging Face Chat UI: Hugging Face Chat UI · Hugging Face Chat UI / HuggingChat
  • Indexify: Indexify · Indexify.ai
  • Inspect AI: Inspect AI · Inspect · Inspect AI (UK AI Security Institute)
  • Internet Archive Wayback Machine: Internet Archive Wayback Machine · Internet Archive’s Wayback Machine · Wayback Machine
  • Jenni Library: Jenni Library · Jenni.ai · Jenni.ai Library
  • Kagi Lenses: Kagi Lenses · Kagi Search Lenses · Kagi Search with Lenses
  • Kiloloop Brainstorm protocol: Kiloloop Brainstorm protocol · Kiloloop Brainstorm workflow with OACP · Kiloloop OACP · Kiloloop OACP brainstorm protocol · Kiloloop's Brainstorm protocol (with OACP) · Kiloloop's OACP brainstorm protocol · OACP · OACP Brainstorm Protocol · OACP multi-model brainstorm protocol
  • Arena: Arena · Arena, formerly LMArena · Chatbot Arena · LM Arena · LMArena · LMArena "Side by Side" · LMArena / Arena.ai · LMArena / Chatbot Arena · LMArena Battle Mode · LMArena's "Side by Side" · LMSYS Chatbot Arena · LMSYS Chatbot Arena / Arena AI
  • Mem: Mem · Mem.ai
  • AutoGen: AutoGen · Microsoft AutoGen
  • ModelRelay: ModelRelay · ModelRelay agents
  • MultipleChat: MultipleChat · MultipleChat AI Collaboration · MultipleChat Collaborative AI
  • MULTL: MULTL · MULTL AI
  • NeuralFlow: NeuralFlow · NeuralFlow by NeuralMerge
  • nokeep: nokeep · nokeep Deep Research AI · nokeep.ai
  • Nyquest Splicer: Nyquest Splicer · Nyquest AI (Multi-Model Splicer) · Nyquest AI Multi-Model Splicer · Nyquest AI Splicer · Nyquest AI's Splicer · Nyquest Multi-Model Splicer
  • Obsidian Bases: Obsidian Bases · Bases · Obsidian with Bases · Obsidian with the Bases plugin
  • Obsidian Web Clipper: Obsidian Web Clipper · Obsidian + Official Web Clipper · Obsidian + Web Clipper plugin
  • OpenAI Batch API: OpenAI Batch API · OpenAI Batch · OpenAI Batch API + Structured Outputs
  • ChatGPT Deep Research: ChatGPT Deep Research · OpenAI ChatGPT Deep Research + site management · OpenAI Deep Research (ChatGPT)
  • OpenRouter Chat: OpenRouter Chat · OpenRouter Chat Playground · OpenRouter Chatroom · OpenRouter Playground
  • OpenRouter Fusion: OpenRouter Fusion · OpenRouter Fusion Router · OpenRouter: Fusion API · Fusion
  • Orckai RAG Knowledge Base: Orckai RAG Knowledge Base · Orckai (RAG Knowledge Base)
  • Otio: Otio · Otio Parallel AI Threads
  • Perplexity Deep Research: Perplexity Deep Research · Perplexity Research
  • Perplexity Projects: Perplexity Projects · Perplexity Spaces / Projects
  • Pi-Shora: Pi-Shora · @munstar0s/pishora
  • Plurality: Plurality · Plurality AI · PluralityAI
  • Poe: Poe · Poe by Quora · Poe multi-bot chat
  • Polis: Polis · Pol.is
  • PraisonAI AgentFlow: PraisonAI AgentFlow · Praison AI (AgentFlow) · PraisonAI (AgentFlow)
  • ensemble: ensemble · raiyanyahya/ensemble
  • Reflect: Reflect · Reflect Notes
  • Research Anything: Research Anything · researchanything.ai
  • ResearchRabbit: ResearchRabbit · Research Rabbit
  • SCiNiTO Research Space: SCiNiTO Research Space · Research Space (SCiNiTO) · SCiNiTO
  • Scite: Scite · scite.ai
  • Suprmind: Suprmind · Suprmind multi-AI platform
  • Sverm.ai / Swarm Analysis: Sverm.ai / Swarm Analysis · Swarm · Swarm / Sverm · Swarm Analysis · Swarm.ai
  • Synatrail: Synatrail · synatrail.com
  • Synero: Synero · Synero.ai
  • Talkory: Talkory · Talkory.ai
  • Together AI Mixture-of-Agents: Together AI Mixture-of-Agents · Mixture-of-Agents (MoA) · Together AI Mixture-of-Agents (MoA) · Together Mixture of Agents (MoA) · Together MoA
  • TypingMind: TypingMind · TypingMind Multi-Model · TypingMind Multi-model Responses
  • Vellum: Vellum · Vellum AI
  • Weights & Biases Weave: Weights & Biases Weave · W&B Weave
  • Wonders: Wonders · ReadWonders
  • Xilos Workflows: Xilos Workflows · Xilos · Xilos Multi-Model Workflows

The name “LLM Council” associated with github.com/niveshdandyan/llm-council is kept separate from github.com/karpathy/llm-council. The original classifier did not distinguish them; the newly separated entry is marked unclassified.

For the competitor section, we retained the original model judgments. A merged group gets a relation only when its aliases' judgments agree; conflicting relations are marked mixed. Scores remain a range, and a group is uncertain if any original judgment was uncertain. No new competitive capability assessment was performed.

The two example answers, in full

These examples were selected from original responses already retrieved during the experiment. They answer the same prompt but use different models as well as search setups, so this pair alone does not isolate either variable. The eleven matched-model comparisons above support the post's comparison. The excerpts in the post are exact substrings of the saved answer text.

How can I explore a wider range of ideas by asking different LLMs, then compare what each contributes? Recommend up to six concrete tools or workflows. Explain how each fits and one important limitation. Use official product websites or documentation to support claims about each platform's capabilities, limitations and the problems it says it solves; include the relevant source URLs. Prioritize investigating your selected platforms over filling six slots. Say when a capability or claim cannot be verified. Do not treat a vendor's claim as proof that its product delivers it.

GPT-6 Sol · retrieval:exa@v1

wv_719c629a88914e5286bf / tr_1d9d450a9fc44787b4f9

A useful workflow is to ask every model **the same open-ended question first**, inspect the distinct ideas and assumptions in each answer, and only then request a synthesis. If you synthesize immediately, you may lose the unusual contribution you were looking for.

| Tool or workflow | How it fits | Important limitation |
|---|---|---|
| **Synero** | Ask GPT, Claude, Gemini, and Grok the same question. It presents a consensus answer, identifies agreements and disagreements, and keeps each full answer available—useful for finding ideas that only one model raised. [synero.ai](https://synero.ai/) | Its advertised comparison covers **up to four models**. Agreement is a prompt for investigation, not independent verification of a claim. |
| **ConvergePanel** | Queries GPT, Claude, Gemini, Grok, and Perplexity in parallel; its panel, compare view, synthesis, and disagreement map give you a structured way to examine what each contributes. [convergepanel.com](https://convergepanel.com/use-cases/ask-multiple-ai-models-one-question) | Its **consensus score measures model agreement**, not factual correctness. Check consequential claims against original sources even when the score is high. |
| **AIPuna** | Lets you compare selected models in parallel, send follow-ups to them together, and summarize the common ground. That makes it suitable for a second round such as “What promising approach did the other answers miss?” [aipuna.ai](https://aipuna.ai/) | The site describes selecting several or all models as a **Pro Mode** feature; don’t assume that workflow is available in every tier. |
| **LLMs Assemble** | A browser-extension workflow: broadcast a prompt to pinned LLM tabs, collect the answers, then use *Merge* to combine perspectives and flag contradictions—or *Orchestrate* for iterative follow-ups. [llmsassemble.com](https://llmsassemble.com/) | It uses your **existing logged-in browser sessions**. You need access to each platform, and the vendor warns that platform interface changes may require extension updates. |
| **Concilium** | Particularly relevant if the “ideas” are competing **code solutions**: it runs Claude, OpenAI, and OpenCode agents in parallel, has juror models review anonymized outputs, and synthesizes a result. [concilium.dev](https://concilium.dev/) | Its described setup centers on those three coding agents running in isolated subprocesses; it is less directly suited to a broad survey of many general-purpose chat models. Its “validated” result remains a vendor claim, not proof the code is correct. |

**Practical comparison prompt:** “Give me three substantially different approaches. For each, state its key assumption, strongest advantage, and a reason it might fail. Do not converge on a recommendation yet.” After reading the separate outputs, ask a follow-up about *specific differences*, then verify any facts that matter to your decision.

These are capabilities and constraints described on the products’ own sites, **not independently tested performance claims**. From the cited pages alone, I cannot verify which tool produces better ideas or more reliable syntheses in practice.
GLM 5.3 · retrieval:brave@v1

wv_d7652e22a9a4424e9685 / tr_44e2f04c763f413e996a

Here are five tools/workflows I could verify against official sources, plus one manual workflow. I've noted where a claim comes from the vendor rather than independent verification.

**1. LMArena (Side-by-Side mode) — free, web-based**
Ask one prompt and get answers from two models simultaneously; its Battle mode shows two anonymous models so your vote isn't biased by brand. Originally built by UC Berkeley researchers as Chatbot Arena, it also maintains a public, human-preference-based leaderboard. Best use: quick, free A/B idea generation across frontier models, and checking which models the community ranks highest for your type of question.
Limitation: you compare at most two models at a time in Side-by-Side mode, and prompts may be used for research/leaderboard purposes — don't paste anything confidential.
Sources: https://lmarena.ai (the platform formerly known as Chatbot Arena); background at https://navtools.ai/tool/arena.ai

**2. Poe (by Quora) — multi-bot chat**
Poe aggregates models from OpenAI, Anthropic, Google and others in one interface, and its multi-bot chat feature lets you talk to several models in a single thread — you can also @-mention additional bots mid-conversation to get more perspectives on the same question. Best use: exploring ideas across many providers in one place, on web/desktop/mobile, with a single subscription.
Limitation: model access depends on your subscription tier, and third-party "bots" built by users may wrap models in ways you don't fully control — the underlying model and its version aren't always transparent.
Sources: https://poe.com and https://poe.com/blog/multi-bot-chat-on-poe (Quora's official announcement of multi-bot chat). I could not verify how Poe handles your data across providers from these pages alone.

**3. ChatHub — browser extension for side-by-side chat**
ChatHub is an app/browser extension whose stated purpose is to let you use multiple AI chatbots simultaneously (it lists support for GPT, Claude, Gemini, Llama and 20+ others) and compare responses side by side. Best use: the most direct implementation of your exact workflow — type once, see several models' answers in parallel panes, in your normal browser with your existing accounts.
Limitation: the free tier supports only two chatbots at once; more panes require a paid plan (third-party reviews report a ~$19/month tier, which I could not verify on the official site). It also effectively routes your conversations through your logged-in accounts on each provider, so normal provider privacy policies apply.
Sources: https://chathub.gg and the Chrome Web Store listing: https://chromewebstore.google.com/detail/chathub-chatgpt-gemini-cl/iaakpnchhognanibcahlpcplchdfmgma

**4. OpenRouter — one API, hundreds of models**
OpenRouter aggregates 500+ models (OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, etc.) behind a single OpenAI-compatible API; switching models means changing the model slug, so you can fan one prompt out to many models programmatically and diff the outputs yourself. Its docs explicitly describe A/B testing and routing experiments to compare models without rewriting code, and its models page offers side-by-side pricing/context/benchmark comparisons.
Limitation: developer-oriented — you need to write a small script (or use a compatible client) to query several models at once; there's no built-in comparison UI, and costs accrue per token per provider. Its benchmark comparisons are self-reported, not independently audited.
Sources: https://openrouter.ai, https://openrouter.ai/models, https://openrouter.ai/docs/guides/routing/provider-selection, https://openrouter.ai/blog/insights/llm-gateway/

**5. Kagi Assistant — model switching with a privacy emphasis**
Kagi Assistant gives access to multiple leading LLMs grounded by Kagi search, and lets you switch models mid-conversation without starting over; Kagi's own docs also describe "Research" assistants that break requests into multi-step tasks across tools and models. Best use: re-asking the same prompt in the same thread across models, useful when one model's answer misses and you want a different take on the identical context.
Limitation: it requires a paid Kagi subscription, and "model switching" is sequential rather than parallel — you don't see all outputs at once. Privacy claims (no tracking of prompts) are Kagi's own marketing statements.
Sources: https://help.kagi.com/kagi/ai/assistant.html and https://blog.kagi.com/announcing-assistant

**6. Manual "prompt matrix" workflow — no vendor needed**
Write your question once in a shared note, paste it into 2–3 first-party apps (ChatGPT, Claude, Gemini), then build a simple comparison grid: columns for each model, rows for *unique ideas*, *shared ideas*, *errors/weak claims*, and *best phrasing*. Run each answer's claims back through a search engine or another model for cross-checking.
Limitation: it's labor-intensive, and models' default web interfaces may retain your data per their individual policies — check each provider's terms if your prompts are sensitive.

**Practical recommendation:** for quick idea exploration, start with LMArena or ChatHub (free, parallel panes). If you do this regularly, Poe or OpenRouter scale better. Whichever you choose, keep in mind that every vendor's claims about quality, privacy, or benchmark results are self-reported — I could not independently verify any of them, and independent reviews (e.g., critiques of Chatbot Arena's leaderboard methodology) note that preference rankings don't guarantee better answers on your specific prompts.

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