𝐀𝐈 𝐂𝐨-𝐀𝐧𝐚𝐥𝐲𝐬𝐭 — 𝐋𝐢𝐯𝐞 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐀𝐩𝐩. 𝐂𝐨𝐬𝐭, 𝐪𝐮𝐚𝐥𝐢𝐭𝐲, 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 — 𝐰𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬? 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬𝐧’𝐭?

Sonnet-3.7 the best, Deepseek 2nd, Gemini excellent. 𝐓𝐫𝐲 𝐢𝐭 👉 rex.tigzig.com (open source)

✔ 𝐓𝐨𝐩 𝐋𝐢𝐧𝐞

As an AI Co-Analyst LLM, Sonnet-3.7 is my top choice for deep, incisive analysis support....loving Gemini-2.0-Flash for balance of quality, reliability and cost.. and it's the fastest. Deepseek-R1 quality close to Sonnet but less reliable. o3-mini is lowest cost but not too great

✔ 𝐓𝐚𝐤𝐞 𝐢𝐭 𝐟𝐨𝐫 𝐚 𝐬𝐩𝐢𝐧

▸Go to rex.tigzig.com → Click ‘Sample’ to auto-upload a sample file into a temporary Postgres database. Choose your advanced analyst agent - Gemini/Sonnet/R1/o3-mini. Use sample prompt or modify it

▸No login, database creds, or API keys needed

▸Option: connect your own database...or upload your own files

✔ 𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐭𝐮𝐩 ➔  𝐅𝐥𝐨𝐰𝐢𝐬𝐞 𝐀𝐈

Sequential Agents (LangGraph). Router agent ➟ regular queries to a general analyst agent and complex queries to an advanced analysis route ➟ Reasoning LLM ➟ analysis plan + SQL queries ➟ execution agent (gpt-4o) reviews, corrects, executes, and debugs before delivering results

✔ 𝐐𝐮𝐚𝐥𝐢𝐭𝐲

My (judgmental) ranking – reasoning & analysis

▸Sonnet – best by far. Brilliant derived variables & approach. Score➔ 100 (baseline). Sometimes too deep for 4o to execute, but superb for iterative analysis

▸R1 – close to sonnet ➔ 95

▸Gemini – excellent ➔ 85

▸o3-mini – hmmm... ➔ 50

✔ 𝐂𝐏𝐐 (𝐂𝐨𝐬𝐭 𝐩𝐞𝐫 𝐐𝐮𝐞𝐫𝐲)

Reasoning-based analysis (breakdown in comments)

▸o3-mini: ~8.5c

▸Gemini: ~11c

▸R1: ~13.5c

▸Sonnet: ~20.5c

𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞: up to ±50% on the same query.. models evolving...and variances coming down.

𝐋𝐚𝐭𝐞𝐧𝐜𝐢𝐞𝐬: mostly 1-4 mins, sometimes 10+ mins....time of day matters – peak vs. off-peak. Gemini the fastest.

✔ 𝐂𝐏𝐐– Regular Queries

▸4o-mini: ~0.10c

▸4o: ~1.5c

4o-mini the workhorse; 4o when it stumbles...Gemini may take over

𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞: ±20% – stable in live deployments

𝐋𝐚𝐭𝐞𝐧𝐜𝐢𝐞𝐬: 15 sec to 3 min depending on query complexity and time of day.

✔ 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

▸o3-mini & Sonnet – high reliability -negligible API failures

▸Gemini – high nowadays...but would like to see for some time

▸R1 – low - API failures & latency spikes. Improving- likely temporary. Alternate hosting options available.

✔ 𝐃𝐞𝐦𝐨𝐞𝐝 𝐄𝐱𝐚𝐦𝐩𝐥𝐞

▸Scoring & Ranking of Indian Banks - Credit Card Segment

▸Data Mart & Profile Summary for 1M Cust + 10M Trans.

✔ 𝐒𝐐𝐋 𝐄𝐫𝐫𝐨𝐫𝐬 / 𝐀𝐏𝐈 𝐅𝐚𝐢𝐥𝐮𝐫𝐞𝐬 / 𝐃𝐚𝐭𝐚 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧𝐬?

See detailed video guide - for live debugging / error catching (link in comments)

✔ 𝐒𝐨𝐮𝐫𝐜𝐞 𝐂𝐨𝐝𝐞𝐬, 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 & 𝐁𝐮𝐢𝐥𝐝 𝐆𝐮𝐢𝐝𝐞

5 repos + 7 Flowise schemas + video build guide. Links in comments

✔ 𝐂𝐚𝐯𝐞𝐚𝐭𝐬 & 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬

Lots of them...plus tips...check comments...

𝐂𝐚𝐯𝐞𝐚𝐭𝐬, 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬 & 𝐓𝐢𝐩𝐬

▸ Reasoning estimates: – ~100 queries across 4 reasoning agents (1-3 iterations per request. 1 iteration = 1 query).

▸ Regular queries: Based on months of live usage (API calls, automation, web scraping, NL-to-SQL via custom UIs).

▸ Use case-specific: Estimates apply to queries demoed in the video.

▸ High variability for same query: expect to come down as LLMs stabilize

▸ Critical to estimate costs for your own use case.

▸ Check actual billing – Pen-and-paper token math is unreliable.

▸ Time-based variability – Example: r1 costs were very high a few weeks ago but are now more reasonable—even though rack rate pricing is unchanged. Be mindful.

▸Prototype app - live working prototype.

𝐂𝐏𝐂 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧- 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 & 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬

▸ o3-mini: ~8.5c (reasoning + execution)

▸ gemini-2.0-flash: ~11c (reasoning = free tier, execution = 11c). Paid tier is cheaper than gpt-4o-mini (~0.10c additional).

▸ r1: ~13.5c (reasoning = 4c, execution = 9.5c)

▸ sonnet-3.7: ~20.5c (planning = 11.5c, execution = 9c)

𝐂𝐏𝐂 - 𝐫𝐞𝐠𝐮𝐥𝐚𝐫 𝐪𝐮𝐞𝐫𝐢𝐞𝐬

▸ gpt-4o-mini – ~0.10c (my workhorse – solid performance, solid pricing)

▸ gpt-4o – ~1.5c (I shift to gpt-4o if gpt-4o-mini stumbles)

▸ sonnet – With 3.5, I used to get ~2.5c. With 3.7, costs are now much higher despite the same token pricing—likely a temporary issue.

𝐖𝐨𝐫𝐤𝐡𝐨𝐫𝐬𝐞 𝐋𝐋𝐌: 4o-mini default; 4o when it stumbles. Flash2 may take over—better performance, quality, and cost, with improved reliability over last year’s Gemini.

𝐃𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐕𝐢𝐝𝐞𝐨 𝐆𝐮𝐢𝐝𝐞

Demo, build guide, architecture, API call flows, error catching, repo walkthrus and more.

https://www.youtube.com/watch?v=hqn3zrdXVSQ

𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬 & 𝐒𝐜𝐡𝐞𝐦𝐚𝐬

Main Repo

With step-by-step build guide & links to other repos

https://github.com/amararun/shared-rexdb-auth-embed-v2

Agents Schemas - Flowise

In docs folder in Main Repo