# A3M Router - Twitter/X Thread

> Meta: 12-tweet thread. Each tweet under 280 chars. Post in order with 30-60 second gaps. Schedule for Tuesday 9AM EST or Thursday 11AM EST for max dev engagement.

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## 1/

I benchmarked 47 LLM providers against 12K+ real queries. Here's what I found 🧵

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## 2/

Most apps send EVERY query to GPT-4. That's like hiring a senior architect to write a "Hello World."

47% of real queries are simple Q&A. They don't need a $0.03/token model.

I built an intelligent router to fix this.

---

## 3/

The numbers:
• 47 providers tested
• 12,847 real queries
• ~$3,200 spent on benchmarking
• Open source, MIT license

Every stat comes from actual traffic, not synthetic benchmarks.

#LLM #AI

---

## 4/

Here's the insight that changes everything:

You don't need ONE model. You need a CURATOR that reads each query and picks the cheapest model that still delivers quality.

Simple question? Route to a fast, cheap model.
Complex reasoning? Escalate to GPT-4/Claude.

That's it.

---

## 5/

The routing logic in practice:

```python
result = router.route(
    prompt="Explain async/await",
    complexity="auto"  # router decides
)
# → sends to Groq/LiteLLM (fast, cheap)
# → NOT GPT-4 ($$$ overkill)
```

3 lines. 70% cost reduction.

#WebDev

---

## 6/

Punchy stat: Groq is 50x cheaper than GPT-4 and 5x faster for simple queries.

If your app handles 10K requests/day, that's the difference between $900/mo and $18/mo.

Same quality for 94% of use cases.

---

## 7/

The surprise finding: Chinese providers are legit.

GLM-4 and MiniMax beat GPT-4 on multilingual tasks (Hindi, Bengali, Hinglish) at 1/10th the cost.

If you're building for global audiences, you're overpaying by ignoring these.

---

## 8/

Plot twist: free tiers actually work.

CommandCode and OpenCode handle ~15% of dev queries with zero cost. Zero.

The router falls back to free providers first. If they pass quality threshold, you pay $0.

That's not a typo.

---

## 9/

Screenshot: a real dashboard showing query distribution across providers.

→ 47% → fast/cheap tier (Groq, free providers)
→ 38% → mid tier (Claude Haiku, GLM-4)
→ 15% → heavy tier (GPT-4, Claude Opus)

Cost drops 70%. Quality stays flat.

#OpenSource

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## 10/

How the router works under the hood:

1️⃣ Classify query complexity (keyword + embedding similarity)
2️⃣ Match to provider tier based on historical accuracy
3️⃣ Execute with automatic retry + fallback
4️⃣ Cache repeated queries

Average routing decision: <2ms.

---

## 11/

```bash
npm install adaptive-memory-multi-model-router
```

That's it. Works with OpenAI, Anthropic, Groq, Gemini, Cerebras, and 40+ more providers out of the box.

872 weekly npm downloads. MIT license. No vendor lock-in.

---

## 12/

The future of AI infra isn't finding ONE perfect model.

It's building systems that know WHICH model to use, WHEN.

That's what A3M Router does.

Try it: `npm install adaptive-memory-multi-model-router`

GitHub: https://github.com/Das-rebel/adaptive-memory-multi-model-router

#LLM #AI #WebDev #OpenSource

---

## Posting Notes

- **Best times:** Tue 9AM EST, Thu 11AM EST (highest dev engagement)
- **Thread spacing:** 30-60 seconds between tweets
- **First tweet:** Pin for 24 hours after posting
- **Quote tweet:** After 2 hours, quote the first tweet with: "50k+ queries routed so far. The data speaks for itself."
- **Engagement:** Reply to every response in first 2 hours
- **Follow-up tweet (next day):** "Update: [X] developers tried A3M Router after the thread. Here are the most common questions..."
- **Images to attach:**
  - Tweet 9: Screenshot of real query routing dashboard showing the 47/38/15 split
  - Tweet 10: Code snippet screenshot of the routing logic (dark mode, syntax highlighted)
  - Tweet 5: Code snippet screenshot of the 3-line usage example
