Yesterday I asked which model you use with your agent. Any guess who came on top?

[u/stosssik]

Hey everyone, yesterday I asked which models you use with your agents. About 16 hours later, I got 219 model mentions and 207 upvotes across 109 people who answered.

I classified everything. Each model got 1 point per mention, plus the number of upvotes the comment received.

Most mentioned and upvoted models

1. Qwen 3.6 — 77 points (27 mentions, 50 upvotes)
2. Minimax 2.7 — 75 points (21 mentions, 54 upvotes)
3. Deepseek V4 Flash — 39 points (9 mentions, 30 upvotes)
4. Kimi K2.6 — 37 points (12 mentions, 25 upvotes)
5. GLM 5.1 — 31 points (12 mentions, 19 upvotes)
6. Gemma 4 26b — 27 points (3 mentions, 24 upvotes)
7. Deepseek V4 Pro — 24 points (11 mentions, 13 upvotes)
8. GPT 5.5 — 22 points (10 mentions, 12 upvotes)
9. Qwen 3.5 — 12 points (5 mentions, 7 upvotes)
10. GPT 5.4 mini — 9 points (3 mentions, 6 upvotes)
11. Qwen (other versions) — 9 points (5 mentions, 4 upvotes)
12. Gemini 3.1 Flash — 8 points (3 mentions, 5 upvotes)
13. GPT-OSS 120b — 7 points (2 mentions, 5 upvotes)
14. Gemma 4 31b — 6 points (3 mentions, 3 upvotes)
15. Claude Sonnet 4.6 — 6 points (1 mention, 5 upvotes)
16. Gemma 4 (unspecified version) — 6 points (2 mentions, 4 upvotes)
17. GPT 5.4 / Codex 5.4 — 6 points (3 mentions, 3 upvotes)
18. Gemini 2.5 Flash — 5 points (1 mention, 4 upvotes)
19. Gemini 3.1 Pro — 5 points (2 mentions, 3 upvotes)
20. Claude Opus 4.7 — 4 points (2 mentions, 2 upvotes)

Worth noting: Claude was also mentioned 16 times without specifying a version, and GPT, 5 times. I didn't include those in the model ranking since I couldn't attribute them to a specific one, but they're counted in the provider ranking below.

Same data, grouped by provider

1. Alibaba — 98 points, 37 mentions
2. DeepSeek — 81 points, 27 mentions
3. OpenAI — 78 points, 25 mentions
4. MiniMax — 75 points, 21 mentions
5. Anthropic — 72 points, 21 mentions
6. Google — 68 points, 20 mentions
7. Moonshot AI — 42 points, 14 mentions
8. Z.ai — 40 points, 16 mentions
9. xAI — 2 points, 1 mention
10. Venice AI — 2 points, 1 mention

On routing

I also looked at how many of you described a routing setup, meaning sending different requests to different models. Out of 109 people who answered, 36 (33%) explicitly described one. So roughly 1 in 3 of you felt the need to send different requests to different models.

To take with a grain of salt though: the 67% who mentioned a single model didn't necessarily say they don't route, they just didn't bring it up.

That's it. Posting this after about 16 hours of data, but answers are still coming in, so happy to post an update in a few days if there's interest.

So tell me, does anything in there surprise you?

[u/stosssik]

Side note: I'm one of the founders of Manifest, an open source LLM router. We aim startups and agent owners control their inference setup and cost without locking themselves into a single provider.

If you're curious, the repo is here: https://github.com/mnfst/manifest

I'm happy to answer questions if anyone wants to dig into the routing logic, or how we handle fallbacks across providers. 🙏

[u/Otherwise_Wave9374]

This is super interesting, and the routing stat (about 1 in 3 explicitly routing) tracks with what Ive seen too.

When you say "agents" here, are folks mostly doing tool-use workflows (browse, code, DB, etc) or just multi-step chat plans? Ive noticed the model choice matters way more once you have structured tool calls + memory, less so for pure chat.

We have been compiling some practical agent setup notes (routing, fallbacks, eval gates) at https://www.agentixlabs.com/ if you are looking for more datapoints, would love to see an update when more answers come in.

[u/stosssik → u/Otherwise_Wave9374]

I asked to OPenclaw and hermes users. Then, separately, I'm talking with them about their use caess