Correctly if I’m wrong but isn’t inkling made by a very well funded startup from an ex - OpenAI exec iirc?

Yes, Mira Murati. They just raised $5B at $40B, after raising $2B at $10B last year.

If you cant beat Deepseek on price im not using it. I actually dont care about beating DS in capabilities, I dont think you need more intelligence than Deepseek for 99% of the things people work on, and I dont want LLMs to do everything, that kind of defeats the point. I just need a smart fuzzer and to offload some tedious busy work.

So why should I pay for 3-20x more for a few small percentage points in preformance. (I would prefer DS did some things better, but largely I can force my desired behavior), but I could only have access to Deepseek 4.1 and still beat almost everyone using opus/fable/astra on a productivity basis, I need automation tools not "do my entire job so Im worthless tools", and DS has already more than gotten there for me.

There’s still room to beat Deepseek in communicating clearly and following instructions to the letter (it’s very over-eager imo). I can put it in subagent but for main model, as far as open models go, there’s still no atlernative to GLM-5.3 Flash.

Not beating dsv4.1 at all

It doesn't beat DS v4.1, and they have so much more compute capacity than any Chinese company, yet still cannot even beat MiniMax M3.1 Flash (220B) despite having 2.7x more parameters (596B).

I wouldn't bet on the numbers of compute reported by Chinese labs. Those Singapore Nvidia orders had to go somewhere.

> yet still cannot even beat MiniMax M3.1 Flash (220B) despite having 2.7x more parameters

MiniMax M3.1 Flash is 428B total parameters, so the size ratio is more like 1.4x. Model card here: https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash

The number HF calculates from the safetensors metadata is often wrong (e.g. counting u8-packed 4-bit weights as half a weight each) so you need to take the number from the actual model card.

Are they distilling from the big labs or Chinese labs at all? In that case isn't it $1 trillion frontier CAPEX + Inkling's costs for the total costs.

"I bought this house with my own savings plus a small million dollar loan from mum and dad."

Everyone is building on everything that comes before.

Houses are not built from scratch either. The know-how, power, water, roads, materials…

Yes, and the sum total of those costs (that makes _some_ economic sense) end up on the total house price.

Is the same true for distillation?

If you’re going to do the cost math that way, be prepared to put a price on ~the total sum of all works created by humanity, which the frontier labs appropriated for their models.

In their last technical report (for the original Inkling model), they mentioned 'To bootstrap post-training, we ran an initial SFT on synthetic data generated by open-weights models including Kimi K2.5. The bootstrap accounts for a small fraction of compute, with the majority being employed for large-scale RL on synthetic and human-created environments.' and in their most recent one they said 'The H3 and H4 mixtures consisted mostly of trajectories sampled from earlier models including Kimi K3 and our own models, filtered for correctness and alignment.' So it does seem like traces from larger models do contribute. I think Microsoft is one of the few companies that has actively made an effort to avoid distillation with their MAI series.

I think Mistral also avoids distilling on other company's models.

domestic -> people live in antarctica. No domestic LLM for them.

No domestic good LLM for me, either.