Thinking Machines launches Inkling 2 Small, a 594B-parameter MoE with 17B active and Apache 2.0 weights promised next week

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Thinking Machines launches Inkling 2 Small, a 594B-parameter MoE with 17B active and Apache 2.0 weights promised next week

Inkling 2 Small is a Mixture-of-Experts model with 594B total parameters and 17B active, supports image inputs and variable effort, and scores 31 on the Artificial Analysis Intelligence Index, above Thinking Machines' first generation.
It ships first through Tinker with closed weights, and the company says the full weights go out under Apache 2.0 next week after external safety testing and red-teaming finish.
Training used RL on hundreds of thousands of long-horizon coding tasks across multiple agent harnesses and context-compaction strategies, with an agentic RL grader that scored how the model approached a problem, not just whether its code passed.
On the published table it is second on FrontierCode v2.0 at 25.4% behind DeepSeek-V4.1-Flash-1017 (28.5%), and second on SWEBench Pro v2 Hard at 86.3% behind Kimi K3 (88.2%).
In the PMPP-Hard kernel optimization run it solved 54.25 of 69 tasks with four attempts and a 10-hour budget, behind GPT-6 Astra (63) and Kimi K3.1 Pro (60.5) and just ahead of GLM 5.3 (52.5).

Hacker News opinions

Is it SOTA yet? No, DeepSeek V4.1 Flash still wins FrontierCode and Vibe Code Bench. But the velocity these open weights labs are showing is undeniable.

The best open models have been good enough for most knowledge work for a few months now, and this might be the first US one that is too. Open weight Opus 5.5 within 12 months, I'm calling it.

Open weight Opus 5.5 in 12 months seems unlikely. Something in that ballpark, maybe. That would be great though.

Honestly refreshing that the team reporting the benchmarks isn't the clear winner in every row. They claim to beat GLM 5.3 Flash, which is impressive if it holds up outside the table.

Where's the cost? The whole point of DeepSeek flash 1017 is how cheap it is for how capable it is. AA lists Inkling at $0.30/$0.03/$1 and $1.18 per task, about double DeepSeek.

Nice domestic open weight model. A small team producing something that beats DeepSeek 4.1 plus a platform to fine-tune it, that's optimistic for the future.

Isn't Inkling made by a very well funded startup run by an ex-OpenAI exec? Let's not pretend this is a garage operation.

If you can't beat DeepSeek on price I'm not switching. I don't need more intelligence than that for 99% of tasks, I need a smart fuzzer and something to offload tedious work onto.

It doesn't beat DS v4.1 at all, and they have more compute than any Chinese lab, yet still can't beat MiniMax M3.1 Flash at 220B when they're running 594B.

The real story here is that training was entirely nvfp4. That's by far the best model ever trained at that precision and it explains how they moved so fast.

594B parameters and they still call it Small. My GPU racks are crying. You could maybe run a Q2-Q3 quant on 2x DGX Spark, so roughly $12k.

Why are we doing wine comparisons? Is there a way to strip the experts down so it's just good at code?

What if your coding project involves wine? You want the facts in the weights, not a web search. It also shows the model generalizes instead of benchmaxing.

I sent the same wine example to Claude Opus 5.5 medium and it failed. Inkling 2 Small got it.

Last Inkling Small was 266B and now this is more than double. Their own small tier keeps growing.

I don't like that they squatted on the name of an old company with a real legacy in AI. Trademark was cancelled so there's nothing to be done, but poor form.

Looks better than Mistral 4 across the board, and the presentation on their site is genuinely well done.