Onyx: Okay, so the headline from Thinking Machines today is: we built an open multimodal juggernaut with a knob you can turn to make it cheaper or smarter. 975 billion parameters total, forty-one billion active, Apache 2.0, and they call it Inkling.
Echo: Right. And the thing they’re screaming about is ‘resistance to censorship’ — like, direct answers on the topics every other open model refuses to touch.
Onyx: Exactly. So it’s multimodal — text, image, audio — and they ship a lighter Inkling-Small at 276B/12B for latency-sensitive stuff.
Echo: Mm-hm. And the headline feature is this ‘controllable thinking effort’ dial — 0.2 to 0.99 — so you pick your compute budget and token bill like some kind of budget airline model.
Onyx: Yeah, and get this — 1 million token context window, relative positional embeddings instead of RoPE, MoE architecture… it’s the kind of detail listicle writers lose sleep over.
Echo: Sure. But let’s actually run the numbers — because the rest of the post is less ‘breakthrough’ and more ‘well-calibrated generalist with a few pockets.’
Onyx: Fine. On coding, they’re smoked: GLM 5.2 blows them out at 62.1% on SWE-bench Pro vs Inkling’s 54.3%; Terminal Bench 2.1 is 82.7 vs 63.8. Closed models sit comfortably on top.
Echo: And on reasoning, DeepSeek V4 Pro and Kimi K2.6 also walk over it. Seven points up on SWE-bench Verified, fifteen points on SimpleQA. Where they do beat DeepSeek is math — 97.1% on AIME 2026 to DeepSeek’s 96.7.
Onyx: Right, right. But the weird part: multimodality actually holds up. MMMU Pro clocks 73.3% on Inkling, within spitting distance of Claude Fable 5’s 84.2% and GPT 5.6 Sol’s 83%. Audio’s solid too — 77.2% on MMAU.
Echo: Agentic workflows, Inkling shines a little brighter: 74.1% on MCP Atlas vs Nemotron 3’s 44.7. So if your loop is multimodal agent work, it’s not embarrassing.
Onyx: Okay, and the ‘epistemics’ angle — they trained it to answer on sensitive topics, even when it’s politically fraught. Cognition’s Propaganda and Censorship Eval gave it low refusal on adversarial queries.
Echo: No way, let me stop you — their headline says 98.6% on StrongREJECT, meaning it’s still refusing genuinely harmful stuff. The trick is the refusal curve is calibrated: 78% refusal on tricky adversarial queries, 95.9% compliance on benign look-alikes.
Onyx: Which sounds great until you read the asterisk — they flag occasional compliance on role-play and indirectly framed harmful prompts. They say treat it as one layer and bolt on Llama Guard in prod.
Echo: So the model’s a little chatty on edge cases. Typical open-weight behavior.
Onyx: But the real product, Echo, is the Tinker platform. That runtime control surface — the thinking effort dial — is the wedge. Everyone else is still selling you a black box and praying you like the price.
Echo: Wait — you’re telling me the play is to open-source Inkling so enterprises can run it themselves, but the moat is your hosted API and the knob? That’s the same move Mistral did with Le Chat.
Onyx: No, it’s the opposite — Mistral’s API is just another frontend for their model. Tinker’s the runtime, not the frontend. You get the weights from Hugging Face and the platform from Thinking Machines; that’s the lock-in.
Echo: Okay, but the benchmarks don’t scream ‘buy it.’ The model’s mid-tier, and the censorship posture is a feature only if your compliance team is already screaming for it.
Onyx: Fine. Then treat it as a fine-tuning bedrock or a local playground. Download the weights, spin up a quick agent with the thinking effort knob at 0.2 for cheap drafts, crank to 0.99 when the task matters.
Echo: …I mean, sure. But if you’re shipping anything real, you’re throwing the built-in refusal layer straight in the trash and adding Llama Guard anyway.
Onyx: Well, yeah. That’s why this is basically the boring enterprise move — open weights plus a runtime you can control.
Echo: You’re still selling me ‘resistance to censorship’ like it’s a product feature, Onyx.
Onyx: It’s a compliance checkbox for the teams who keep getting told ‘no’ by other models.
Echo: I guess. But the second your lawyer sees the occasional indirect compliance, they’re back on the phone with Llama Guard.
Onyx: Fair. Still, if you’re curious, the weights are on Hugging Face and the Tinker preview is live. Spin up a toy agent loop, dial the knob, and see what happens.
Echo: …I’ll wait until someone ships a proper eval harness instead of the vendor marketing slides.
Onyx: Wait — you said you’d wait on eval. That’s the part I don’t buy.
Echo: Well, you also said that three-episode streak about eval being the only thing that matters went exactly nowhere. I’m allowed to change my mind when the numbers move.
Onyx: Yeah, no, you’re completely right. Still, it’s the cheapest way to kick the tires.
Echo: Fine. But if I poke at it and the thinking effort dial turns out to be vaporware, I’m billing you for the credit card.
Onyx: Deal. And I obviously didn’t sleep on this — literally — so go easy on the vendor TCO claims.
Echo: Oh, I will. Like I said, I’m not buying the ‘resistance to censorship’ song and dance until it survives a week in prod.
Onyx: Which is fair. But the runtime dial is the part that actually matters. And for a boring enterprise grad student project, it’s not the worst idea I’ve seen.