An Open Model, Partly Taught by the Rivals It Answers

The chronology matters because two of these events are easy to conflate. The Bridgewater/Tinker case study — a fine-tuned Qwen3-235B beating frontier models on financial-document classification — was published two weeks before Inkling existed. It demonstrates what fine-tuning on Thinking Machines' Tinker platform can do; it says nothing about Inkling's own performance, and no dedicated Inkling deployment case is publicly documented. It is included here because it is the platform-economics evidence behind Inkling's own fine-tuning story, not because it is about Inkling.

Thinking Machines states its own case plainly: Inkling is “not the strongest overall model available today, open or closed.” Its bet is a combination — multimodal capability, an efficient controllable thinking-effort dial, and availability on Tinker for fine-tuning — that makes it “a good open-weights base for customization,” not a leaderboard entry. The architecture: a 66-layer decoder-only transformer routing each token to 6 of 256 experts plus 2 always-active shared experts, hybrid local/global attention, and an encoder-free multimodal design — images patch-encoded, audio represented as dMel spectrograms, both projected into the same hidden space the decoder processes text in. The thinking-effort dial's technical range is 0 to 0.99; Thinking Machines' own published benchmark curves sweep it from 0.2 to 0.99, where Inkling reaches a given Terminal Bench 2.1 score at roughly a third the tokens of Nemotron 3 Ultra.

• 0–0.99thinking-effort dial — full technical range
• 30M+RL rollouts → spontaneous CoT condensation

Two independent measurements frame where Inkling landed. Data Gravity's own token-routing analysis: by May 2026, Chinese open-weight models held ~61% of tokens routed on OpenRouter, “four of the five most-used models are Chinese — and Meta's Llama, the open-weight leader two years ago, has fallen off the rankings entirely,” below 1% of routed volume. Artificial Analysis, the same day Inkling shipped: it is “the leading open weights release from a U.S. lab,” three points above the previous leader, Nemotron 3 Ultra. The complication is that Thinking Machines' own stated rationale does not frame it this way. Its manifesto, published five days before release, argues against concentrating AI development in a “central planning”-like handful of labs, quoting Hayek on dispersed knowledge — and never names China. The “Western answer” framing belongs to the independent market measurements above, not to Thinking Machines' own words. And Inkling's own post-training complicates whichever framing is used: its bootstrap fine-tuning drew on synthetic data from open-weights models “including Kimi K2.5” — one of the very Chinese models the market-share figures name.

The comparison Bridgewater and Thinking Machines publish jointly sets their fine-tuned model against six frontier models. The strongest of them on this task is GPT 5.5, at 78.2% average accuracy and $65.06 per thousand tasks; Claude Opus 4.8 scores 78.0% but costs $92.59, the most expensive in the set. The fine-tuned Qwen3-235B reaches 84.66% at $4.72 — 13.8× cheaper than GPT 5.5, the figure the post itself states, and 19.6× cheaper than Claude Opus 4.8. Two caveats travel with it: the study is a joint publication by the platform's vendor and its customer, with no independent replication, and it measures a Qwen3-235B fine-tune rather than Inkling.

Because open weights can be re-tuned to strip refusal training, Thinking Machines fine-tuned “helpful-only” variants of both Inkling and Inkling-Small — deliberately optimised to comply rather than refuse — and ran them against the same dual-use evaluations. Its own conclusion: “the helpful-only variants did not provide new uplift on CBRN and cyber tasks, and remained comparable to existing open-weight models.” Combined with the internal and external results, Thinking Machines concluded releasing Inkling did not add material risk beyond what already-downloadable open-weight models pose.

Artificial Analysis's own breakdown, published the day Inkling-Small shipped: it edges ahead of Inkling on Humanity's Last Exam (32% vs 30%) and GPQA Diamond (89% vs 87%) at under a third of the parameters, but trails on agentic work — τ³-Banking 15% vs Inkling's 24% — and scores negative on Artificial Analysis's own factual-calibration index, where Inkling scores positive, “meaning incorrect answers outweigh correct ones.” Both models were independently placed close together on the Intelligence Index (40 vs 41), which is a coarser summary than either of these individual results and does not on its own say which model suits which task.