Dropstone is not a foundation model. It is a runtime that turns open-weight models into functional agents, and the version number tracks the integration cycle rather than the weights. Each cycle we evaluate the strongest open-weight models available and ship whichever wins. In 1.7 all three tiers changed: Fast moves to DeepSeek V4 Flash 0731, Pro to GLM-5.2, and Heavy to Kimi K3. The headline is that Kimi K3 scores 57.1 on the Artificial Analysis Intelligence Index — the highest of any open-weight model, and the first open model to clear GPT-5.5. It does not catch the closed frontier. Claude Opus 5 sits at 60.7. We will come back to that gap rather than skip past it. Heavy runs Kimi K3. Pro runs GLM-5.2. Fast runs DeepSeek V4 Flash 0731. The Pro change is the one most users will feel. GLM-5.2 was 1.6's Heavy tier — a 744-billion-parameter model, roughly 40 billion active per token, MIT licensed, 1M context. It is now the Pro default, which means the model that was gated to paid plans six weeks ago is available on every plan including free. Moving it down a tier is not a demotion of the model. It is a statement that what counted as our top tier last cycle is now our baseline. This is the part of the release we nearly under-reported, and it is the largest single move in it. DeepSeek V4 Flash 0731 shares its architecture and its price with the V4 Flash we shipped at 1.6 — 284B total, 13B active, 1M context, same rate card. But it is a retrained model rather than a point revision, and Artificial Analysis measures it at 50.0 on the Intelligence Index against 40.3 for its predecessor. Ten points is a bigger move than anything else in this release, Heavy included. Three things follow. Fast is now 6 points ahead of DeepSeek V4 Pro, a model a tier above it in its own family, while activating 13B parameters per token. Fast is 1.1 points behind our own Pro tier, and both are free on every plan, so the capability floor of a free Dropstone account moved to within a point of its ceiling. And the top three open-weight models on the index — Kimi K3 at 57.1, GLM-5.2 at 51.1, V4 Flash 0731 at 50.0 — are Dropstone Heavy, Pro and Fast. We did not arrange the lineup to produce that sentence. The three models we selected independently on capability are the three that occupy those positions. At 1.6 we described Fast as a latency tier and made no capability claim for it. That description is no longer accurate, and we would rather correct it than let it stand. Three closed models are ahead of the best open-weight model in the world. Claude Opus 5 leads Kimi K3 by 3.6 points, Fable 5 by 2.8, and GPT-5.6 Sol by 1.8. Anyone whose work concentrates in the hardest band should weigh that. What changed is the size of the premium. At 1.6 the equivalent gap was 8.8 points, and at that distance the frontier was a different class of tool. At 3.6 points it is a margin most workloads will not notice, and the ones that will are identifiable in advance. The gap narrowed by more than half in one cycle. It did not close, and we are not going to describe it as though it did. DeepSeek published nine agent benchmarks alongside the 0731 retrain, including a DeepSWE score of 54.4 against 7.3 for the preview. We are not reporting those as measurements. They are vendor-run, DeepSWE used a harness that was not public at the time of writing, and no third party has reproduced them. The reason to be careful is visible in the one benchmark that appears on both lists. DeepSeek reports Terminal-Bench 2.1 at 82.7. Artificial Analysis measures 79. We use 79. Where a vendor number and an independent number disagree, the independent number is the number — and we would rather publish the smaller one and be right. The same rule retires Kimi K3's own coding benchmarks, which run on Moonshot's harness. We exclude them entirely rather than discount them, which means Heavy has no directly comparable coding-specific score this cycle. We would rather have that hole in the report than fill it with something we cannot stand behind. Fast and Pro are both text-only. Heavy has a native vision encoder but is gated to paid plans. So an image attached on any tier, on any plan including free, is routed to Kimi K2.7 Code, which has one. K2.7 Code was 1.6's Pro tier and is no longer selectable in 1.7, but it stays in the routing map for exactly this reason. That routing is what keeps screenshot-to-code working without a paid plan, and it is invisible: you attach an image to whichever tier you were already using and it is handled. The models swap every cycle. The runtime guarantees do not. Inference runs exclusively on US-hosted infrastructure. Every tool call, file edit, shell command and API call sits behind an approval gate before it executes, regardless of which model is behind the wheel. That boundary lives in the runtime, not the weights, which is what makes it hold when the weights change underneath it — as they just did on all three tiers at once. We did not train the base models. Kimi K3, GLM-5.2 and DeepSeek V4 Flash 0731 are third-party open-weight models. We select, host and integrate them; we cannot audit their training data or weights, and we do not pretend to. We changed the primary benchmark axis this cycle, so the 1.7 numbers are not continuous with 1.6's SWE-bench Pro numbers and should not be compared against them. We think the new axis is better evidence and we have published our reasons, but a reader is entitled to treat a mid-stream axis change skeptically, and we would rather flag that than have it noticed. Index scores move. Artificial Analysis re-runs models and revises its index; everything here is stamped v4.1, July 2026. And the Frontend Code Arena result predates Claude Opus 5, which has no score on it yet — if Opus 5 ranks above K3 when it is scored, that result supersedes ours and we will say so. Dropstone 1.7 is live today across Fast, Pro and Heavy. Fast and Pro are free on every plan; Heavy requires Pro or Max, because a single long-horizon Heavy task can consume a free account's entire weekly allowance. Full benchmarks, methodology and the complete limitations list are in the technical report at https://blankline.org/research/dropstone-1-7.