From July 8 to 16, five frontier models — Grok 4.5, GPT-5.6, Muse Spark 1.1, Inkling, Kimi K3 — appeared in succession, the highest density ever. But when all five labs' capabilities have crossed the "good enough" line, the benchmark table is no longer a ranking, but a marketing choice. Build Fast with AI, after testing these five models through APIs and real workflows, found that what truly decides selection has become per-token price, license terms, fine-tunability, and the actual task you're running. Kimi K3 pushes "buy capability with scale" to the limit — 2.8T parameters, 1M context, native video input, scoring 91.2% BrowseComp and 93.5% GPQA Diamond as the open-source #1, at the cost of high pricing. GPT-5.6 Sol runs at 750 tok/s on its self-developed Cerebras hardware, with the tier split (Sol/Terra/Luna) directly covering the full price band from batch to flagship. Inkling is the only true Apache 2.0 open-weight on the leaderboard (975B total / 41B active), with an adjustable thinking-intensity knob, and Thinking Machines itself says it's "not the strongest model" — openly positioned as a base layer, not a leaderboard entrant. Meta Muse Spark 1.1 slams price down to $0.25 / $0.25, yet simultaneously takes the tool-calling SOTA on MCP Atlas with 88.1, supporting text / image / video / audio / PDF single-endpoint access — to date the cheapest and most multimodal agent brain. Grok 4.5 takes the differentiated path of X-platform-native search + 500K context, with $0.50 / $1.50 pricing plus independently Artificial-Analysis-verified intelligence index, holding the value tier. The price spread has already exceeded 12x, but the capability gap is only a few percentage points. In Build Fast with AI's test, Muse Spark spent 4 cents on a 3D house generation task, Fable 5 spent $1.82 — same output, 45x price difference. When a single leaderboard no longer defines winners, selection logic has to be rewritten: route 80% of daily traffic to Muse Spark 1.1, Grok 4.5, or GPT-5.6 Luna; save Sol or Fable 5 for the 20% real hard problems; as long as the task is repeatable, Inkling's fine-tuning path is a more cost-effective long-term investment than renting a closed-source model. July's real signal isn't that some model became a god, but after "the capability threshold" was crossed by all five at the same time, LLM competition has formally switched from competing on scores to competing on cost, license, and ecosystem — open weights are back at the center of the table, the price war has spread from per-token to per-completed-task, and customization has become the rarest moat.