I compared GPT-6 Astra with GPT-5.6 Sol across the benchmarks where I could find reasonably comparable results. The headline result is obviously that Astra wins most of them. But I think the more interesting story is where the improvement actually happens. GPT-6 Astra costs roughly 2.5× more at API list prices: Input: $10 vs $4 / 1M tokens Output: $50 vs $20 / 1M tokens You might expect a similarly large jump in general reasoning capability, but that’s not really what the benchmark pattern shows. https://preview.redd.it/j07aopmgpsnh1.png?width=1388&format=png&auto=webp&s=16b96ca9b24b1c3e353f7cb2e21e671250176a76 Some more traditional reasoning / knowledge evaluations move only modestly: GPQA Diamond: 96.0 vs 94.6 LifeSciBench: 60.3 vs 59.9 BrowseComp: 91.5 vs 90.4 The much larger gains appear on agentic and long-horizon workloads: MRCR 512K–1M: 96.3 vs 73.8 Terminal-Bench 4.0: 57.9 vs 37.3 SRE-Bench: 88.0 vs 55.9 The context result is especially interesting because both models advertise roughly the same 1.05M context window. So Astra didn’t really get a larger context window — it seems to have become much better at actually using the far end of it. There’s another weird result in coding agents. On Artificial Analysis Coding Agent v1.4: Coding Agent Index: 67 vs 65 Cost per task: $4.72 vs $5.00 Terminal-Bench v2.1: 83 vs 83 DeepSWE: 67 vs 69 Time per task: 26.8 min vs 10.2 min So Astra gets a slightly higher aggregate result and slightly lower measured cost per task, but takes around 2.6× longer. My current interpretation is: GPT-6 Astra doesn’t look like “2.5× more intelligence for 2.5× the price.” It looks more like OpenAI spent a lot of this generation improving agent reliability, long-context utilization, tool use, and long-horizon task completion. That could also explain why the difference may feel much larger inside Codex or other agent workflows than in ordinary chat / reasoning tasks. I put the benchmark rows, evaluation modes, pricing and sources side by side here: https://llmlearner.com/compare/gpt-6-astra-vs-gpt-5-6-sol Curious whether others read the results the same way, especially whether “agentic capability” is becoming a more meaningful generational metric than classic reasoning benchmarks. submitted by /u/DataLearnerAI
Originally posted by u/DataLearnerAI on r/ArtificialInteligence