SOTA parade: GPT-6 Astra, Claude Fable 5.1, Muse Spark 1.3, Gemini 3.8 Flash and the post-train Qwen 3.8 Max 0902.
Muse Spark 1.3
https://research.meta.ai/blog/introducing-muse-spark-1-3
On their benchmarks it looks beautifully straight SOTA, but the real results don't add up: it loses skill instructions, instead of a targeted edit it may rewrite the whole file, generated websites look rough. Just like models a year ago. Separately, the cheap contributor pricing continues as dumping. Meta writes that an open-weights release for Muse Spark is ahead.
Gemini 3.8 Flash
https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/
No Pro for a long time now, this is the third Flash update in six weeks, tuned for long agent coding sessions (which suits us), available immediately in API, AI Studio, Antigravity. The cheap price was kept until the end of the year. It can work in small iterations — "works harder", but that's exactly why on hard tasks it eats more tokens, Google itself says so. The Cyber version is only for trusted defenders.
Claude Fable 5.1
https://www.anthropic.com/claude/fable
Improved to Fable 5.1 (generally available) + Mythos 5.1 (limited). Expensive, but top. They say it was taught not to report fake success and not to cut corners like disabling a failing test. Long autonomous runs need less babysitting, review is faster. Its writing became more human, but it invisibly marks text (watermarking), on HN they separately praise that the templated feel is gone.
GPT-6 Astra — finishes long tasks, but eats your limit
https://openai.com/index/gpt-6-astra/
As of September 7, GPT-6 Astra has been publicly available for only a few days. OpenAI positions it not so much as a model for "generating working code" but for independently going through the whole software engineering cycle. Very cool game demos, plus the ability to "take over" Blender and Unity or Godot via computer use. It's all long and expensive, but it gets to the result.
The docs explicitly emphasize: complex reasoning, coding, computer use, research and long multi-step tasks. Astra can work with a repository, run programs, see results, fix errors, continue work after requirements change and, in Codex, even ask questions asynchronously without stopping independent parts of the work.
Astra has 1M tokens, but the actually available context in a specific Codex environment can be much smaller. OpenAI also introduced a new context-preservation mechanism: instead of the usual constant compaction of old sessions, the model can keep notes between windows, with previous context windows remaining searchable.
In Terminal-Bench 4.0 data there is almost no gap between Astra and Fable 5.1. But Astra itself feels more autonomous and like one that understands the repository (although not always). Excessive usage/token budget consumption is already noticeable. On Reddit there are several reports of Astra burning a significant part of the five-hour limit in literally one session. In my Codex on a Plus subscription it ate the 5-hour limit doing a simple task in less than 15 minutes, I suspect that window was tightened even further.
In today's usual way of working the programmer is an AI operator: prompt → got code (diffs) → checked → threw in fixes. With Astra another mode becomes increasingly realistic, where the programmer sets intent, constraints and completion criteria, after which the model unwinds the process itself. The main upgrade in Astra is not that it generates unbroken code, but that it is increasingly often (but not always) able to drive the whole task to completion.
#newllmmodel #openai #gpt6 #fable #musespark #gemini #qwen