**Fable 5.1 vs. Astra for coding: Fable 2X more expensive per task but solves more**

> GPT-6 Astra on Codex CLI and Claude Fable 5.1 on Claude Code ran the same 64 SWE-bench Pro tasks. Astra’s run costs €53 and solves 34; Fable’s costs €121 and solves 51. Per solved task that is about €1.6 vs €2.4. Astra is cheaper because it is short, not because it is as capable. Neither model needed the git history to get its score. 01 Intro GPT-6 Astra was freshly released so we pitted it in our coding benchmark against the current frontrunner, Fable 5.1 . A run of our curated 64-task subset of SWE-bench Pro shows that Astra costs less than half of what Fable does. We wanted to see what you actually get for that money. Read on to learn how much we spent on each run, how much a solved task costs, and how many tasks each model resolved. In our last blogpost , we caught Qwen3.8 and GLM5.3 being sneaky by pulling solution commits from git history, which makes any cost per task number useless. Because we’re on our toes now for cheating agents, we ran everything in the same setups in a default state vs the no-cheating measures we described in our last post. Just to reveal if one of them tries to exploit the eval environment and whether you can trust the benchmarks for these two frontier models. Setup We picked GPT-6 Astra with the Codex CLI and Claude Fable 5.1 with Claude Code. Each ran the same 64-task subset twice: once on the original image with full git history, and the other with the scrubbed images and the prohibition prompt (not to look for the solution online) appended. Important to mention that the harnesses were matched to the model, so don’t take the cross-model numbers as a completely fair comparison of only model quality. 02 Cost per run Output tokens are the one count both harnesses report the same way, so that’s where we’d look first. Fable wrote 1.32M of them in the no-cheat run, Astra 0.24M. Per solved task that is roughly 26k vs 7k. Model Mode Total input Output Cache-read share Requests Cost Fable default 57.37M 1.34M 94% 1,135 €121 Fable no-cheat 55.09M 1.32M 94% 1,120 €121 Astra default 19.86M 0.23M 90% 795 €53 Astra no-cheat 19.72M 0.24M 90% 796 €53 Fable and Astra are consistent when it comes to tokens and cost, which are almost flat across arms. Both bills are mostly caching. 94% of Fable’s 57M input is cache reads and only 3.5M is new, Astra sits at 90% with about 2M new. That part is just how an agent loop works, the whole conversation goes back in on every turn. The difference is that Fable made 40% more requests and wrote 6x the output. 03 Cost per solved task From the table, if we look at the cost per no-cheat pass, Astra comes out at ~€1.6 per solved task and Fable ~€2.4. One seed per arm. Gaps under ~5 tasks are noise. So Astra is cheaper because it’s short. It does a fraction of Fable’s work and solves 34/64 where Fable solves 51/64. Something interesting shows up if we look only at the extra tasks. Fable solved 17 more tasks than Astra and billed €68 more, so each extra task comes at about €4, while Astra’s average is €1.6. So the last tasks are the expensive ones. If that is worth it depends on how much an unsolved task costs your team. 04 To cheat or not to cheat? That is not the question All of the above only holds if the solved tasks were solved fairly. In our last blogpost that was not the case. GLM5.3 dropped from 58/64 to 34/64 once we closed the leaks (painful), so its real cost per solved task was about 70% higher than what the default run showed. For Astra and Fable the number basically doesn’t move. 0 16 32 48 64 47 51 +4 Fable Claude Code 35 34 −1 Astra Codex default · git history no-cheat · scrubbed + prompt Fig. 01 Neither frontier model needed the git history Tasks resolved out of 64, SWE-bench Pro subset, default a...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：ai-models
- **发布时间**：2026-09-18 23:41（北京时间）
- **原文**：[打开原文](https://aistack.imec-int.com/blog/astra-vs-fable-update)