GPT-6 Astra vs Fable 5.1: What They Actually Cost to Run
A token-cost breakdown of GPT-6 Astra ($198) versus Fable 5.1 ($113) on identical coding benchmark tasks, separate from quality scoring.
Running GPT-6 Astra through a full coding benchmark cost about $198 in tokens. Running Fable 5.1 through the same tests cost about $113. That’s roughly 75% more spent on Astra, and in one independent tester’s results, it delivered fewer preferred outputs across a set of larger app-building tasks despite scoring close to Fable on a smaller benchmark.
This gap matters because token spend and benchmark score don’t move together. Astra placed third on a KingBench 3 leaderboard at 90%, just behind Fable 5.1 (92.5%) and GLM 5.3 (91.25%). That’s a two-point difference on paper. But once the same models were pushed through four bigger, longer-running app builds, the cost and quality gap widened in ways the leaderboard score didn’t capture.
TL;DR
- Astra cost $198 in tokens against Fable 5.1’s $113 across an identical set of coding tests, a difference of about $85 or roughly 75%.
- The KingBench 3 score gap was small: Astra scored 90% (72/80) versus Fable’s 92.5% (74/80), putting Astra third on the leaderboard behind Fable and GLM 5.3.
- Larger, longer-running builds showed a bigger gap than the short benchmark did, with Astra failing key functionality in a terminal movie tracker and an Obsidian-style note app.
- Astra ran through Codex with Ultra Thinking, and Fable ran through Veridant, so the cost and quality differences reflect specific tool setups, not just the raw models.
- Astra showed recurring design habits, including a preference for green, landing-page style layouts, and generic card/grid UI, which the tester says can add unnecessary complexity to simple requests.
- Subscription plan value wasn’t part of this comparison, these are pay-per-token costs, and a separate plan-based comparison could change the value picture for Codex specifically.
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How much more expensive is Astra than Fable 5.1?
Across the tester’s full run, spanning eight short KingBench 3 tests plus four larger “long horizon” app builds, Astra came in at about $198 in tokens. Fable 5.1 came in at about $113. That’s an $85 difference, or close to 75% more spent on Astra for the same set of tasks.
The tester is careful to frame this as a specific result from a specific setup, not a universal pricing comparison between the two models. Astra was run through Codex with Ultra Thinking enabled. Fable 5.1 was run through Veridant. Agent scaffolding, thinking level, and how much iterative work each setup does all affect token spend, so the dollar gap reflects these particular tools and settings, not necessarily what every user would see with different configurations.
Does the extra cost buy better results?
Not consistently, according to this testing. On the eight short KingBench 3 tests, the two models were close. Astra won three tests outright (a folding 3D table animation, a panda SVG illustration, and a 3D wristwatch), Fable won three (an elevator simulation, a 3D contact lens case, and a bow-and-arrow game), and they tied on two (a math permutation problem and a Gemma fine-tuning task). The final tally was 72/80 for Astra versus 74/80 for Fable, a two-point spread.
The bigger divergence showed up in the four long horizon tests, which asked each model to build more complete applications rather than isolated demos:
- A terminal-based movie tracker pulling from the TMDB API: Astra’s version had flickering posters, broken layout, and a TMDB integration that didn’t actually work, meaning the core search function failed. Fable’s version rendered cleanly and worked as intended.
- A poster creator and printer with a 3D preview: both models handled this well. The tester called it a tie, though Astra’s visual style leaned into a generic grid layout.
- A 3D digital Blu-ray shelf pulling artwork and color data from TMDB: both versions worked, but Fable’s was judged more realistic with better animation. A small win for Fable.
- An Obsidian-style notes app with inline image generation and an agent that could read open files: Astra made unprompted design decisions, shipped cosmetic features that didn’t function, and the agent integration didn’t work at all. Fable’s version worked closer to what was requested.
Across those four builds, Fable took two clear wins, one smaller win, and one tie. That result diverges more sharply from the near-even KingBench 3 score, and it lines up with the cost gap: more money spent on Astra, fewer usable results.
Why did the gap widen on bigger projects?
Short, well-defined benchmark tasks (draw an SVG, solve a math problem, animate a simple UI) tend to have a narrower range of ways to fail. Longer, multi-part builds have more surface area: API integrations need to actually connect, agents need to actually respond, and UI choices need to hold up across window resizes and real user interaction.
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The tester’s Astra failures clustered around exactly these integration points. The TMDB search not working in the terminal app, and the Open Code SDK agent not functioning in the notes app, weren’t cosmetic issues. Both undermined the core purpose of the app. Meanwhile Astra’s stronger results (the folding table animation, the wristwatch, the Blu-ray shelf’s 3D placement) tended to be self-contained pieces of functionality rather than systems that depended on multiple things working together.
This suggests the KingBench 3 score, while a reasonable signal for isolated coding ability, may not fully predict how a model performs on realistic, multi-step app development, which is also where token costs accumulate fastest.
Is Astra worth the extra token spend?
Based on this testing, not for this tester’s workflow. The combination of a higher dollar cost and a lower hit rate on the larger, more representative builds made Fable 5.1 the preferred choice for most coding work. The tester also flagged Astra-specific habits that add friction: a tendency to default to landing-page layouts even when none was requested, recurring green color schemes, generic card and grid UI patterns reminiscent of older GPT model outputs, and a tendency to overcomplicate simple requests rather than ship short, direct code.
There’s an important caveat on the cost side. This comparison used pay-per-token pricing through Codex and Veridant respectively, not subscription plan pricing. The tester specifically noted an interest in comparing subscription plan usage separately, since Codex’s plan-based pricing could offer better value even if the tester still prefers Fable’s raw output quality. That’s a distinct question from the per-token cost comparison covered here, and it wasn’t tested in this round.
Frequently Asked Questions
What is GPT-6 Astra?
GPT-6 Astra is a coding-capable AI model that was benchmarked here through Codex with Ultra Thinking enabled. In this testing it scored 90% on a KingBench 3 benchmark, placing third behind Fable 5.1 and GLM 5.3.
What is Fable 5.1?
Fable 5.1 is the comparison model in this testing, run through a tool called Veridant. It scored 92.5% on the same KingBench 3 benchmark, placing first on that leaderboard, and cost less in tokens across the full test suite.
Why did Astra cost more than Fable to run?
The exact cause wasn’t isolated in this testing, but likely factors include the Ultra Thinking setting used with Astra, differences in how Codex and Veridant manage token usage, and the amount of iterative work each setup performed on the longer app-building tasks. The tester noted that agent setup and thinking level can significantly affect spend independent of the underlying model.
Does a higher KingBench 3 score mean a model is better value?
Not necessarily. Astra and Fable were close on the short KingBench 3 tests (a two-point gap out of 80), but the gap in practical usefulness and cost widened substantially on longer, more complex app builds, where Astra had functionality failures in API integration and agent behavior.
Is this a fair comparison of the two models overall?
It’s a specific comparison under specific conditions: one tester’s prompts, run through Codex for Astra and Veridant for Fable, at a particular thinking level. Different agent scaffolding, subscription plans, or thinking settings could change both the cost and the quality results.
