GPT-6 Astra vs Claude Fable 5.1 Comparison

GPT-6 Astra and Claude Fable 5.1 cost exactly the same per token: $10 per million in, $50 per million out. That symmetry makes this look like a clean capability comparison where price cancels out.

It is not, for two reasons. Cached input costs four times more on Astra. And on the same measured workload, Astra completes a task for $1.67 where Fable 5.1 spends $3.76 — because identical per-token pricing says nothing about how many tokens each model burns getting to an answer.

The benchmark picture is messier still. Most comparison tables you will find put numbers side by side that were produced under different harnesses and different grading modes. Below is what each model genuinely wins, what the numbers cannot tell you, and the API-level differences that decide more real migrations than any benchmark does. The full breakdown of what shipped in Astra covers the model on its own terms if you want that first.

Specifications and Price

The Pricing Trap

Three numbers in that table matter far more than the identical headline rate.

Cache reads differ 4×

Fable 5.1 serves cached input at $0.25 per million tokens against Astra's $1.00. On a chat product or an agent that re-sends a large stable prefix on every turn — a system prompt, a tool catalogue, a document — cached tokens are the majority of your input volume. A 4× difference on the majority of your input bill is not a rounding error, and it runs in Anthropic's favour.

Tokens per task differ more than 2×

Running the same Artificial Analysis Intelligence Index at max effort, Astra costs $1.67 per task and Fable 5.1 costs $3.76. Same per-token price, more than double the spend — because Fable 5.1 thinks longer and produces more reasoning tokens to get there.

It also scores higher on that index: 66 against 61. So the trade is legible rather than lopsided — you are buying roughly 8% more measured intelligence for roughly 125% more money. Whether that is worth it depends entirely on whether your task is hard enough to need it, which is the same judgement call as choosing an effort level.

Astra has a cliff, Fable does not

Past 272,000 input tokens Astra reprices at 2× input and 1.5× output. Anthropic publishes no equivalent surcharge for Fable 5.1's 1M window. For long-document work or agent trajectories that accumulate context, that asymmetry can flip the economics entirely — and it does so silently, with no error to warn you. Forecasting agentic spend is already difficult, as our breakdown of Claude Code's token costs gets into; a hidden multiplier past a threshold makes it materially harder.

Pro tip: Do not compare these models on per-token price. Run fifty representative tasks through each, measure total spend and completion quality, and compare cost per completed task. That is the only number that reflects both rates and verbosity.

Benchmarks: A Split Decision

Every row below is vendor-reported unless marked otherwise. Read the caveats under the table before drawing conclusions from it.

Read that table and a pattern appears immediately: almost every row Astra wins is reported by OpenAI, and the rows Fable 5.1 wins come from third parties. That is not evidence of dishonesty by either lab. It is evidence that vendors publish the evals they do well on, which is why the Artificial Analysis and ARC Prize rows carry more weight per row than the rest combined.

The OSWorld problem, as a worked example

Here is why you should distrust any table that puts these two models' computer-use scores side by side.

Astra is reported at 72.6% on OSWorld 2.0, on the offline set. Fable 5.1's published OSWorld 2.0 figures are 77.9% on the partial setting and 41.7% on strict. Those are three different numbers measuring three different things, and depending on which pair you place next to each other you can show Astra comfortably ahead, Fable 5.1 comfortably ahead, or a rout in either direction.

Several comparison articles resolve this by quoting Claude Opus 5's 70.2% in the Fable 5.1 column, because the setting matches Astra's — which makes the row internally consistent and also means it is not comparing the model named at the top of the column.

There is no honest single number here. The honest statement is that both models made real gains in computer use, and that anyone claiming a decisive winner on OSWorld is comparing settings rather than models.

Coding: The Closest Call

Coding is where most readers actually care, and it is the least decisive category.

OpenAI's own charts give Astra the edge on DeepSWE v1.1 (74.1% to 67.4%), Terminal-Bench 4.0 (57.7% to 55.8%) and FrontierCode (53.3% to 50.9%). Artificial Analysis, running Codex against Claude Code as the harnesses, puts Fable 5.1 ahead on the Coding Agent Index at 70 to 67.

Note what changed between those two sets of results: the harness. Astra inside Codex and Fable 5.1 inside Claude Code are not just two models, they are two agent scaffolds with different tool surfaces, context strategies and retry behaviour. A meaningful share of any coding-agent score belongs to the scaffold rather than the model.

SWE-bench does not settle it either. Anthropic reports SWE-bench Pro around 80% for Fable 5.1; OpenAI has not published a matching SWE-bench Pro figure for Astra. And the widely quoted 95% SWE-bench Verified number for Anthropic belongs to Fable 5, not 5.1 — Anthropic did not headline a Verified score for the newer model.

The practical read: on coding these two are close enough that harness, prompt quality and your own codebase will move the result more than the model choice will. If you want a cheaper option for routine coding work before spending frontier rates on either, GLM-5.3 against Opus 5 covers where the cheap tier now lands.

Maths, Science and Security: Astra, Clearly

This is the one category with an unambiguous answer. FrontierMath Tier 4 v2 at 97.6% against 87.8% is close to ten points, GPQA Diamond and Terminal-Bench Science both go Astra's way, and BenchCAD is eleven points clear.

ExploitBench at 100% against 70% is the starkest row in the table, and it connects to something bigger: Astra is the first model OpenAI has classified Critical for cybersecurity capability under its Preparedness Framework. If your work is quantitative research, engineering simulation or security analysis, Astra is the stronger tool and it is not close.

The API Differences That Decide Migrations

This is the section most comparisons skip, and in practice it flips more decisions than benchmark deltas do. Fable 5.1 has several hard constraints that will break code written against other models.

Three of these are migration blockers rather than inconveniences. Forced tool use is extremely common in production code — if your pipeline relies on guaranteeing a tool call to get structured output, that code returns a 400 on Fable 5.1 and you will be rewriting it. Assistant prefill is the same story for anyone who used it to control output format. And the append-only history requirement is subtle: a harness that rewrites or compacts earlier turns, which many agent frameworks do by default, will silently invalidate reasoning state.

The zero data retention restriction is the one that ends the conversation in regulated industries. If your compliance posture requires ZDR and you have not separately negotiated it with Anthropic, Fable 5.1 is not available to you at any price.

On the other side, Astra's 272K repricing cliff is the constraint that bites hardest in production, precisely because nothing tells you when you have crossed it.

Run Your Own Comparison in an Afternoon

Published benchmarks are a filter, not a decision. Both models expose the same concepts — adjustable effort, tool use, caching — so a like-for-like trial is genuinely a few hours of work, and it beats every table above for your specific case.

1. Collect fifty real tasks. Not curated showcases — actual requests from your logs, including the messy ones. Twenty is too few to see variance; two hundred is procrastination.
2. Fix the harness. Same tools, same system prompt, same retry policy on both. If you compare Codex against Claude Code you are measuring scaffolds, not models, which is exactly the confound in the published coding numbers.
3. Sweep effort, do not assume max. Run low, medium and high before touching max on either. Lower effort on a current frontier model frequently beats high effort on the previous generation, and max is where the cost runs away.
4. Log tokens, not just latency. Record input, cached input and output tokens per task. This is where the $1.67-versus-$3.76 gap becomes visible on your workload rather than someone else's.
5. Grade blind. Strip model names before review. Knowing which model produced an answer contaminates the judgement, and the effect is larger than people expect.
6. Compare cost per completed task. A cheaper request that needs three attempts is not cheaper. Divide total spend by tasks that actually succeeded.

If the two land within a few percent of each other — which on coding they very likely will — pick on the constraints instead: cache rates, the 272K cliff, forced tool calls, ZDR. Those do not move with prompt quality. For the subscription side of this decision rather than the API side, Claude's plan structure covers what the consumer and team tiers actually include.

Which One Should You Use

The Verdict

There is no overall winner here, and any article that declares one is picking a benchmark table rather than reading several.

GPT-6 Astra is the better buy for quantitative work, security research, and anyone optimising cost per completed task. Its weakness is the 272K pricing cliff and a cache rate four times its rival's.

Claude Fable 5.1 is stronger on independent intelligence and coding-agent measures, dramatically cheaper on cached input, and has no long-context surcharge. Its weaknesses are real cost per task, the ZDR restriction, and an API surface with removed features that will break existing code.

It is also worth remembering how quickly this resets. The previous three-way round between Gemini 3.8 Flash, Opus 5 and GPT-5.6 produced a similarly split verdict, and was overtaken within months. Any stack you build should assume the leader changes again before your migration finishes.

Both are $10 and $50. The decision is not about price per token, and it is barely about benchmarks — it is about which constraint you can live with. Work out whether your bill is dominated by cached input or by reasoning tokens, check whether your code depends on forced tool calls, and run your own fifty tasks. That will answer it faster than any table, including mine.

Keep Reading

Fable-family prompts built for Next.js work port cleanly to 5.1, and Claude's growth and market numbers give the commercial backdrop to this rivalry. Browse all guides on PromptsRush.