Claude Fable 5.1 vs GPT-6 Astra 2026: Same $10/$50 Price, Opposite Trade-offs
Claude Fable 5.1 vs GPT-6 Astra 2026: Same $10/$50 Price, Opposite Trade-offs
Verdict first: Claude Fable 5.1 (September 1, 2026) and GPT-6 Astra (September 3, 2026) launched within 48 hours of each other, and they list at exactly the same price: $10 per million input tokens, $50 per million output tokens. That identical sticker is where the similarity in pricing ends. Fable 5.1 charges $0.25 per million cached input tokens against Astra's $1.00 — a 4x gap — and OpenAI adds a long-context surcharge above 272K tokens that Anthropic does not charge at all. But measured cost per task flips the result: Astra comes in at roughly $1.67 per task against Fable 5.1's $3.76, because Astra writes far fewer output tokens to finish the same job, and output tokens are the expensive kind. However, the published benchmarks disagree with each other, because the two labs do not test each other's models. This comparison sorts out which differences actually matter for which workload.
Fable 5.1 vs GPT-6 Astra — same sticker price, opposite cost profiles
Quick Verdict
| If you care most about… | Pick | Why |
|---|---|---|
| Lowest cost per completed task | GPT-6 Astra | ~$1.67 vs ~$3.76 measured per task — fewer output tokens to finish the job |
| Cheap cache-heavy agent loops | Claude Fable 5.1 | $0.25/M cache reads vs $1.00/M — a 4x gap that compounds every turn |
| Very large contexts (>272K) | Claude Fable 5.1 | No long-context surcharge; Astra doubles cache reads and raises input to $20 / output to $75 |
| Computer use / GUI automation | GPT-6 Astra | ScreenSpot-Pro 92.7% vs 87.3% under OpenAI's numbers |
| Math and hard science | GPT-6 Astra | FrontierMath T4 v2 97.6% vs 87.8%, GPQA Diamond 96.0% vs 93.7% |
| Deep reasoning with tools | Claude Fable 5.1 | Humanity's Last Exam with tools 65.0% vs 57.2% |
| Writing quality | Claude Fable 5.1 | Consistently the stronger prose of the two across third-party reviews |
| Cybersecurity work | GPT-6 Astra | ExploitBench 100% vs 70%; OpenAI's first "Critical" cyber-capability model |
| Using the safest option | Claude Fable 5.1 | Astra's opaque recurrence makes chain-of-thought monitoring harder — OpenAI lists this as an open problem |
Both models are excellent and both are expensive. If you are deciding between them, you are not choosing between good and bad — you are choosing between two different cost curves applied to the same quality tier.
Specs Side by Side
| Attribute | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | September 1, 2026 | September 3, 2026 |
| Context window | 1M tokens | ~1.05M tokens |
| Max output | 128K tokens | 128K tokens |
| Input price | $10 / 1M | $10 / 1M |
| Output price | $50 / 1M | $50 / 1M |
| Cache write | $12.50 / 1M (5-min), $20 / 1M (1-hr) | $12.50 / 1M |
| Cache read | $0.25 / 1M | $1.00 / 1M |
| Long-context surcharge | None | >272K: input $20, output $75, cache read $2.00 |
| Batch discount | 50% | 50% |
| Thinking control | Always-on, five levels (low → max) | Opaque recurrence; reasoning not fully visible |
| Notable strength | Reasoning depth, writing, cache economics | Token efficiency, computer use, math/science, security |
| Companion model | Mythos 5.1 (same base, tighter guardrails, gated) | Fast Mode (~2x price for ~2.5x speed) |
Three rows in that table decide most real-world purchasing decisions: cache read, long-context surcharge, and token efficiency. Everything else is close enough to be a matter of taste.
Pricing: Same Sticker, Different Bill
Both models charge $10 in and $50 out, both give a 50% batch discount, and both charge $12.50 per million tokens for cache writes. If your workload never reuses context and never exceeds 272K tokens, the two are priced identically and you can decide purely on capability.
Two differences break that tie, and both favor Anthropic.
Difference 1: Cache reads cost 4x more on Astra
Fable 5.1 charges $0.25 per million cached input tokens. Astra charges $1.00. That is a 4x gap on the cheapest input class — and it is the class that dominates agent loops, where the same system prompt, tool definitions and conversation history are re-read on every single turn.
This is the pricing change that made Fable 5.1 notable at launch: Anthropic cut cache reads from $1.00 to $0.25 per million tokens, a 75% reduction, while holding the headline $10/$50 unchanged. For a workload that re-reads 100K tokens 50 times, that change alone moved the bill from about $5 to about $1.25.
Difference 2: OpenAI charges extra above 272K tokens
Cross 272K input tokens on Astra and the rates change: input doubles to $20, output rises to $75, and cache reads double to $2.00. Anthropic applies no long-context surcharge at all.
The contrast is starkest on large retrieval workloads. A 10M-token retrieval job costs about $150 on Fable 5.1 versus about $275 on Astra — because you are past the threshold where Astra's rates change and Anthropic's do not.
| Scenario | Claude Fable 5.1 | GPT-6 Astra | Winner |
|---|---|---|---|
| Standard request, no cache, under 272K | $10 / $50 | $10 / $50 | Tie |
| Cache read, per 1M tokens | $0.25 | $1.00 | Fable 5.1 (4x) |
| Cache read above 272K, per 1M | $0.25 | $2.00 | Fable 5.1 (8x) |
| Input above 272K, per 1M | $10 | $20 | Fable 5.1 (2x) |
| Output above 272K, per 1M | $50 | $75 | Fable 5.1 |
| 10M-token retrieval workload | ~$150 | ~$275 | Fable 5.1 |
On per-token economics, Fable 5.1 wins clearly. The next section is where that conclusion gets complicated.
Cost Per Task (The Number That Matters)
Per-token pricing is a poor proxy for what you actually pay, because your bill depends on how many tokens the model needs to finish the job — and on that measure the two models are not close.
Artificial Analysis measured cost per task at roughly $1.67 for GPT-6 Astra against roughly $3.76 for Fable 5.1. A second report put the same comparison at $3.26 versus $7.63. The absolute numbers differ between sources, but the direction is consistent and the explanation is not mysterious: Astra writes substantially fewer output tokens to complete the same task, and output tokens cost $50 per million on both models.
| Metric | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Measured cost per task (source A) | ~$3.76 | ~$1.67 |
| Measured cost per task (source B) | ~$7.63 | ~$3.26 |
| Token efficiency | More verbose | Fewer output tokens per task |
| Cache-heavy loop | Cheaper | More expensive |
| Very large context (>272K) | Cheaper | More expensive |
That produces a genuinely useful split, and it is the single most practical finding in this comparison:
- High-volume, one-shot tasks → Astra. If you are running thousands of independent requests where each one reads a prompt and produces an answer, Astra's token efficiency dominates and the cache gap never gets a chance to matter.
- Long-running agent loops with stable context → Fable 5.1. If the same 100K-token context is re-read across dozens or hundreds of turns, the 4x cache-read gap compounds until it outweighs the token-efficiency difference.
- Anything above 272K tokens → Fable 5.1. The surcharge is not marginal: input doubles and output rises 50%.
The general lesson is worth stating plainly, because it applies well beyond these two models: identical per-token pricing does not mean identical cost. Verbosity is a pricing variable, and it does not appear on any pricing page.
Benchmarks: Why They Disagree
This is the part of the comparison where you should trust no single table — including the one below — without knowing who produced it.
OpenAI's own published table puts Astra ahead on nearly every row. That is not evidence of dishonesty; it is evidence that labs benchmark their models on the tasks they optimized for, using their own evaluation harness. The two labs do not benchmark each other, so there is no neutral head-to-head published by either vendor.
| Benchmark | Claude Fable 5.1 | GPT-6 Astra | Source |
|---|---|---|---|
| FrontierMath Tier 4 v2 | 87.8% | 97.6% | OpenAI |
| GPQA Diamond | 93.7% | 96.0% | OpenAI |
| ScreenSpot-Pro | 87.3% | 92.7% | OpenAI |
| ExploitBench | 70% | 100% | OpenAI |
| AutomationBench | 31.4% | 41.4% | OpenAI |
| Terminal-Bench 4.0 | 55.8% | 57.7% | OpenAI |
| Humanity's Last Exam (with tools) | 65.0% | 57.2% | Independent |
| AA Coding Agent Index | 70 (Claude Code) | 67 (Codex) | Artificial Analysis |
| AA Intelligence Index | 66 vs 61 | 61 | Artificial Analysis (one snapshot) |
| AA Intelligence Index (later) | 53 | 53 | Artificial Analysis (Sept 9) |
Note what happens if you read only the top six rows: Astra wins everything. Read only the bottom four and Fable 5.1 wins the reasoning and coding measures that independent evaluators produced. Both readings are self-serving, and the honest conclusion is in the middle.
Three things to hold onto when reading benchmark tables
- Vendor-run benchmarks favor the vendor. This is structural, not malicious. Prefer vendor-neutral evaluators such as Artificial Analysis or LMArena where a number exists, and note the date — the Intelligence Index moved from 66 vs 61 in one snapshot to a 53-53 tie on September 9, which tells you the measurement is sensitive to methodology and timing.
- The harness matters as much as the model. The coding agent index put Fable 5.1 at 70 in Claude Code and Astra at 67 in Codex. Those are different scaffolds, so the 3-point gap is not cleanly attributable to the model. If you use one vendor's harness you may get a different ranking than if you use the other's.
- "With tools" versus "without tools" can flip a result. Fable 5.1's 65.0% on Humanity's Last Exam is the tool-assisted figure. Always check which variant a headline number refers to.
One benchmark result deserves separate mention because it is not about capability at all: OpenAI's own disclosure that Astra's opaque recurrence makes chain-of-thought monitoring harder, which OpenAI lists as an open problem. A model that is harder to inspect is a real consideration for regulated or safety-sensitive deployments, and it does not show up in any of the tables above.
Which Model Wins Which Workload
| Workload | Winner | Reason |
|---|---|---|
| High-volume, independent one-shot requests | GPT-6 Astra | Fewer output tokens per task; cache gap never applies |
| Long-running agent loop, stable context | Claude Fable 5.1 | 4x cheaper cache reads compound every turn |
| Very large documents / retrieval above 272K | Claude Fable 5.1 | Astra's input doubles to $20 and output rises to $75 |
| Computer use and GUI automation | GPT-6 Astra | ScreenSpot-Pro 92.7% vs 87.3% |
| Mathematics and hard science | GPT-6 Astra | FrontierMath T4 v2 97.6% vs 87.8% |
| Safety-critical / auditable deployments | Claude Fable 5.1 | Astra's recurrence resists chain-of-thought monitoring |
| Long-form writing and editing | Claude Fable 5.1 | Third-party reviews consistently rate the prose higher |
| Batch processing jobs | Tie | Both apply a 50% batch discount |
| Cybersecurity and exploit research | GPT-6 Astra | ExploitBench 100% vs 70% |
| Reasoning with tool use | Claude Fable 5.1 | HLE with tools 65.0% vs 57.2% |
If you want the shortest possible summary of this table: Astra is the more efficient model, Fable 5.1 is the cheaper one under context reuse. Which of those properties dominates is a function of your architecture, not your preferences.
Coding and Agent Work
This is the workload most readers actually care about, and it is the one where the pricing analysis bites hardest — because coding agents are the archetypal cache-heavy, long-running loop. A coding session re-reads the repository context, the tool definitions and the conversation history on every turn, which is precisely the pattern that makes Fable 5.1's $0.25 cache reads valuable.
But the token-efficiency counterargument applies here too, and it is strong: if Astra completes the same refactor using half the output tokens, and output is $50 per million on both, Astra can win the session even while losing every per-token comparison.
| Consideration | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| AA Coding Agent Index | 70 (in Claude Code) | 67 (in Codex) |
| Terminal-Bench 4.0 | 55.8% | 57.7% |
| Cache-heavy multi-turn cost | Cheaper (4x cache reads) | More expensive |
| Cost per completed task | ~$3.76 | ~$1.67 |
| Harness availability | Claude Code | Codex |
The practical guidance for coding teams: measure your own session cost before standardizing. The two effects — cache reuse and output verbosity — push in opposite directions, and which one dominates depends on how long your sessions run and how much context stays stable. Long sessions with a stable codebase context favor Fable 5.1. Short, numerous, independent edits favor Astra.
The AA Coding Agent Index gap is worth flagging in both directions: 3 points is not a large margin, and it was measured across two different harnesses. If you are already standardized on one vendor's tooling, switching models to chase a 3-point index difference is unlikely to be the highest-value change you could make.
The Honest Take
These two models are close enough in capability that capability should not be your deciding criterion. The published benchmarks contradict each other because they come from interested parties, and the independent measurements that do exist put them within a few points on most axes. If you are waiting for a definitive answer on which is "better," the honest answer is that the question is underspecified — better at what, at what cost, over how many turns.
The decision that actually matters is cost structure, and here the two models genuinely diverge in a way that is measurable in advance:
- Astra is the more token-efficient model. It finishes tasks with fewer output tokens, and output is the expensive class. On measured cost per task it wins by roughly 2x. If your workloads are independent and high-volume, this is the whole story and the answer is Astra.
- Fable 5.1 is the cheaper model under reuse. Its cache reads are 4x cheaper, and it does not apply a long-context surcharge above 272K. If your workloads are long-running loops with stable context, or single requests over very large documents, this reverses the outcome — dramatically, in the 10M-token case.
There is one more consideration that no benchmark table captures, and it cuts toward Anthropic: Astra's opaque recurrence makes monitoring its reasoning harder, and OpenAI lists that as an unresolved problem. If you operate in a regulated industry, or you simply need to be able to explain why a model produced an output, a model that resists inspection is a governance risk regardless of how well it scores. That is the kind of consideration that does not appear in a cost-per-task number and often decides real procurement.
What I would actually do: run the same representative workload against both, on your own data, for a week, and look at total spend and total completions rather than at any leaderboard. The difference between these two models is not large enough to justify switching on the strength of someone else's benchmark table — but it is large enough that the wrong choice costs you real money at scale.
Who Should Pick Which
| If you are… | Pick |
|---|---|
| Running high-volume, stateless API workloads | GPT-6 Astra — token efficiency dominates when there is no context to reuse |
| Building a long-running coding or research agent | Claude Fable 5.1 — 4x cheaper cache reads compound across every turn |
| Processing very large documents or retrieval corpora | Claude Fable 5.1 — no surcharge above 272K, and the gap widens with size |
| Automating GUI or computer-use tasks | GPT-6 Astra — ScreenSpot-Pro 92.7% vs 87.3% |
| Doing math, science, or quantitative research | GPT-6 Astra — FrontierMath T4 v2 97.6% vs 87.8% |
| Working in a regulated industry needing auditability | Claude Fable 5.1 — Astra's reasoning is harder to monitor, by OpenAI's own admission |
| Writing long-form content or documentation | Claude Fable 5.1 — the stronger prose model |
| Already standardized on Claude Code or Codex | Stay with your harness — the index gap is 3 points across two different scaffolds |
| Cost-sensitive with unpredictable workloads | Measure first — the two effects push in opposite directions and neither wins unconditionally |
Frequently Asked Questions
Is Claude Fable 5.1 or GPT-6 Astra cheaper?
The list prices are identical: both charge $10 per million input tokens and $50 per million output tokens, both offer a 50% batch discount, and both charge $12.50 per million tokens for cache writes. The differences are in cache reads and long context. Fable 5.1 charges $0.25 per million cached input tokens versus $1.00 for Astra — a 4x gap. And above 272K input tokens, Astra doubles cache reads to $2.00 and raises input to $20 and output to $75, while Anthropic charges no long-context surcharge at all. That makes Fable 5.1 substantially cheaper for cache-heavy agent loops and very large requests.
Which model is cheaper per task in real use?
GPT-6 Astra, by a wide margin in measured testing. Artificial Analysis pegged Astra at roughly $1.67 per task against Fable 5.1 at roughly $3.76 in one report, and $3.26 versus $7.63 in another. The reason is token efficiency: Astra writes far fewer output tokens to complete the same job, and output tokens are priced at $50 per million on both models. Per-token pricing being equal does not mean per-task cost is equal. The exception is workloads with heavy cache reuse or very large contexts, where Fable 5.1's $0.25 cache reads and lack of a long-context surcharge reverse the outcome — a 10M-token retrieval workload runs about $150 on Fable 5.1 versus about $275 on Astra.
Why do the benchmarks disagree about which model is better?
Because the two labs do not benchmark each other. OpenAI's own published table puts GPT-6 Astra ahead on nearly every row, while independent evaluators tend to favor Fable 5.1 on reasoning and coding. Artificial Analysis reported an Intelligence Index of 66 versus 61 with Fable ahead in some snapshots, but a later September 9 snapshot showed the two tied at 53. Astra leads on FrontierMath Tier 4 v2, GPQA Diamond, ScreenSpot-Pro, ExploitBench, AutomationBench and Terminal-Bench 4.0 under OpenAI's numbers. Fable 5.1 leads on Humanity's Last Exam with tools and on the Artificial Analysis Coding Agent Index. Treat any single number as source-dependent, and prefer the vendor-neutral evaluators where they exist.
Which should I choose for coding and agent work?
It depends on the shape of the workload rather than on raw quality, because the two are close enough that cost and latency usually decide it. Choose GPT-6 Astra for token-efficient one-shot tasks, computer use and GUI automation, math and science, cybersecurity, and professional artifacts — its lower cost per task compounds across high-volume agent runs. Choose Claude Fable 5.1 for long-horizon reasoning, writing quality, cache-dominated agent loops, and requests above 272K input tokens where Astra's long-context surcharge applies. The Artificial Analysis Coding Agent Index put Fable 5.1 at 70 in Claude Code against 67 for Astra in Codex, suggesting the harness matters as much as the model.
Do both models support 1M token context?
Yes. Fable 5.1 offers a 1M token context window with a 128K maximum output. Astra is specified at approximately 1.05M tokens, also with 128K max output. The difference is not the size of the window but the price of using it: beyond 272K tokens Astra applies a surcharge that roughly doubles input cost and raises output by 50%, while Anthropic's rates stay flat.
Is there a cheaper alternative to both?
If you are cost-sensitive and your task does not require frontier reasoning, the answer is almost certainly yes — this comparison is between two premium models at $10/$50, and both vendors offer cheaper tiers. Gemini 3.8 Flash, for instance, launched at a promotional $0.75/$3.75 rate through the end of 2026, and DeepSeek's V4.1-Flash is open-weight under an MIT license with a fraction of the API cost. The honest framing is that Fable 5.1 versus Astra is a question about which premium model, not about whether you need a premium model at all.
Should I switch from one to the other?
Only with your own measurements. The capability gap between these models is small and the published benchmarks contradict each other, so switching on the strength of someone else's leaderboard is unlikely to pay off. What is worth acting on is cost structure, and that is measurable on your own workload: run the same representative tasks against both for a week and compare total spend against total completions. If your workload is stateless and high-volume, expect Astra to win. If it involves long sessions with stable context, or documents above 272K tokens, expect Fable 5.1 to win.
Sources
- OrcaRouter — Claude Fable 5.1 vs GPT-6 Astra Comparison (September 2026)
- Artificial Analysis — Benchmarking GPT-6 Astra
- Ofox — GPT-6 Astra vs Claude Fable 5.1: Which Is the Better Fit for Your Agent?
- DataCamp — GPT-6 Astra vs Claude Fable 5.1: Benchmarks and Prices
- Improvado — GPT-6 Astra vs Claude Fable 5.1: Which AI Should You Use?
- ET Now — I tested GPT-6 Astra vs Claude Fable 5.1 vs Sonnet 5
Disclosure: Anthropic and OpenAI do not benchmark each other's models, so no vendor-neutral head-to-head published by either lab exists. Every benchmark figure above is attributed to its source, and OpenAI-sourced numbers should be read as vendor-run evaluations of the tasks OpenAI optimized for. Cost-per-task figures come from third-party measurement by Artificial Analysis and differ between reports in absolute terms while agreeing on direction. Pricing and context limits are as published at the time of writing and are subject to change — verify current rates on each vendor's official pricing page before making a purchasing decision. This article reflects analysis of published information and does not represent hands-on testing by the author.
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