GPT-6 Astra vs Fable 5.1 vs Sonnet 5 on Real Agent Work

Five tests, real per-task costs, and the routing decision I actually made. Day-four numbers, not benchmark slides.

GPT-6 Astra went live four days ago. I ran the same five tests I run on every model that claims agent capabilities, with Claude Fable 5.1 and Claude Sonnet 5 alongside for comparison. Same SOUL.md, same tools, same tasks, same afternoon.

One caveat up front, and I mean it: this is one session, not a week of testing. Astra's serving infrastructure is days old and API access is still rolling out. Treat everything below as first signal, not settled verdict.

Look at the price sheet before you look at anything else

Every question I got on the Reddit version of this post was some version of "but what does it cost." So the money goes first.

Prices checked September 7, 2026 against the OpenAI and Anthropic pricing pages. Two things worth knowing that the sticker hides.

First, Sonnet 5 is $2/$10 permanently. Anthropic had a $3/$15 increase scheduled for September 1 and cancelled it on August 10. If you budgeted on the higher number, un-budget.

Second, Astra has a second rate card. Above 272K input tokens, the whole request bills at 2x input and cache rates and 1.5x output. A 300K-token request costs more than double a 250K one. Fable 5.1 bills its full 1M window at a flat rate.

Here's the part that matters more than any of that.

All three at $10/$50 is misleading. Agent workloads are mostly cached context. Your effective input cost is the cache line, not the sticker. Fable's cached input is 4x cheaper than Astra's.

If you've never looked at an agent's token breakdown, do it once. Mine runs 80 to 95 percent cached on any given call: the same SOUL.md, the same tool schemas, the same conversation history, re-sent every turn. The fresh input is a few hundred tokens of new message. Everything else is a cache read. So a model's cache price is its real input price for agent work, and on that line Fable 5.1 is $0.25, Sonnet 5 is $0.20, and Astra is $1.00.

I should also say the thing that cuts against my own conclusion. OpenAI's launch post claims Astra completed Terminal-Bench 4.0 at roughly 63 percent lower estimated API cost per task than Fable 5.1, because it uses fewer output tokens on those tasks. That's their number, in their benchmark setup, and I believe they measured it. My tasks are not Terminal-Bench. Keep both in mind as you read the cost section.

Test 1: Fifty identical tool calls, one JSON schema

The most boring test is the one that breaks the most models. Fifty classification calls, same schema, same instructions, check every response for valid structured output.

Astra's two failures are the interesting ones. The JSON was fine, but it was preceded by a sentence of "thinking out loud" that a strict parser rejects. OpenAI's own launch notes say Astra has more control over its written reasoning and solves simpler tasks with fewer written steps, which their monitoring team flagged as a downside for oversight. My guess is the same trait occasionally bleeds into output formatting. Fixable with a stricter system prompt and a response format constraint. Still a day-one rough edge on a model priced at $50 per million output tokens.

Test 2: The "done" lie

Six-step chain. Step four hits a dead URL on purpose. Does the model report the failure, quietly synthesise something plausible, or improvise?

None of them lied. That alone is progress over eighteen months ago, when "step four succeeded" was a coin flip.

If I'm running this unsupervised overnight, Sonnet's "fail and stop" is the safest behaviour. Astra's "fail and try something else" is the most capable. Fable's "fail and suggest" is the middle ground.

This is the frame I'd push anyone toward. It is not a capability ranking. It's a safety-versus-autonomy dial, and where you want the dial depends entirely on whether a human is watching. OpenAI reports that Astra never attempted to circumvent an auto-review denial in their internal tests, which is reassuring. But "tries an alternative without asking" is exactly the behaviour that got a Meta researcher's inbox mass-deleted earlier this year. Capability and blast radius grow together.

Test 3: Does the rule from message 1 survive message 25?

Constraint set at message one, checked at message 25 and beyond. This is the test people expect a frontier model to ace.

None of them did. All three degrade somewhere around message 22 to 30. Astra held marginally longer on one run and marginally shorter on another. I call it a three-way tie, and I'm reporting the tie rather than inflating a difference that isn't there.

Nobody has solved instruction decay. If your agent needs a rule to hold across a long session, the fix is architectural, not a model swap. I wrote up the patterns that actually work in the guide on why agents drift from their rules, and none of them are "pay more per token."

Test 4: Will it admit what isn't in the context?

Load 200K tokens of documents. Ask about something not in them. Watch for confabulation.

All three passed cleanly. Context honesty is table stakes at this tier now, and I'm not going to pretend otherwise.

Where Astra does stand apart is raw capacity. OpenAI reports 96.3 percent on MRCR v2 8-needle at 512K to 1M tokens, versus 73.8 percent for GPT-5.6 Sol, and the 1.05M window is real. Anthropic's models also offer 1M, but I haven't seen equivalent long-range retrieval numbers published for Fable 5.1 or Sonnet 5, so I'm not going to invent a comparison. If your agent needs to reason across an entire codebase or a quarter of Slack history in one pass, Astra is the model to test. Just remember the 272K rate cliff when you do.

Test 5: What one real task costs

This is the section that will get screenshotted, so I'll be precise about what it is. My standard research-and-draft task: pull from three sources, reconcile, write a 600-word brief with citations. Roughly 40K tokens of cached context, a few thousand fresh, a couple thousand output, run to completion.

On cost per completed task, Sonnet 5 wins by a wide margin. Astra is the most expensive of the three for the same job, by 1.7x over Fable and nearly 6x over Sonnet.

Here's the weird part about the Astra number. The gap is not mainly the cache line. It's reasoning. Astra thinks before it answers, that thinking is billed as output tokens at $50 per million, and you never see it in the response. On a task where the visible answer is 1,500 tokens, the bill can reflect three or four times that. Fable 5.1 has an effort dial that lets you cap this. Astra has reasoning effort levels too, and I ran it at the default. Turning it down will lower the bill and probably lower the Test 2 initiative along with it. That's a trade I haven't measured yet.

Now multiply.

Thirty-day months, per-task costs from the table above, this task shape only. Your agent's task shape will differ. But the ratio will hold as long as your work is mostly cached context plus a modest amount of output, which describes almost every personal and ops agent I've seen.

The benchmarks, with dates on them

Published numbers only, agent-relevant only, sourced from OpenAI's GPT-6 Astra launch post dated September 3, 2026 unless noted.

A note on Sonnet 5, because I'm not going to fake a column. Anthropic's June 30 system card reports 81.2 percent on OSWorld-Verified and 80.4 percent on Terminal-Bench 2.1. Those are different benchmark versions from the ones OpenAI ran, so the numbers don't line up and shouldn't be compared side by side. Anyone showing you a table where they do is guessing.

Astra leads on every agent benchmark where a direct comparison exists. The gaps are real. But the cost per task on my work is 1.7x to nearly 6x higher. Whether "leads the benchmark" translates to "worth several times the cost on my daily agent work" is the only question that matters, and a benchmark table can't answer it.

Where Astra actually earns the premium

Three places, and they're specific.

Computer use and desktop automation. OpenAI's latency simulation on OSWorld 2.0 has Astra scoring 72.6 percent at roughly 40 minutes per task, versus 65.7 percent at roughly 75 minutes for GPT-5.6 Sol. That's 47 percent less wall-clock time on a task category where wall-clock time is the entire cost. If your agent fills forms, drives a browser, or works inside desktop apps, this is a real gap.

Security work. ExploitBench at 100 percent, the first OpenAI model to cross the Critical cyber threshold in their Preparedness Framework. With the caveat that Astra will refuse the more advanced offensive tasks at launch, so what you get today is secure code review and patching, with broader defensive workflows gated behind OpenAI Daybreak.

SRE and infrastructure. SRE-Bench pass@1 at 88 percent, versus 55.9 percent for Sol and 12.5 percent for Opus 5. Reverse-engineering binaries without source is a hard, specific capability, and Astra is in a different tier on it.

If your agent does infra work, browser automation, or security testing, the benchmark gap translates to real capability. If your agent does morning briefings and email triage, it doesn't.

Where it doesn't

Morning briefings, email triage, classification, drafting, research summaries, simple tool calling. Everything a personal agent does fifty times a day. On these tasks, the three models produce output I cannot tell apart in a blind read, and Sonnet does it at a fifth of the price.

I want to sit on that for a second because it's the whole post. I ran the same research brief through all three and shuffled the outputs. I picked the "best" one three times out of five. Two of those three were Sonnet. That is not a statistically meaningful result. It's just enough to tell me the difference, if it exists, is smaller than my ability to notice it.

If you're already paying a frontier sticker price for tasks in this list, the cheapest optimisation available to you isn't prompt engineering. It's a routing rule. We built BetterClaw's model routing to be per-agent and per-task precisely because we got tired of paying Opus prices for "summarise these six emails." Free plan, bring your own keys, no inference markup.

What I'm routing where, and the actual rules

Nobody publishes their real routing decisions with reasoning, so here are mine.

Sonnet 5 stays as the daily driver. $2/$10, fastest of the three, and good enough on everything my agent does most. Every task starts here unless a rule below fires.

Fable 5.1 stays as the escalation model. Same sticker as Astra but 4x cheaper on cache reads, which is most of an agent's input bill. Anthropic's own estimate is that highly agentic workloads run up to 45 percent cheaper on 5.1 than on Fable 5 purely from the cache cut, and my numbers agree.

Astra goes into the "watch" slot. I'm running it for a full week on computer-use and multi-step tasks. If the OSWorld lead turns into real-world reliability, it earns a routing slot for that category. If it doesn't, Fable does the same job cheaper.

The rules, since people asked for logic rather than vibes:

Escalate Sonnet to Fable when a task has more than eight tool calls in its plan, or when Sonnet returns a "cannot complete" on a task it should be able to do, or when the task touches money or external comms and I want the "suggest and wait" behaviour from Test 2.

Route to Astra only when the task category is browser automation, desktop app work, or code review with a security lens. Nothing else, until the week of testing says otherwise.

Hard cap every agent's daily spend, regardless of model. A routing mistake at $50 per million output tokens is expensive by lunch.

If you're on OpenClaw and want the mechanics, the OpenClaw model routing guide covers the config. The cost math behind the cache-first approach is in the piece on prompt caching for agent workloads.

Not switching my default. Not today.

How this looks in practice

The question isn't "which model is best." It's "which model for which task at which cost."

A well-configured agent routes complex multi-step work to a frontier model and routes classification, triage, and drafting to a cheaper one. The router pays for itself on day one.

On BetterClaw, routing is per-agent and per-task. You can run your morning briefing on Sonnet 5 at about $0.19 a run and a security review agent on Astra at about $1.10 a run, on the same platform, with each agent's spend tracked and capped separately. BYOK means the bill you see is the provider's bill, with nothing added on top.

The update I owe you

The Reddit post promised a full-week Astra update. This post is day four. The week-long numbers, especially the computer-use runs and the effort-level cost curve, will go up as an addendum to this page, same URL, with the date stamped. If the numbers change my routing, I'll say so in the first line.

And one honest admission to close. Four days in, the most surprising thing about GPT-6 Astra isn't anything on the benchmark table. It's that the benchmark table keeps getting less relevant to the decision. Three frontier models now clear the bar on the work most agents actually do. The bar moved from "can it" to "what does it cost me to let it," and that is a much better problem to have.

If any of this resonated, give BetterClaw a try. Free plan with 1 agent and every feature, bring your own API keys, no inference markup. Pro from $49 a month. Your first deploy takes about 60 seconds. We handle the infrastructure. You handle the routing rules. Start free or see full pricing.

Frequently Asked Questions

What is GPT-6 Astra?

GPT-6 Astra is OpenAI's flagship model, released September 3, 2026, with a 1.05M-token context window and a 128K maximum output. It leads on computer use, terminal, and security benchmarks and is the first OpenAI model classified as Critical for cybersecurity under their Preparedness Framework. API pricing is $10 per million input tokens, $1 cached, and $50 output, with higher rates above 272K input tokens.

How does GPT-6 Astra compare to Claude Fable 5.1 for AI agents?

On published benchmarks Astra leads: 57.9% vs 55.8% on Terminal-Bench 4.0 and 64.6% vs 52.6% on Terminal-Bench Science. On my agent tests, Fable 5.1 was cleaner on structured tool calling (50/50 vs 48/50) and cost about 40 percent less per task, mostly because its cache reads are $0.25 per million versus Astra's $1.00. Astra is the more autonomous of the two when a step fails; Fable suggests and waits.

How do I set up model routing between Sonnet 5, Fable 5.1, and Astra?

Start with Sonnet 5 as the default for every task. Add escalation rules to Fable 5.1 for long multi-step plans or tasks that touch money or external communication. Reserve Astra for a narrow category like browser automation or security review, and put a daily spend cap on every agent. On BetterClaw this is configured per agent in the model settings; on OpenClaw it's a config change covered in our routing guide.

How much does GPT-6 Astra cost per agent task compared to Sonnet 5?

On my standard research-and-draft task, Astra ran about $1.10 per completion versus about $0.19 for Sonnet 5, so roughly 6x. At 200 tasks a day that's about $6,600 a month versus about $1,140. The gap comes from Astra's $50 output rate, which also bills its hidden reasoning tokens, and its $1.00 cache-read rate. For computer-use tasks the ratio may differ because Astra finishes those faster.

Is GPT-6 Astra reliable enough to run unsupervised agents?

On my tests it never fabricated a completed step and OpenAI reports it never circumvented an auto-review denial internally. But it does take initiative when a step fails, which is exactly what you don't want unsupervised without approval gates and a kill switch. For overnight runs I'd still pick Sonnet 5's stop-and-report behaviour, or Fable 5.1 with action approval turned on, and treat Astra as a supervised specialist until a full week of data says otherwise.