Release report · September 2026Claude Opus 5.5 · Without fallbacks

A generation's jump, at last generation's price.

モデル
claude-opus-5-5
供給者
Anthropic
実行
Sep 17 – Sep 18, 2026
Benchmark
AutomationBench 1.0.6
タスク
657 per effort · 5 efforts

Claude Opus 5.5 completes 40.0% of AutomationBench's strict, end-to-end business tasks at max effort. That's 13.1 points above Opus 5 for 6% more per task, and 1.4 points behind GPT-6 Astra for 17% less.

Best pass rate40。0%Max effort · 263 of 657 tasks
vs. Claude Opus 5+13.1pts26.9% at Max
Cost per task$4.22Max effort · no-cache estimate
On the frontier5/ 5effort settings no model in this comparison beats on both cost and accuracy

Key findings

01

Medium beats last generation's max

At medium effort, Opus 5.5 passes 28.6% of tasks for $2.33 each. Opus 5 at max passes 26.9% for $3.98. Higher accuracy for 41% less.

02

Every effort setting sits on the frontier

Of the 25 model-and-effort configurations in this run's comparison, eight are Pareto-optimal: nothing else is both cheaper and more accurate. Opus 5.5 holds five of them, one at every effort level. Claude Sonnet 5.5, tested a week later, isn't part of this comparison.

03

Effort pays off unevenly

Support gains the most, from 15% at low to 36% at max. Operations leads at 62%. HR is still the hardest domain, topping out at 24.2%.

04

Max effort thinks more, not longer

Reasoning tokens grow 18.6× from low to max, but steps per task only rise from 16.1 to 18.7.

01

Results by effort

Every step up in effort buys accuracy. The biggest jump is the last one: +5.6 points from xhigh to max.

Scoring is strict. A task passes only if every assertion about the final state of the simulated company holds. All 657 tasks stay in the denominator at every effort, refusals included.

Low
23。3%
153/657$1.62
Medium
28。6%
188/657$2.33
High
32。0%
210/657$2.57
XHigh
34。4%
226/657$3.17
マックス
40。0%
263/657$4.22

Official pass rate · task_completed_correctly · 657 tasks per effort, all in the denominator

02

Cost vs. accuracy

Medium, high and xhigh all beat Opus 5's max on both cost and accuracy.

Each line is one model, traced from low to max effort. The dotted line marks the frontier, the configurations no other model beats on both cost and accuracy. Past 40%, only GPT-6 Astra at max goes further: 41.4% at $5.10 per task.

0%10%20%30%40%50%$0$1$2$3$4$5$6$7$8Cost per task (USD) →Claude Fable 5.1Claude Opus 5GPT-6 AstraGPT-5.6 SolClaude Opus 5.5LowMediumHighXHighMax
Claude Opus 5.5 Frontier Frontier point DominatedHover a point for details
View the data
モデルLowMediumHighXHighマックス
Claude Opus 5.523.3%$1.6228.6%$2.3332.0%$2.5734.4%$3.1740.0%$4.22
Claude Fable 5.116.6%$4.7417.8%$5.1319.6%$5.3421.3%$6.5322.4%$7.09
Claude Opus 520.4%$2.8623.9%$3.2220.5%$3.1325.3%$3.6826.9%$3.98
GPT-6 アストラ30.3%$3.6534.1%$4.0437.1%$4.4839.0%$4.6541.4%$5.10
GPT- 5 . 6ソル11.7%$1.3319.6%$1.8424.8%$2.0426.3%$2.3728.8%$3.06

API cost per task, no-cache estimate · pass rate at each effort, low to max · Comparison models were run on the same benchmark version and shown at five effort levels each.

03

By domain

Operations is closest to solved. HR is still the hardest domain in the benchmark.

Support gains the most from extra effort, climbing 21 points. Sales climbs slowly until max, then jumps 11.1 points in a single step.

0%10%20%30%40%50%60%70%
Low → マックス
オペレーションズ
48。0%62。0%
+14.0pts
マーケティング
28。0%43。0%
+15.0pts
販売
23。9%42。7%
+18.8pts
支える
15。0%36。0%
+21.0pts
金融
17。5%35。8%
+18.3pts
HR
10。8%24。2%
+13.4pts
Low Medium, High, XHigh マックス

Full matrix

ドメインLowMediumHighXHighマックス
販売23.9%28/11726.5%31/11728.2%33/11731.6%37/11742.7%50/117
マーケティング28.0%28/10037.0%37/10039.0%39/10040.0%40/10043.0%43/100
オペレーションズ48.0%48/10053.0%53/10051.0%51/10054.0%54/10062.0%62/100
支える15.0%15/10022.0%22/10033.0%33/10034.0%34/10036.0%36/100
金融17.5%21/12023.3%28/12030.0%36/12031.7%38/12035.8%43/120
HR10.8%13/12014.2%17/12015.0%18/12019.2%23/12024.2%29/120

Pass rate · passed / tasks per cell · darker = higher

04

費用

A successful task at max effort costs $10.54 once you count the failures.

Failed attempts still cost money. Spend per pass divides every task's cost by the number of passes. Max effort delivers 72% more passes than low for 52% more per pass.

EffortCost / taskPassed taskFailed taskSpend per pass
Low$1.62$2.34$1.40$6.95
Medium$2.33$3.06$2.05$8.16
High$2.57$3.45$2.16$8.05
XHigh$3.17$4.14$2.66$9.21
マックス$4.22$5.04$3.67$10.54

USD, no-cache estimate · $4.00 input / $20.00 output per 1M tokens · spend per pass = all task costs ÷ passes, failures included

05

Tokens & tools

Reasoning grows 18.6×. Steps barely move.

From low to max, tool calls per task rise 41% while steps rise only 16%. Higher effort packs more thinking and more calls into each step instead of running longer. About 90% of input is served from cache at every level, and no task hit the 50-step cap.

Reasoning Visible output (text + tool calls)Mean output tokens per task
Low
12.7k1.2k reasoning
Medium
15.7k2.0k reasoning
High
17.2k2.7k reasoning
XHigh
21.9k5.8k reasoning
マックス
39.5k21.6k reasoning
EffortTool callsステップCalls / stepInput tokensCached shareAt step cap
Low46.816.12.9341.0k89。1%0
Medium52.117.92.9505.1k90。6%0
High53.918.32.9557.6k90。9%0
XHigh58.919.33.1682.6k91。3%0
マックス65.818.73.5856.9k90。1%0

Mean per task, all tasks included · step cap 50

06

Refusals

Refusals roughly double at max effort, from 2.0% to 4.4%.

A refused task counts as a failure. Marketing draws the most refusals, 10% of tasks at max. These are recorded refusal flags, not inferred from response text.

2。0%
Low13/657
2。0%
Medium13/657
2。1%
High14/657
3。0%
XHigh20/657
4。4%
Max29/657
ドメインLowMediumHighXHighマックス
販売1。7%0。9%1。7%5。1%4。3%
マーケティング7。0%7。0%4。0%6。0%10。0%
オペレーションズ1。0%2。0%2。0%3。0%4。0%
支える1。0%0。0%1。0%2。0%4。0%
金融0。8%0。0%1。7%0。8%1。7%
HR0。8%2。5%2。5%1。7%3。3%

Recorded refusal flags · refused tasks stay in the denominator

07

Methodology

AutomationBench tests AI agents on end-to-end workflow execution using 47 simulated apps across six business functions: Sales, Marketing, Operations, Support, Finance and HR. Each task boots a small simulated company, hands the agent one request a real operations person would get, then grades the world it leaves behind with deterministic assertions.

  1. 01

    Boot a tiny company

    CRM records, inbox threads, spreadsheets and support cases, including traps like stale rows and near-duplicate names.

  2. 02

    The agent works alone

    The agent gets one trigger message and tools to search, read, update and send. It can't ask clarifying questions, and there's no human in the loop.

  3. 03

    Grade the end state

    Deterministic assertions check what changed. The task passes only if every one holds.

モデル
claude-opus-5-5
Configuration
Opus 5.5 (without fallbacks)
Benchmark
AutomationBench 1.0.6
Run dates
Sep 17 – Sep 18, 2026
タスク
657 per effort setting
Efforts
Low, Medium, High, XHigh, Max
Mode
API · 50-step cap
メトリック
task_completed_correctly (strict)

CostCosts are a no-cache estimate at the provider's announced launch rates. Every input token is billed at the full rate, cache discounts are ignored and reasoning is billed as output. These are not billed costs, and they run higher than the leaderboard's Cost / task, so don't compare the two directly.

Rates$4.00 input · $20.00 output · $0.20 cache read per 1M tokens.

ComparisonsComparison models were run on the same benchmark version and shown at five effort levels each.