AiAmbAThree models. Your real workload.
A leaderboard is a starting point. The model that earns a place in your stack has to perform on your code, your tools and your definition of done.
Capability is only part of the choice.
OpenAI positions GPT-6 Astra for demanding reasoning, coding, research and computer-use work. Its standard API text rates are $10 per million input tokens and $50 per million output tokens; prompts above 272K input tokens have higher rates. [1]
Anthropic launched Opus 5.5 on September 22, 2026. Its announcement compares the model with Fable 5.1 and Astra. Standard Opus 5.5 input/output rates are $4/$20 per million tokens. Fable pricing and access are not established by the sources used here. [2]
| Model | Input / 1M | Output / 1M |
|---|---|---|
| GPT-6 Astra | $10 | $50 |
| Claude Opus 5.5 | $4 | $20 |
| Claude Fable 5.1 | Not verified here | Not verified here |
Different tasks. Different leaders.
Source: Anthropic launch table. Different effort settings; see the methodology below.
The chart reproduces selected results from Anthropic’s launch table, not an AiAmbA independent evaluation. Terminal-Bench 4.0 reports 66.4% for Opus 5.5, 57.9% for Astra and 55.8% for Fable 5.1. AutomationBench reports 40.0%, 41.4% and 31.4%, respectively. [2]
Configurations differ. Anthropic reports Opus at xhigh and Astra at high for Terminal-Bench, and notes safeguard fallbacks in its evaluation. AutomationBench results are attributed to Zapier. Read the source methodology before interpreting small differences. [2]
Run the same job. Keep the evidence.
Our suggested evaluation has three tasks: fix a reproducible bug, implement a feature with an ambiguous edge case, and recover a failed workflow. Give each model the same starting files, tools, budget and acceptance criteria.
Measure pass rate, human corrections, elapsed time and total billed cost. Repeat representative tasks to expose variance. A cheaper token rate can lose its advantage if a model needs more retries; a faster answer can create more review work.
Complete [task] from this repository state. Acceptance criteria: [criteria]. Allowed tools: [tools]. Budget: [time/cost]. Preserve [invariants]. Verify behavior with [checks]. Return the result, verification evidence, unresolved issues and measured usage if available. Do not invent benchmark results.
Choose for the system around the model.
For an industrial assistant, test whether it distinguishes machine evidence from inference, respects operator permissions and handles missing telemetry. These are our recommended acceptance tests, not claimed benchmark wins for any model.
AiAmbA brings the question back to connected systems: machine context, cloud intelligence and mobile conversation. A model recommendation should explain the tradeoff for that workflow rather than declare one universal winner.
Sources & further reading
- OpenAI: GPT-6 Astra model specification ↗
- Anthropic: Introducing Claude Opus 5.5, benchmarks and methodology ↗
Pricing, access and allowances can change. Vendor statements are attributed; prompts and evaluation methods are AiAmbA editorial recommendations. No independent performance testing is claimed.