Where it fits
Agentic coding: multi-file changes, terminal-driven workflows, and test-backed refactors with a reviewer in the loop.
The grounding numbers: DeepSWE v1.1 74.1%, Terminal-Bench 4.0 57.9%
GPT-6: Astra scores 74.1% on DeepSWE v1.1 and 57.9% on Terminal-Bench 4.0, OpenAI's own agentic-coding evaluations. Honestly stated: on the independent Artificial Analysis Coding Agent Index, Claude Fable 5.1 currently leads at 70 against Astra's 67. Astra is a strong coding agent, not the leading one on this specific independent composite.
Example prompt
Plan the following engineering objective as a sequence of checkpoints with dependencies, failure recovery, validation gates (tests to run at each step), and a handoff summary:
[Describe the objective here]Ready to test the workflow?
Create account & add creditsWho should look elsewhere
If the independent Coding Agent Index is your deciding factor and price is comparable, Claude Fable 5.1 currently scores higher. Evaluate both on your own repository and test suite before committing production routing to either.
Recommended access
Use pay-as-you-go API credits for a controlled evaluation on your own workload and cost profile before committing production routing.
Frequently asked questions
Is GPT-6: Astra good for software engineering?
GPT-6: Astra scores 74.1% on DeepSWE v1.1 and 57.9% on Terminal-Bench 4.0, OpenAI's own agentic-coding evaluations. Honestly stated: on the independent Artificial Analysis Coding Agent Index, Claude Fable 5.1 currently leads at 70 against Astra's 67. Astra is a strong coding agent, not the leading one on this specific independent composite. If the independent Coding Agent Index is your deciding factor and price is comparable, Claude Fable 5.1 currently scores higher. Evaluate both on your own repository and test suite before committing production routing to either.
Put GPT-6: Astra to work
Fund a controlled evaluation, start with a prepared prompt, and measure quality and cost on your own workload.