Quick Answer
GPT-6 Astra has no traditional benchmark scores yet — and that's deliberate. OpenAI's reveal centered on 10 formally-verified math proofs (Lean 4) rather than a leaderboard. What we have: hard evidence of deep reasoning in math, and nothing official for coding or general tasks. The message: wait for real benchmarks before betting on it.
What We Have (Math, Formally Verified)
- 10 math problems previously considered human-level, solved at ~$2,000 in tokens each.
- Each proof verified by Lean 4, a formal proof assistant — mechanically checkable, unforgeable.
That's the extent of the hard evidence. It's credible specifically because formal verification can't be gamed by memorization or guessing.
What We Don't Have (Yet)
| Benchmark type | Status |
|---|---|
| Coding (SWE-bench-style) | No official results |
| General reasoning / MMLU-class | No official results |
| Multimodal | No official results |
| Safety/cyber (beyond "Critical" flag) | No public detail |
The absence matters: Astra's "native multi-agent" may be a real leap, but there's no evidence yet that it wins at coding — the task most developers care about. Don't let "solved 10 math problems" become "beats everything at code" in your head.
Why the Shift to Proofs Over Scores
Traditional benchmarks have a credibility problem: contamination (training-data leakage), saturation (frontier models near the ceiling), and "teaching to the test." Formal verification sidesteps all three — a Lean 4 proof either checks or it doesn't. If this becomes the new standard, "how strong" will be measured by "can it produce verifiable proofs," not a leaderboard position.
How to Evaluate Astra Yourself
When it ships, don't trust the marketing — run your own tests:
- Your own repo — the only benchmark that matters for your workflow.
- A/B against your current model — same tasks, compare output quality and cost.
- Cost-weighted — a 10% quality gain isn't worth 25× the price for volume tasks.
The practical setup is a multi-model gateway where you can flip between Astra and cheaper models per task:
from openai import OpenAI
client = OpenAI(api_key="sk-teamo-xxxxxx", base_url="https://api.teamorouter.com/v1")
# try model="gpt-6" vs "deepseek-v4-pro" / "claude-fable-5" on the same task
FAQ
Q: Are there any official Astra benchmark numbers? No. Only the 10 math proofs. No coding, general, or multimodal scores have been published.
Q: Is "no benchmarks" a red flag? Not necessarily — it may reflect the shift to verification over leaderboards, plus the model is still gated behind security review. But it does mean you should hold off on performance claims until real results land.
Q: What should I watch for after release? SWE-bench-style coding results, cost per task (not just per token), and real-world multi-agent performance — plus your own repo tests.
Summary
Astra's only hard evidence is formally-verified math; coding and general benchmarks are still missing. Judge it on your own tasks once it ships, and route by task so cost stays sane. Sign up for TeamoRouter to A/B Astra against cheaper models the day it launches.
Get Started
TeamoRouter — flip between Astra and cheaper models on the same task, one key.