# GPT-6 Astra vs GPT-6.1 Sol: which to choose for work, chat and API

> AI Release · @ai_release1 · https://ai-release.org/guides/gpt-6-astra-ili-gpt-6-1-sol-chto-vybrat-dlya-raboty-cha_en.html

## TL;DR
- Time: ~40 minutes
- Difficulty: Intermediate
- You will need: ChatGPT/Codex access with GPT-6 Astra, an OpenAI API key, Python 3.10+, `curl`, a terminal, a plain-text task list file
- Outcome: a documented decision (work / chat / API) backed by one acceptance run per use case, not by impressions

Verified facts used in this guide come only from our channel's posts: GPT-6 Astra in Codex as a "staff engineer collaborator" (Notion), a one-line feature request built and self-tested with computer use (Ramp), an aircraft flight-control design challenge (Figma), 99.95% on ARC AGI 3 and an adaptivity test near 100% versus ~48% for an average human. **GPT-6.1 Sol is not covered by any post in this channel** — every cell for it below is marked "check the documentation", not guessed.

## Requirements and setup
![Illustration: Requirements and setup](https://ai-release.org/guides/img/gpt-6-astra-ili-gpt-6-1-sol-chto-vybrat-dlya-raboty-cha-en-1.jpg?v=1792962001)

- OS: macOS 13+, Ubuntu 22.04+, or Windows 11 with WSL2 (the commands below use bash).
- Python 3.10+ and `pip`: run `python3 --version` and confirm it prints `Python 3.10.x` or newer.
- `curl` for raw HTTP checks: `curl --version`.
- Access to a ChatGPT/Codex workspace where GPT-6 Astra is selectable in the model picker.
- An API key stored in an environment variable — never in a file you commit.
- A project folder with a Git repository (`git init`) so every model-made change is a reviewable diff.
- Free disk: 2 GB is enough for the test project used here.
- Read the vendor documentation for the exact model IDs and pricing of both GPT-6 Astra and GPT-6.1 Sol before you start; the API model string is not published in our posts.

```bash
mkdir -p ~/model-choice && cd ~/model-choice
git init
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade openai
python -c "import openai; print(openai.__version__)"
```

**Example:** the last command prints a version such as `1.x.x`. If it prints nothing and exits with an error, the virtual environment is not activated.

## Step 1. Write down your tasks and the verified facts before comparing anything
**What to do:** create `tasks.md` with three sections — Chat, Work (agentic coding), API — and one real task per section taken from your own backlog. Then add `facts.md` containing only claims you can source.

**Why:** the posts show GPT-6 Astra doing three different jobs: designing a flight-control experience for a new personal aircraft (Figma), adding routing controls for individual API keys from a one-line request and then checking its own work with computer use (Ramp), and acting as a staff engineer collaborator in Codex when given the goal plus context (Notion). Matching your task to that list prevents you from "evaluating" a model on tasks nobody documented.

**How to check:** `cat tasks.md` lists exactly three headings and three tasks; `cat facts.md` contains only sentences you can trace to a source.

```bash
cat > tasks.md <<'EOF'
## Chat
Summarise a 12-page vendor contract into 10 bullet points.

## Work (agentic coding)
Add per-key rate limiting to our internal API and prove it works.

## API
Batch-classify 200 support tickets into 5 categories.
EOF

cat > facts.md <<'EOF'
- GPT-6 Astra (Codex): goal + context given, then it tackles the problem (Notion).
- GPT-6 Astra: one-line feature request -> change made -> self-checked with computer use (Ramp).
- GPT-6 Astra: interface generation vs. system design gap (Figma).
- GPT-6 Astra: 99.95% on ARC AGI 3; adaptivity near 100% vs ~48% human average.
- GPT-6.1 Sol: no data in this channel -> check the documentation.
EOF
```

| Model | Difference | Price |
|---|---|---|
| GPT-6 Astra | Agentic work in Codex with goal + context; builds a feature from a one-line request and self-verifies using computer use; used for system-level design tasks (flight-control UX); 99.95% on ARC AGI 3; adaptivity near 100% vs ~48% human average | Not stated in our posts — check the documentation |
| GPT-6.1 Sol | No verified behaviour documented in this channel's posts | Not stated in our posts — check the documentation |

For a documented chat-focused comparison of another model pair, see [GPT-5.5 Instant vs GPT-6.1 Sol: which model to choose for chat, API and everyday tasks](https://ai-release.org/guides/gpt-5-5-instant-ili-gpt-6-1-sol-chto-vybrat-dlya-chata.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) — it uses the same "one task, one acceptance run" method.

## Step 2. Configure two access paths: Codex workspace and API client
![Illustration: Step 2. Configure two access paths: Codex workspace and API client](https://ai-release.org/guides/img/gpt-6-astra-ili-gpt-6-1-sol-chto-vybrat-dlya-raboty-cha-en-2.jpg?v=1792962005)

**What to do:** set up (a) a Codex workspace with your repository open, and (b) a minimal API client that reads the key from `OPENAI_API_KEY` and the model ID from `MODEL_ID`.

**Why:** the Notion case worked because the engineer shared the goal, added context, and let the model work inside the codebase — not because of a clever prompt. The API path needs the same context, delivered programmatically, so both paths must be reproducible and identical in inputs.

**How to check:** the Python script prints a non-empty answer, and `echo $OPENAI_API_KEY` prints a masked-length string (never the full key in a shared terminal). If the script raises an authentication error, jump to Troubleshooting.

```bash
cat > .env <<'EOF'
OPENAI_API_KEY=<your-api-key>
MODEL_ID=<model-id-from-docs>
EOF
set -a; source .env; set +a
```

```python
# smoke_test.py — one call, one printed answer
import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
resp = client.chat.completions.create(
    model=os.environ["MODEL_ID"],          # exact ID: check the documentation
    messages=[{"role": "user",
               "content": "List 3 risks when adding per-key rate limiting to an API."}],
)
print(resp.choices[0].message.content)
```

**Example:** on success you see three numbered risks. On failure you see an exception class name — read it before changing anything.

For agent workflows that touch a real repository (branching, rebasing, conflict resolution), pair this setup with [Jujutsu or Git: What to Choose for AI Agent Workflows](https://ai-release.org/guides/jujutsu-ili-git-chto-vybrat-dlya-raboty-s-ii-agentami-i.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) so every agent-made change stays bisectable.

## Step 3. Run one acceptance test per use case and read the evidence
![Illustration: Step 3. Run one acceptance test per use case and read the evidence](https://ai-release.org/guides/img/gpt-6-astra-ili-gpt-6-1-sol-chto-vybrat-dlya-raboty-cha-en-3.jpg?v=1792962011)

**What to do:** execute the three tasks from `tasks.md`, requiring the model to verify its own output the way Ramp's Astra did — by actually running the check instead of claiming success.

**Why:** self-verification is the difference between "the interface looks right" and "the system works", the exact gap the Figma experiment highlighted. A model that prints a passing test result gives you evidence; a model that says "done" gives you a claim.

**How to check:** the command below must end with `1 passed`, and `git diff --stat` must show changed files you can read.

```bash
cat > test_ratelimit.py <<'EOF'
from ratelimit import allow  # module the model is asked to create

def test_sixth_call_is_blocked():
    key = "demo-key"
    assert all(allow(key) for _ in range(5))
    assert allow(key) is False
EOF

pytest -q test_ratelimit.py
git diff --stat
```

**Example of success:** `1 passed in 0.04s` plus a diff listing `ratelimit.py | 24 +++++`. **Example of a failure you must not ignore:** `AssertionError` on the sixth call — the limit is off by one, so the work is not accepted regardless of how confident the summary sounds.

- [ ] Chat task answered and factually checkable against the source document
- [ ] Work task produced changed files plus a passing test
- [ ] API task returned parseable output for all 200 items
- [ ] Every result stored in `results/` with the model name and date
- [ ] Any GPT-6.1 Sol column filled only from official documentation

## Troubleshooting

**Problem. The script cannot find the SDK right after installation**
- Error: `ModuleNotFoundError: No module named 'openai'`
- Cause: the command ran in the system Python, not in the virtual environment created in Requirements.
- Solution: `source .venv/bin/activate && pip install --upgrade openai && python -c "import openai; print(openai.__version__)"`.

**Problem. The API rejects the key**
- Error: `openai.AuthenticationError: Error code: 401 - {'error': {'message': 'Incorrect API key provided'...`
- Cause: `OPENAI_API_KEY` is empty, contains quotes, or `.env` was overwritten by a later shell session.
- Solution: `set -a; source .env; set +a; echo ${#OPENAI_API_KEY}` — the number must be greater than zero. Do not echo the key itself into a terminal that logs history.

**Problem. The model ID is rejected**
- Error: `Error code: 404 - The model '<model-id>' does not exist or you do not have access to it`
- Cause: the placeholder from `.env` was passed through unchanged, or your account lacks access to that model.
- Solution: replace `MODEL_ID` with the exact string from the vendor documentation, re-source `.env`, and rerun `python smoke_test.py`. If it still fails, your workspace plan does not include that model — request access before continuing the comparison.

## Summary and next steps
Choose GPT-6 Astra for agentic coding and system-level design work, where the documented behaviour is goal-plus-context work in Codex with self-verification via computer use, and leave the GPT-6.1 Sol column empty until official documentation fills it. Next, re-run Step 3 once a month with the same `tasks.md` so your decision stays based on evidence rather than memory.

## FAQ

**Is GPT-6 Astra really AGI?**
No verdict is possible from our data: the channel's 2Weekly episode reports that Astra scored 99.95% on ARC AGI 3 and that Jensen Huang and OpenAI claim AGI is reached, but those are claims and a single benchmark, not an operational guarantee for your workload.

**What is computer use in the Ramp example?**
It is the model operating the machine itself — running checks in the environment instead of describing them — which is why Astra could build a feature from a one-line request and then confirm its own change worked.

**Which model should I pick for chat only?**
Use the acceptance run from Step 3 with your own chat task; our posts document Astra's engineering behaviour, not its chat quality, and publish nothing about GPT-6.1 Sol, so test both against your own prompt and keep the transcript as evidence.

**Where do I get the exact model IDs and prices?**
From the vendor's official documentation and pricing page — they are deliberately not stated here, because our channel's posts contain neither IDs nor prices and a guessed value would break your integration silently.