# Gemini 4 Carbon vs GPT-6 Astra for Code Security Audits: Which to Choose > AI Release · @ai_release1 · https://ai-release.org/guides/gemini-4-carbon-ili-gpt-6-astra-dlya-audita-bezopasnost_en.html ## TL;DR - Time to complete: 45 minutes (one short audit run on a small module; a full repository takes longer and depends on the model queue). - Difficulty: Intermediate. - You will need: Linux/macOS (or WSL2 on Windows), `git`, Python 3.11+, the `requests` and `jsonschema` packages, an API key for the security model you pick, a test copy of the repository, and about 2 GB of free disk for reports and a sandbox checkout. - Deliverable at the end: a JSON findings report where every finding is either reproduced with a proof of concept (PoC — a concrete request or input that triggers the bug) or explicitly marked “not reproducible”, plus a patch that applies with `git apply` and passes your tests. - Key constraint from the source: the channel post describes **Gemini 4 Carbon** as a single-purpose security model — it finds a vulnerability, confirms it is exploitable, and writes a patch — but publishes **no benchmark scores and no price**. **GPT-6 Astra** has **no security-audit figures** in the source at all. Any number you see quoted elsewhere for these two models must be checked in vendor documentation before you put it in a report. ## Requirements and setup ![Illustration: Requirements and setup](https://ai-release.org/guides/img/gemini-4-carbon-ili-gpt-6-astra-dlya-audita-bezopasnost-en-1.jpg?v=1793263644) - **OS**: Ubuntu 22.04/24.04, macOS 14+, or Windows 11 with WSL2. Check: `uname -a` (Linux/macOS) or `wsl --status` (Windows). - **Git** 2.x: `git --version` must print something like `git version 2.43.0`. - **Python** 3.11+: `python3 --version` must print `Python 3.11.x` or higher. - **Model access — Gemini 4 Carbon**: the source states Carbon is *not* pushed into open retail distribution, unlike the earlier Gemini 4 Argon expansion for paying users and developers two weeks before. So the model ID, endpoint and region availability must be confirmed with the vendor before writing any code; treat them as ``, `` placeholders until confirmed. - **Model access — GPT-6 Astra**: the source has no security-audit results for Astra. Confirm that your plan includes API access to it, and read the model choice notes in [GPT-6 Astra vs GPT-6.1 Sol](https://ai-release.org/guides/gpt-6-astra-ili-gpt-6-1-sol-chto-vybrat-dlya-raboty-cha.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) before you standardize on it for code review. - **Repository**: a clone of the code under review, never a production checkout with live credentials. - **Disk**: at least 2 GB free — `df -h .` shows the `Avail` column. ## Step 1. Scope the repository and choose the model **What to do:** pin the exact code snapshot, limit the audit scope, and write down which model you will use for which stage. ```bash mkdir -p audit/{reports,prompts,patches} git clone https://github.com//.git audit-target cd audit-target git rev-parse HEAD | tee ../audit/commit.txt find . -name '*.py' -not -path './.venv/*' | wc -l ``` **Example:** `git rev-parse HEAD` prints `9f2c1ab4e77d1b0f3a5c8d21f0b6e4c9a1d7f203`. The `find | wc -l` command prints `143`, the number of Python files available — that is your scoping budget, so pick modules (auth, sessions, payments) rather than the whole tree. **Why:** a model finding is only reproducible against a fixed commit. Without `commit.txt` you cannot tell whether a patch failed because the model was wrong or because the code moved. **How to check:** `cat ../audit/commit.txt` must print a 40-character SHA, and `git status` must show a clean working tree. If `git status` lists modified files, stop — you are not auditing a known snapshot. Model comparison, using only what the source post states: | Model | Difference (per the source post) | Price | |---|---|---| | Gemini 4 Carbon | Single-profile security model: finds a vulnerability, confirms it is actually exploitable, and writes the patch. The source reports it leads in three checks — vulnerability detection, exploitability confirmation and code repair. Not distributed through open retail channels. | Not published in the source — confirm on the vendor pricing page | | GPT-6 Astra | No security-audit data in the source. Positioned for work, chat and API use in our Astra/Sol comparison; treat it as a general-purpose reviewer until vendor security benchmarks are confirmed. | Not published in the source — confirm on the vendor pricing page | Decision rule: if your deliverable is “confirmed finding + patch”, start with Carbon for the find-and-confirm stages because that is the only capability the source actually documents for it. Use Astra as a second reviewer when your team already standardizes on it, and treat its output as a lead, not a verdict. Background on assistant families and their coding roles: [AI for programmers: coding assistants in 2026](https://ai-release.org/guides/ii-dlya-koda.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide). **How to check:** write the scope to a file and count it — `ls src/auth src/sessions > ../audit/scope.txt && wc -l ../audit/scope.txt` prints e.g. `2`. Two entries means the scope is fixed and small enough for a first pass. ## Step 2. Run the three-stage audit: find, confirm, patch ![Illustration: Step 2. Run the three-stage audit: find, confirm, patch](https://ai-release.org/guides/img/gemini-4-carbon-ili-gpt-6-astra-dlya-audita-bezopasnost-en-2.jpg?v=1793263651) **What to do:** create a strict output schema, then run the model three times per finding — detection, exploitability confirmation, patch generation. ```bash export SECURITY_MODEL_ENDPOINT='' export SECURITY_MODEL_ID='' export SECURITY_MODEL_KEY='' python3 -m venv .venv && source .venv/bin/activate pip install requests jsonschema python -c "import os; print(os.environ['SECURITY_MODEL_ID'], os.environ['SECURITY_MODEL_ENDPOINT'])" ``` **Example:** the last command prints ` `. If either is empty, your shell session lost the export and every call will fail later with a 404 or 401 — fix it now, not after a 40-minute run. Schema file `audit/prompts/find.json`: ```json { "type": "object", "required": ["findings"], "properties": { "findings": { "type": "array", "items": { "type": "object", "required": ["id", "file", "line", "cwe", "severity", "evidence", "confidence"], "properties": { "id": {"type": "string"}, "file": {"type": "string"}, "line": {"type": "integer"}, "cwe": {"type": "string"}, "severity": {"enum": ["low", "medium", "high", "critical"]}, "evidence": {"type": "string"}, "confidence": {"type": "number"} } } } } } ``` Runner `audit/run_audit.py` (the request body below uses the common chat-completions shape — confirm the exact field names in your vendor’s API reference before the first run): ```python import json, os, pathlib, requests ENDPOINT = os.environ["SECURITY_MODEL_ENDPOINT"] MODEL = os.environ["SECURITY_MODEL_ID"] KEY = os.environ["SECURITY_MODEL_KEY"] def ask(prompt: str) -> dict: r = requests.post( ENDPOINT, headers={"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"}, json={"model": MODEL, "messages": [{"role": "user", "content": prompt}], "response_format": {"type": "json_object"}}, timeout=180, ) r.raise_for_status() return json.loads(r.json()["choices"][0]["message"]["content"]) if __name__ == "__main__": src = pathlib.Path(os.environ["TARGET_FILE"]).read_text() stage = os.environ["STAGE"] # find | confirm | patch schema = json.loads(pathlib.Path(f"audit/prompts/{stage}.json").read_text()) prompt = (f"Stage: {stage}. Commit: {os.environ['AUDIT_COMMIT']}. " f"Return JSON only, matching this schema: {json.dumps(schema)}. " f"For stage 'confirm' every finding needs a concrete request path and preconditions, " f"or the verdict 'not reproducible'. For stage 'patch' return a unified diff only.\n\n{src}") print(json.dumps(ask(prompt))) ``` Run stage one across the scope: ```bash export AUDIT_COMMIT=$(cat audit/commit.txt) for f in src/auth/*.py; do TARGET_FILE="$f" STAGE=find python audit/run_audit.py >> audit/reports/find.jsonl done ``` **Why it works:** the schema is sent inside the prompt and enforced by `response_format`, so the model cannot answer in prose; every finding arrives with a file, line and CWE (Common Weakness Enumeration — the standard bug-class ID, e.g. `CWE-89` for SQL injection), which is what makes triage mechanical instead of a reading exercise. **How to check:** `python -c "import json;[json.loads(l) for l in open('audit/reports/find.jsonl')];print('ok')"` prints `ok`. Then pick the confirmed findings only — anything without a “not reproducible” verdict must be reproduced in Step 3 before it goes into the report. ## Step 3. Verify the finding and the patch ![Illustration: Step 3. Verify the finding and the patch](https://ai-release.org/guides/img/gemini-4-carbon-ili-gpt-6-astra-dlya-audita-bezopasnost-en-3.jpg?v=1793263659) **What to do:** reproduce each finding against a local sandbox, apply only patches that pass `git apply --check`, and re-run tests. ```bash git checkout -b audit/fix-$(date +%F) git apply --3way --check audit/patches/F-001.diff && git apply --3way audit/patches/F-001.diff python -m pytest -q tests/ ``` **Example:** before the patch, record the baseline response of the PoC request, for example `curl -s -o /dev/null -w '%{http_code}\n' "http://127.0.0.1:8080"` returning `302`. After the patch the same request must return the safe status your test asserts (for example `400`). If it still returns `302`, the patch did not fix the bug even though it compiled. **Why:** the source credits Carbon with confirming exploitability, but a model’s “exploitable” claim is a hypothesis; the sandbox run is the evidence. `git apply --check` fails before touching your tree, so a bad diff costs you nothing. **How to check:** `python -m pytest -q tests/` must end with a line like `12 passed in 4.31s` and exit code 0 (`echo $?` prints `0`). Then finish the report checklist: - [ ] `audit/commit.txt` exists and matches `git rev-parse HEAD` at audit time - [ ] every finding has a PoC output or the verdict `not reproducible` - [ ] every patch applied via `git apply --3way --check` - [ ] `pytest` green on the patch branch - [ ] `audit/reports/` archived next to the commit SHA ## Troubleshooting **Problem. The runner stops on the first call with a 404.** - Ошибка: `requests.exceptions.HTTPError: 404 Client Error: Not Found for url: ` - Причина: the model ID or endpoint was copied from general documentation, or Carbon is not enabled on your account — the source notes Carbon is not distributed through open retail channels, so access is granted per account. - Решение: print the values and compare each one with the vendor’s API reference and your console model list: `python -c "import os;print(os.environ['SECURITY_MODEL_ID'], os.environ['SECURITY_MODEL_ENDPOINT'])"`. Request access for the account if the model is absent from that list. **Problem. The model answers in prose and parsing fails.** - Ошибка: `json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)` - Причина: `response_format` was dropped, the prompt did not repeat the schema, or a very large file was truncated mid-answer. - Решение: keep `response_format` in the request, keep the schema text inside the prompt, and split the file per function before sending. Validate with `python -c "import json,jsonschema;jsonschema.validate(json.load(open('audit/reports/one.json')), json.load(open('audit/prompts/find.json')));print('valid')"` — it must print `valid`. **Problem. The generated patch does not apply.** - Ошибка: `error: patch failed: src/auth/session.py:42` followed by `error: src/auth/session.py: patch does not apply` - Причина: the file changed after the audit snapshot, or the model quoted context lines with different indentation than the real file. - Решение: return to the audited snapshot and regenerate — `git stash && git checkout $(cat audit/commit.txt)`, re-run the `patch` stage, then test with `git apply --3way --check audit/patches/F-001.diff` before applying. ## Summary and next steps You now have a pinned commit, a schema-validated findings report, reproduced PoCs and a patch that passes `git apply --check` and your test suite. Next, run the same three stages with GPT-6 Astra as a second reviewer on the same commit and diff the finding lists — the disagreements are your real triage queue. ## FAQ **Can Gemini 4 Carbon replace a human security reviewer?** No. The source describes a model that finds, confirms and patches, but accountability, threat modelling and release sign-off stay with your team; the model’s claims become evidence only after you reproduce them. **Is there published benchmark data comparing Carbon and GPT-6 Astra on security?** The source reports Carbon leading in three checks — detection, exploitability confirmation and code repair — but publishes no scores, and gives no security-audit figures for Astra. Ask both vendors for the benchmark report and cite it with a date. **How much do these models cost?** The source publishes no prices, and Carbon is not sold through open retail channels, so per-account commercial terms apply. Check the vendor pricing page or ask your account manager before quoting any number internally. **Can I send an entire private repository to the model?** Only if your contract and data-retention terms allow it. Run on a sanitized test copy, strip secrets first (`gitleaks detect --source .` or your standard scanner), and confirm retention rules in vendor documentation; for a wider tool landscape see [ChatGPT, Claude, Gemini, DeepSeek: AI assistant comparison](https://ai-release.org/guides/sravnenie-ii-assistentov.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide).