Fix ChatGPT Code Interpreter Sandbox Timeout Mid-Run

Q: What is the actual timeout, in seconds?

As of June 2026 the sandbox kills a run after roughly **120 seconds of sustained compute** in the ChatGPT app. Free is the tightest in practice (fewer resources, lower daily upload count), Plus / Team / Pro / Enterprise get more headroom, but the per-run wall-clock cap is finite everywhere. OpenAI does not publish exact per-tier numbers and they shift, so treat any single cell over 30 seconds as risky regardless of tier.

Q: My uploaded file vanished mid-conversation. Did the timeout cause it?

No, that is a separate sandbox-restart issue. See [ChatGPT file disappears from conversation](/en/articles/chatgpt-file-disappears-from-conversation/) — re-upload and persist to parquet before any heavy step.

Code Interpreter kills your Python job halfway with 'execution timed out' — usually a CPU-bound loop, hung network call, or memory spike inside the ~120s, ~1GB sandbox. The fast fix: split into checkpointed cells.

Published: May 24, 2026 Updated: Jun 15, 2026 Author: AI Productivity Guide Team 🌐 查看中文版本

You ask ChatGPT to crunch a 200MB CSV, fit a model, and chart the residuals. Twenty seconds in, the response cell goes red: The code execution timed out or Sandbox terminated. Retrying.... The model retries once, hits the same wall, and falls back to printing pseudocode instead of running it.

Fastest fix: stop asking for one giant cell. Tell the model to split the job into separate cells, each under 30 seconds, and persist intermediate results to /mnt/data/ as parquet. That single change resolves most timeouts because the sandbox caps each run, not the whole conversation.

Code Interpreter (labeled Advanced Data Analysis in the tool picker, same feature) runs your Python inside an ephemeral container with a hard wall-clock cap — roughly 120 seconds of sustained compute per run as of June 2026, on top of a tight ~1GB RAM ceiling and limited CPU. The container itself lives for the whole conversation but is killed after about 20 minutes idle. Most timeouts are not “ChatGPT is broken”; they are a script doing too much in one cell, holding the entire dataframe in RAM, or blocking on a network call the sandbox cannot make.

Which bucket are you in?

Time the failure with a stopwatch first — wall-clock vs. instant death tells you almost everything.

Symptom	Most likely cause	Jump to
Red after ~60-120s, no output	One cell doing everything (CPU-bound)	Step 1
Dies in under 10s, “timed out”	Memory spike, OOM-killed	Step 2
Hangs the full timeout, no progress lines	Network call / blocked `pip install`	Step 6
Slow on big frame, instant on `df.head()`	`.apply(lambda)` / `.iterrows()`	Step 3
Stalls right after `plt.show()`	Too many plot points	Step 4
Every cell re-reads the upload from scratch	Lost state across turns	Step 5

Common causes

Ordered by what we see most often in real sessions.

1. Single cell tries to do everything end-to-end

The model writes one giant cell: load CSV → clean → feature-engineer → train → cross-validate → plot. Even on a 50MB file this routinely passes the sandbox’s per-run compute cap. Code Interpreter does not stream partial output, so when the cell dies you lose all intermediate state.

How to spot it: The failing cell has more than ~40 lines of Python, multiple for loops over the dataframe, and at least one fit / cross_val_score call.

2. Network call inside the sandbox

The sandbox has no outbound internet access — confirmed in OpenAI’s own docs as of June 2026. Some calls fail instantly, but DNS-style lookups in code like requests.get("https://api.example.com/...") or yfinance.download(...) often do not fail fast — they hang until the sandbox watchdog kills them around the 120s mark. Same for pip install against PyPI, which the sandbox cannot reach.

How to spot it: The cell imports requests, urllib, httpx, yfinance, pandas_datareader, or calls pd.read_csv("http..."). There is no progress output before the timeout.

3. Memory spike kills the container before the timer

Big joins or df.pivot() over a wide table can blow past the ~1GB RAM ceiling in the ChatGPT app. The kernel is OOM-killed and ChatGPT surfaces it as a generic timeout, even though no time elapsed. (The OpenAI API’s Code Interpreter tool exposes a memory_limit of 1g/4g/16g/64g, but the consumer ChatGPT app gives you no such knob — you are stuck near the bottom tier.)

How to spot it: Cell dies in under 10 seconds with a timeout message. Re-running on a small slice (df.head(1000)) finishes instantly.

4. Pandas chained operations on a wide dataframe

df.apply(lambda row: ...) over 1M+ rows is single-threaded Python. A vectorized version finishes in 2s; the lambda version runs long enough to hit the per-run timeout. The model often writes the lambda version because it is shorter.

How to spot it: .apply(lambda ...) or .iterrows() on a frame with more than ~100k rows.

5. Uploaded file lost between cells, code re-uploads implicitly

Each new conversation turn can spin up a fresh sandbox. If turn 3 references a file uploaded in turn 1, the model may re-read it, re-parse it, and re-build features from scratch on every turn — burning the timeout budget redoing setup. See ChatGPT code execution loses uploaded files between cells for the sandbox-state nuance.

How to spot it: Every cell starts with pd.read_excel("/mnt/data/<file>.xlsx") and a fresh df.head(). Variable state from previous cells is gone.

6. Plot rendering with too many points

matplotlib scatter plots with 500k+ points serialize the figure to PNG on the sandbox CPU. That alone can eat 30s+ before the rest of the cell runs.

How to spot it: Last visible action before timeout is plt.show() or sns.scatterplot. Reducing to 5k points removes the timeout.

Before you start

Confirm your plan tier — Free has stricter sandbox limits and a low daily upload count (about 3 files/day on Free) versus Plus/Team/Pro/Enterprise.
Note whether the timeout is deterministic (every time, same cell) or intermittent (sometimes finishes).
Capture the offending cell’s code before re-prompting; ChatGPT often rewrites the cell on retry and you lose evidence.
Check the file size of any upload. As of June 2026 the hard cap is 512MB per file, but spreadsheets and CSVs realistically choke around ~50MB depending on row width, and text/document files are capped at 2M tokens each. You can attach up to 10 files per message and roughly 80 every 3 hours.

Information to collect

The exact error string (“execution timed out”, “sandbox terminated”, “kernel restarted”).
Approximate wall-clock time before the timeout fired (use a stopwatch — it tells you OOM vs. wall-clock).
Dataframe shape (df.shape) and dtype memory (df.memory_usage(deep=True).sum() / 1e6 MB).
Whether the cell does any network I/O or pip install.
The specific model in use — GPT-5.5 is the ChatGPT default as of June 2026 (picker: Instant / Thinking / Pro). Model choice affects how the planner splits work into cells.

Step-by-step fix

Ordered cheapest-first.

Step 1: Force the model to split the job into checkpointed cells

Add to your prompt:

Run this analysis in separate cells, each under 30 seconds:
Cell 1: Load and inspect the file. Print shape and dtypes only.
Cell 2: Clean and downcast. Persist df to /mnt/data/clean.parquet.
Cell 3: Feature engineering. Save features.parquet.
Cell 4: Train and evaluate. Print metrics.
Cell 5: One chart only.

Do not combine cells. Persist intermediate state to /mnt/data/ between cells.

This converts one 120s cell into five 20s cells, and intermediate parquet files survive sandbox restarts.

Step 2: Downcast and slice before any heavy step

Before training or pivoting, force the dataframe to a smaller footprint:

import pandas as pd
df = pd.read_csv("/mnt/data/input.csv")
for col in df.select_dtypes("float64"):
    df[col] = pd.to_numeric(df[col], downcast="float")
for col in df.select_dtypes("int64"):
    df[col] = pd.to_numeric(df[col], downcast="integer")
for col in df.select_dtypes("object"):
    if df[col].nunique() / len(df) < 0.5:
        df[col] = df[col].astype("category")
print(df.memory_usage(deep=True).sum() / 1e6, "MB")

A 1.8GB frame routinely drops to 300MB. The same cell now fits in budget.

Step 3: Replace `apply(lambda)` with vectorized operations

Ask the model:

Rewrite this cell with no .apply() and no .iterrows(). Use vectorized
pandas / numpy ops only. If a step cannot be vectorized, do it on
df.sample(50_000) and explain the trade-off.

This single rewrite often turns a 90-second timeout into a 3-second success.

Step 4: Cap matplotlib point count

For any scatter on a large frame:

sample = df.sample(min(5000, len(df)), random_state=0)
plt.scatter(sample["x"], sample["y"], s=4, alpha=0.4)
plt.show()

Or switch to hexbin / 2dhist which aggregates before rendering. Render time goes from 30s to under 1s.

Step 5: Persist intermediate state to `/mnt/data/`

The sandbox CWD /mnt/data/ survives most sandbox restarts within the same conversation. After each heavy step:

df_clean.to_parquet("/mnt/data/clean.parquet")
features.to_parquet("/mnt/data/features.parquet")

Next cell starts with df = pd.read_parquet(...) — sub-second. A timeout in cell 4 no longer wipes cells 1-3.

Step 6: Move network calls out of the sandbox

If the script needs external data, do not ask Code Interpreter to fetch it. Instead:

Download the data yourself (locally / with curl).
Upload the file to the chat.
Tell the model to read from /mnt/data/<file> only.

For paywalled or auth-required sources, see ChatGPT browsing tool blocked by paywall sites — the same boundary applies to Code Interpreter. OpenAI’s own Code Interpreter tool docs confirm the sandbox has no outbound network; the web-browsing tool is a separate, non-Python surface.

Step 7: If everything fits but still times out, restart the sandbox

In a new turn:

Reset the Python sandbox. Confirm with print("ok") in a fresh cell, then
re-load /mnt/data/clean.parquet.

Stale kernel state (hung threads, leaked file handles from earlier turns) is a real source of slowdowns that a fresh kernel fixes instantly. Note the flip side: the container is killed after roughly 20 minutes idle, so if you walk away mid-analysis and come back to “session expired”, your /mnt/data/ files are gone too — re-upload and re-run from your saved parquet, not from memory.

Verify

The previously failing cell now completes with output visible inline.
Total wall-clock for the analysis is under your sandbox budget with headroom (target: each cell under 30s).
Re-running the same prompt in a new conversation reproduces success — proves it is not a flaky kernel.
Memory usage after downcast is under 500MB for a dataframe that previously needed 1.5GB+.

Long-term prevention

For any file over 50MB, start the conversation by asking the model to print shape, dtypes, and a downcast plan before touching the data.
Keep the rule “one analytical action per cell” in your prompt template. Loads, cleans, joins, models, plots are all separate cells.
Always persist to parquet after any step that took more than 10 seconds; treat the sandbox as ephemeral.
For repeated workflows, prefer a local Jupyter / Colab notebook and use ChatGPT only for code generation and review.
Never let the model write requests.get(...) inside the sandbox; pre-download or skip the call.
When using Advanced Data Analysis with very large files, split the upload first (split -n l/4 big.csv) and process chunks separately.

Common pitfalls

Telling the model “make it faster” without a concrete budget — it produces marginal tweaks instead of restructuring.
Re-running the same failing prompt 5 times and burning your message cap; see ChatGPT message cap reached for the cost.
Assuming pip install <pkg> is free; on some plans it triggers a sandbox refresh that costs 10-20s.
Asking for a chart with 1M raw points “for accuracy” — the eye cannot distinguish 5k points from 1M, but the sandbox can.
Forgetting that /mnt/data/ is wiped when you start a brand-new conversation. Files survive within a thread, not across threads.
Trusting a “completed successfully” message when no plot or table actually rendered — re-check the output cell.

FAQ

Q: What is the actual timeout, in seconds?

As of June 2026 the sandbox kills a run after roughly 120 seconds of sustained compute in the ChatGPT app. Free is the tightest in practice (fewer resources, lower daily upload count), Plus / Team / Pro / Enterprise get more headroom, but the per-run wall-clock cap is finite everywhere. OpenAI does not publish exact per-tier numbers and they shift, so treat any single cell over 30 seconds as risky regardless of tier.

Q: Can I increase the timeout or RAM in the ChatGPT app?

No. The consumer app gives you no timeout or memory dial. You restructure instead: smaller files, downcast, vectorize, split cells, persist parquet. The only place a memory knob exists is the OpenAI API Code Interpreter tool, where memory_limit can be set to 1g/4g/16g/64g — that is a developer/API path, not something you can toggle inside ChatGPT.

Q: My cell finishes locally in 3 seconds but times out in ChatGPT — why?

The sandbox has fewer CPU cores than your laptop, no GPU, ~1GB RAM, and slower disk. Operations dominated by single-threaded Python (lambdas, loops) hit a much lower ceiling. Vectorize and the gap usually disappears.

Q: Why did my files vanish after I stepped away for lunch?

The container is reclaimed after about 20 minutes idle. When it expires, /mnt/data/ is wiped and you may see “session expired” or a fresh sandbox on the next message. Re-upload the source file and re-run from your saved parquet checkpoints.

Q: Does Advanced Data Analysis have the same limits as Code Interpreter?

Yes — they are the same feature. “Code Interpreter” was the original name; “Advanced Data Analysis” is the label you see in the tool picker. The sandbox, packages, and timeout behavior are identical.

Q: My uploaded file vanished mid-conversation. Did the timeout cause it?

No, that is a separate sandbox-restart issue. See ChatGPT file disappears from conversation — re-upload and persist to parquet before any heavy step.

Tags: #ChatGPT #code-interpreter #timeout #python #Troubleshooting