/modal

name: modal
description: Use when the user needs to run isolated code remotely — a disposable container, optional GPU access (T4 → H100), or a safer place for untrusted / heavy code. Prefer local execution for normal repo work; use Modal sandboxes for isolation, hardware access, or one-shot heavy compute.
triggers:
  - "modal sandbox"
  - "remote python"
  - "sandbox execution"
  - "isolated code run"
  - "gpu sandbox"
  - "h100"
  - "a100"
  - "remote container"
  - "ephemeral container"
  - "run untrusted code"

Modal Sandboxes

Disposable remote containers (with optional GPU) via Modal, paid per call in USDC. No Modal account, no GPU procurement.

**Base only in wallet mode; fine on an API key.** `sol.blockrun.ai` carries the `/v1/modal/*` routes but has no Modal backend configured, so every action — create, exec, status, terminate — answers `503`. That reads as "the sandbox service is down" rather than "wrong chain", which is exactly the wrong conclusion to act on: retrying will not help. The tool checks the active chain first and says so. Switch with `blockrun_wallet action:"chain" chain:"base"`. Prices below are Base prices and include its per-transaction fee.

READ THIS BEFORE SETTING `timeout`

**`timeout` is the BILLED lifetime, charged upfront in full, and never refunded — not an idle timeout.** Above 300s the price switches from a flat rate to **per-hour billing for the entire duration you ask for**, whether you use it or not. Terminating early refunds nothing.

That makes `timeout` the single most expensive field in this MCP:

| what you ask for | what you pay | |---|---| | `{ timeout: 300 }` | **$0.0110** | | `{ timeout: 300, gpu: "A100" }` | **$0.2010** | | `{ timeout: 600, gpu: "A100" }` | **$1.1677** | | `{ timeout: 300, gpu: "T4", cpu: 8, memory: 32768 }` | **$0.3343** | | `{ timeout: 86400, gpu: "H100" }` | **$216.0010** |

A 24h H100 sandbox costs **$216 upfront, non-refundable**, even if your job finishes in a minute.

**So: ask for the time you need, not a safe-looking ceiling.** Need 20 minutes of H100? `timeout: 1200` is $3.00, not $216. Keep `timeout ≤ 300`, `cpu ≤ 1` and `memory ≤ 1024` and you stay on the flat rate.

How to Call from MCP

// 1. Create — timeout: 300 keeps you on the FLAT rate ($0.0110, or $0.2010 with A100).
//    Anything above 300, or above 1 CPU or 1024 MB, bills hourly for the full requested lifetime, no refund.
blockrun_modal({ path: "sandbox/create", body: {
  image: "python:3.11",
  gpu: "A100",
  timeout: 300,
  setup_commands: ["pip install torch transformers"]
}})
// returns { sandbox_id, ... }

// 2. Exec
blockrun_modal({ path: "sandbox/exec", body: {
  sandbox_id: "sb_abc...",
  command: ["python", "-c", "import torch; print(torch.cuda.get_device_name(0))"]
}})

// 3. Terminate
blockrun_modal({ path: "sandbox/terminate", body: { sandbox_id: "sb_abc..." } })

Endpoint Catalog

| Path | Method | Body | Price | |---|---|---|---| | `sandbox/create` | POST | `{ image?, timeout?, cpu?, memory?, gpu?, setup_commands? }` | **depends on `timeout`, `gpu`, `cpu` and `memory` — see below** | | `sandbox/exec` | POST | `{ sandbox_id, command: ["python","-c","..."], timeout? }` | $0.0020 | | `sandbox/status` | POST | `{ sandbox_id }` | $0.0020 | | `sandbox/terminate` | POST | `{ sandbox_id }` | $0.0020 |

`sandbox/create` pricing is bimodal

**`timeout ≤ 300s`, `cpu ≤ 1` and `memory ≤ 1024` — flat rate, charged once:**

| gpu | price | |---|---| | *(none, CPU)* | $0.0110 | | `T4` | $0.0510 | | `L4` | $0.0810 | | `A10G` | $0.1010 | | `A100` | $0.2010 | | `H100` | $0.4010 |

**Anything longer or larger — per-hour × the full requested lifetime, never below the flat rate, upfront, no refund:**

| gpu | per hour | 1h | 24h (max) | |---|---|---|---| | *(none, CPU)* | $0.25 | $0.2510 | $6.0010 | | `T4` | $4.00 | $4.0010 | $96.0010 | | `L4` | $4.50 | $4.5010 | $108.0010 | | `A10G` | $5.00 | $5.0010 | $120.0010 | | `A100` | $7.00 | $7.0010 | $168.0010 | | `H100` | $9.00 | $9.0010 | **$216.0010** |

Hours are exact, not rounded up — `timeout: 1800` on `A100` is 0.5h = $3.5010. Every figure above includes the $0.001 flat transaction fee. Max `timeout` is 86400 (24h).

Field Reference

| Field | Default | Notes | |---|---|---| | `image` | `python:3.11` | Any public Docker image. `nvidia/cuda:12-runtime` if you bring GPU code. | | `timeout` | 300 | **BILLED lifetime in seconds — charged upfront for the full amount, never refunded.** NOT idle eviction: you pay for what you ask for, not what you use. `≤300` = flat rate; `>300` switches to per-hour billing (see the tables above). Max 86400 (24h). This is the field that turns a $0.01 sandbox into a $192 one. | | `cpu` | 1 | CPU cores | | `memory` | 512 | Memory in MB | | `gpu` | none | `T4` / `L4` / `A10G` / `A100` / `H100` — those five only. Anything else is rejected: `{"gpu":"A100-80GB"}` returns HTTP 400 *"Unsupported GPU type. Allowed: T4, L4, A10G, A100, H100"*. Drives the price hard — see the tables above. | | `setup_commands` | `[]` | Shell commands run once during sandbox provisioning | | `command` (exec) | required | Array form: `["python","-c","print(2+2)"]` |

Worked Examples

1. Quick Python eval

const { structuredContent: sb } = await blockrun_modal({ path: "sandbox/create", body: {} })
await blockrun_modal({ path: "sandbox/exec", body: {
  sandbox_id: sb.sandbox_id,
  command: ["python", "-c", "import numpy; print(numpy.__version__)"]
}})
await blockrun_modal({ path: "sandbox/terminate", body: { sandbox_id: sb.sandbox_id } })

**Cost: $0.0150** — create $0.0110 + exec $0.0020 + terminate $0.0020. Every call carries the $0.001 transaction fee, so three calls pay it three times; batch your work into one `exec` rather than several.

2. GPU inference, A100, with deps pre-installed

blockrun_modal({ path: "sandbox/create", body: {
  gpu: "A100",
  timeout: 1200,
  memory: 16384
}})

Then `sandbox/exec` with your inference command.

**Cost: