AI code-execution sandbox

Together Code Interpreter

Together Code Interpreter is a session-based API that runs LLM-generated Python in an isolated sandbox, billed at a flat $0.03 per 60-minute session.

Last verified 2026-08-09

Together Code Interpreter (TCI) is the code-execution API from Together AI, built for running model-generated Python at high concurrency in reinforcement-learning and agent pipelines. Billing is per session, a 60-minute reusable execution environment, rather than per vCPU. Together also offers a separate Together Code Sandbox with usage-based vCPU and RAM pricing for longer-lived workloads.

Key specs

IsolationManaged isolated sandbox
Pricing modelPer session ($0.03 / 60-min session)
GPUNo
Session limit60-minute reusable sessions
PersistenceSession-scoped (reusable within 60 min)
LanguagesPython
MCP supportn/a
DeploymentManaged cloud (API)
Billing$0.03 per 60-min session
LanguagePython

Strengths

  • Flat, predictable $0.03 per session
  • Built for high-concurrency execution
  • Simple API from the Together stack

Trade-offs

  • Python only
  • No published cold-start figure
  • No GPU in the interpreter

Best for: RL pipelines and agents that need cheap, high-concurrency Python execution priced per session.

How Together Code Interpreter works

TCI exposes code execution as a simple API call: you create a session, an isolated environment with a 60-minute lifespan, and call it to run Python, streaming back stdout, stderr and evaluated output. Sessions are reusable and can build on prior executions, and the design targets high concurrency (hundreds of simultaneous executions), which is why it shows up in reinforcement-learning and agent-evaluation pipelines. It is Python-only today. For longer-lived or non-Python workloads, Together offers a separate Code Sandbox with usage-based vCPU and RAM pricing.

Pricing in practice & watch-outs

Billing is simple: $0.03 per session, where a session is a 60-minute reusable environment, so cost scales with how many execution environments you spin up rather than raw compute time. That makes budgeting easy for batch and RL workloads. The constraints are scope: Python only, no GPU in the interpreter, and no published cold-start figure, so latency-sensitive interactive use should be validated first. If you are already on the Together stack for inference, running generated code in the same place is the natural fit.

Head-to-head

Alternatives to Together Code Interpreter

See the full Together Code Interpreter alternatives comparison →

Sources: Together Code Interpreter docs ↗ · Together Code Sandbox & Interpreter announcement ↗

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