Best code execution sandbox for AI agents
4 models · updated 2026-08-14
The verdict
E2B leads — All 4 models rank E2B the top pick.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank E2B #1 for code execution sandbox for ai agents on ModelsAgree — a unanimous pick. The models' case: Best all-around, purpose-built agent sandbox: secure microVM isolation, fast startup, excellent Python/TypeScript SDKs, reusable templates, filesystem and process APIs. The models' main caveat: Meaningful production concurrency and 24-hour sessions require a $150/month Pro plan, making sustained workloads expensive. The strongest alternative is Daytona — Near-tie with E2B on practitioner value. Source: https://modelsagree.com/best/best-code-sandbox-for-ai-agents (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #1Gemini #1Grok #1
Best all-around, purpose-built agent sandbox: secure microVM isolation, fast startup, excellent Python/TypeScript SDKs, reusable templates, filesystem and process APIs, desktop environments, and the deepest agent-framework ecosystem.
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GPT Best all-around, purpose-built agent sandbox: secure microVM isolation, fast startup, excellent Python/TypeScript SDKs, reusable templates, filesystem and process APIs, desktop environments, and the deepest agent-framework ecosystem.
Claude Purpose-built Firecracker microVM sandboxes designed specifically for AI agent code execution; sub-200ms startup, generous SDK (Python/JS), persistent filesystem, long-running sessions, and a self-hostable open-source core give it the best balance of isolation, speed, and agent-native ergonomics for the typical builder wiring an LLM to run untrusted code.
Gemini Purpose-built for AI agents with first-class Python and TypeScript SDKs, sub-second boot times, hardware-level isolation via Firecracker microVMs, and specialized sandboxes for Jupyter code interpretation, bash execution, and desktop control; rank assumes most agent builders prioritize fast, secure, ephemeral tool execution.
Grok Purpose-built Firecracker microVM isolation for untrusted agent code with sub-200ms starts, mature Python/TS SDKs, pause/resume + snapshots, massive production scale (1B+ sandboxes, high Fortune 100 use), and first-class agent framework integrations including OpenAI Agents SDK; assumes security of generated code is the primary constraint for most practitioners
Where it falls shortper GPT Meaningful production concurrency and 24-hour sessions require a $150/month Pro plan, making sustained workloads expensive.
per Claude Managed pricing scales with concurrent sandbox-seconds and can get expensive at high fan-out; self-hosting the Firecracker stack is real ops work, so it's not for teams wanting zero-infra or the absolute cheapest bulk execution.
per Gemini Not designed for complex persistent multi-container development workspaces or long-term stateful developer environments where background services persist across days.
per Grok No GPU support and session length caps make it suboptimal for long-running ML or always-on persistent workspaces
- 2GPT #2Claude #3Gemini #3Grok #2
Near-tie with E2B on practitioner value; exceptionally complete lifecycle management—snapshots, forks, pause/resume, recovery, resizing—and support for containers, Linux VMs, Windows, and GPUs with straightforward usage pricing.
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GPT Near-tie with E2B on practitioner value; exceptionally complete lifecycle management—snapshots, forks, pause/resume, recovery, resizing—and support for containers, Linux VMs, Windows, and GPUs with straightforward usage pricing.
Grok Sub-90ms starts, unlimited persistence with snapshots/forks, full composable Linux environments including Computer Use and optional GPU, strong agent SDKs and open-source roots; near-tie with E2B for stateful coding agents where speed and environment fidelity outweigh pure isolation
Claude Sandbox infrastructure explicitly targeted at AI agents, with very fast (sub-100ms claimed) stateful sandbox creation, snapshot/fork of environment state, and an OSS core; the fork/snapshot model is genuinely useful for agent branching and reproducible runs, making it a strong agent-native alternative to E2B.
Gemini Leading open-source and self-hostable workspace orchestrator for autonomous coding agents (SWE agents) that need complete persistent repositories, Git workflows, language servers, and multi-cloud infrastructure supporting Docker, Kata, and Sysbox runtimes.
Where it falls shortper GPT Standard organization limits of 4 vCPUs, 8 GB RAM, and 10 GB disk are restrictive for large builds or data-heavy agents.
per Claude Younger ecosystem with a smaller community and fewer battle-tested integrations than E2B/Modal; less proven at large scale in production.
per Gemini Incurs higher architectural complexity and heavier cold starts than lightweight microVM execution APIs, and requires explicit Kata configuration to safely isolate untrusted multi-tenant code.
per Grok Default container-based isolation (Kata optional) is weaker than microVMs for highest-security multi-tenant untrusted code
- 3GPT #3Claude #2Gemini #2Grok #3
Serverless container platform with excellent Python-native ergonomics, fast cold starts via its own filesystem/snapshotting, GPU access, and Sandbox primitives that make spinning up isolated per-agent environments trivial while also handling the surrounding batch/inference workloads; strong for teams that want one platform for both agent sandboxes and heavier compute.
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Claude Serverless container platform with excellent Python-native ergonomics, fast cold starts via its own filesystem/snapshotting, GPU access, and Sandbox primitives that make spinning up isolated per-agent environments trivial while also handling the surrounding batch/inference workloads; strong for teams that want one platform for both agent sandboxes and heavier compute.
Gemini Exceptional developer ergonomics for combining agent code execution with custom runtime dependencies, fast gVisor container isolation, instant auto-scaling, and seamless access to GPU compute for hybrid model-inference and data-processing tasks; near-tie with E2B on execution speed and reliability.
GPT Strongest for massive parallelism and compute-heavy agents, with per-second billing, custom images, persistent volumes, GPU access, network controls, and filesystem, directory, and memory snapshots.
Grok gVisor-isolated serverless sandboxes with production-grade scale, excellent Python ergonomics, seamless GPU access (T4–B200) inside the same boundary, and native agent SDK support; best when agents mix code execution with ML workloads
Where it falls shortper GPT Its default gVisor runtime has Linux syscall compatibility gaps, while the full-VM alternative remains beta and lacks some snapshot and GPU capabilities.
per Claude Python-centric and fully managed/proprietary — no self-host, and non-Python or on-prem/air-gapped requirements are poorly served.
per Gemini Adheres to a serverless dispatch paradigm rather than an interactive full-OS virtual desktop with standard persistent shell sessions; can become costly if workspaces are kept continuously warm.
per Grok Higher CPU cost multiplier and less agent-native than pure sandboxes; isolation is strong but not hardware-virtualized microVM
- 4GPT #4Claude —Gemini —Grok #5
Best persistent-agent design: isolated VMs resume from standby in under 25 ms, scale to zero while retaining memory and filesystem state, and provide MCP, previews, volumes, firewalling, and managed lifecycle APIs.
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GPT Best persistent-agent design: isolated VMs resume from standby in under 25 ms, scale to zero while retaining memory and filesystem state, and provide MCP, previews, volumes, firewalling, and managed lifecycle APIs.
Grok MicroVM perpetual sandboxes with industry-leading ~25ms resume from standby, automatic scale-to-zero, full filesystem/process access, and co-located agent hosting for minimal latency; strong for always-available agent state
Where it falls shortper GPT It has a smaller ecosystem and shorter production track record than the top three, increasing platform and execution risk for conservative teams.
per Grok Managed-only with less transparent isolation details and smaller ecosystem adoption than E2B/Daytona
- 5GPT #5Claude —Gemini #4Grok —
Tailor-made for autonomous AI software engineering with instant microVM snapshot-and-resume capabilities, live port tunneling, session replay/debugging, and built-in tooling optimized for SWE-bench-style coding and testing loops.
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Gemini Tailor-made for autonomous AI software engineering with instant microVM snapshot-and-resume capabilities, live port tunneling, session replay/debugging, and built-in tooling optimized for SWE-bench-style coding and testing loops.
GPT Excellent for software-engineering agents and evaluation fleets, with microVM isolation, blueprints, snapshot branching, suspend/resume, browser support, credential brokering, egress policies, benchmarks, and VPC deployment.
Where it falls shortper GPT The most useful production features require the $250/month Pro tier, and its coding-agent specialization makes it less compelling for general-purpose code interpreters.
per Gemini Closed proprietary service with higher vendor lock-in, higher cost per execution hour, and narrower utility outside autonomous software development workflows.
- 6GPT —Claude #4Gemini —Grok —
Runs sandboxed containers on Cloudflare's edge tightly integrated with Workers, Durable Objects, and the broader Cloudflare AI/agents stack; near-zero-config global deployment, strong isolation, and pay-per-use economics make it excellent for practitioners already building agents on Cloudflare.
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Claude Runs sandboxed containers on Cloudflare's edge tightly integrated with Workers, Durable Objects, and the broader Cloudflare AI/agents stack; near-zero-config global deployment, strong isolation, and pay-per-use economics make it excellent for practitioners already building agents on Cloudflare.
Where it falls shortper Claude Best value only inside the Cloudflare ecosystem; resource/runtime limits and edge-container constraints make it a poor fit for heavy, long-running, or GPU workloads.
- 7GPT —Claude —Gemini —Grok #4
Firecracker microVMs designed as full persistent Linux computers for agents, ~300ms checkpoints with zero idle cost, durable filesystem, and simple API/CLI for long-horizon stateful workflows
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Grok Firecracker microVMs designed as full persistent Linux computers for agents, ~300ms checkpoints with zero idle cost, durable filesystem, and simple API/CLI for long-horizon stateful workflows
Where it falls shortper Grok Creation latency higher than pure ephemeral options and tightly coupled to Fly.io infrastructure
- 8GPT —Claude —Gemini #5Grok —
Excellent microVM-based sandboxing with instant memory snapshotting, live web preview URLs, and low-latency filesystem/terminal APIs optimized for web application generation and full-stack coding agents.
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Gemini Excellent microVM-based sandboxing with instant memory snapshotting, live web preview URLs, and low-latency filesystem/terminal APIs optimized for web application generation and full-stack coding agents.
Where it falls shortper Gemini Heavily optimized for web and application runtimes rather than arbitrary low-level systems programming, data science kernels, or heavy GPU compute workloads.
- 9GPT —Claude #5Gemini —Grok —
The open-source isolation primitives underlying most managed offerings; rolling your own on Firecracker microVMs or gVisor gives maximum control, no per-sandbox vendor markup, full data residency, and proven Google/AWS-grade security boundaries — the right answer for teams with platform engineers and strict cost/compliance needs.
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Claude The open-source isolation primitives underlying most managed offerings; rolling your own on Firecracker microVMs or gVisor gives maximum control, no per-sandbox vendor markup, full data residency, and proven Google/AWS-grade security boundaries — the right answer for teams with platform engineers and strict cost/compliance needs.
Where it falls shortper Claude You build and maintain the orchestration, warm-pooling, filesystem, and SDK yourself — significant engineering investment and not remotely turnkey for a typical app developer.
By use case
How this board's leaders rank when the same four models are asked a more specific question.
| Product | This board | cloud platforms long-running coding | secure sandboxes |
|---|---|---|---|
| E2B | #1 | #2 | #1 |
| Daytona | #2 | #1 | #2 |
| Modal | #3 | #4 | #3 |
| Blaxel | #4 | #3 | #8 |
| Runloop | #5 | #5 | — |
Rank history
Just missed the top 5
GPT Cloudflare Sandbox SDK — excellent VM isolation, global Workers integration, and economics, but Workers lock-in and substantial 2026 API churn reduce general-purpose appeal · Fly Machines — flexible, proven microVM infrastructure, but practitioners must build much of the agent-specific lifecycle, file, snapshot, and orchestration layer themselves
Claude Northflank — solid managed container/microVM platform with sandbox use cases, but less agent-native and not primarily marketed for LLM code execution
Gemini Fly Machines — Offers superior global Firecracker microVM infrastructure, but lacks dedicated agent SDKs and high-level execution primitives out of the box · Cloudflare Sandbox SDK — Excellent edge-native isolation, but tightly bound to Cloudflare Workers and restricted for heavy multi-language runtime dependencies
Grok Vercel Sandbox — excellent Firecracker + ms starts but ecosystem-locked to Vercel users · Northflank — flexible isolation + BYOC/GPU but more general platform than agent-native
By model
ChatGPT
- 1.E2B
- 2.Daytona
- 3.Modal
- 4.Blaxel
- 5.Runloop
Claude
- 1.E2B
- 2.Modal
- 3.Daytona
- 4.Cloudflare Sandbox SDK
- 5.gVisor + Firecracker
Gemini
- 1.E2B
- 2.Modal
- 3.Daytona
- 4.Runloop
- 5.CodeSandbox SDK
Grok
- 1.E2B
- 2.Daytona
- 3.Modal
- 4.Sprites
- 5.Blaxel
Common questions
What is the best code execution sandbox for ai agents according to AI models?
E2B leads. All 4 models rank E2B the top pick. The current top 3: E2B, Daytona, Modal. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which code execution sandbox for ai agents did each AI model pick first?
ChatGPT: E2B. Claude: E2B. Gemini: E2B. Grok: E2B.
What changed in the latest code execution sandbox for ai agents ranking?
In the latest poll (2026-08-14): Daytona climbed 1 spot, Blaxel climbed 2 spots, Runloop climbed 3 spots; Modal dropped 1 spot, Cloudflare Sandbox SDK dropped 1 spot; Sprites and gVisor + Firecracker entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this code execution sandbox for ai agents ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
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Cite this ranking
ModelsAgree, “Best code execution sandbox for AI agents” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-code-sandbox-for-ai-agents (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand