Best code execution sandbox for AI agents
4 models · updated 2026-07-15
The verdict
E2B leads — All 4 models rank E2B the top pick.
As of 2026-07-15, 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 Modal — Sandboxes as a primitive inside a broader serverless platform — gVisor isolation, sub-second starts, easy GPU attachment, image building in code, and. 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 The de facto standard purpose-built for this exact job — open-source Firecracker microVM sandboxes with ~150ms cold starts, first-class Python/JS SDKs, filesystem + process + PTY APIs, pause/resume persistence, and desktop sandboxes; battle-tested at scale by major agent products (Perplexity, Manus-class workloads), so the sharp edges a typical practitioner hits are already sanded down. Near-tie with Modal at the top.
Gemini Serves as the industry benchmark for lightweight, secure, and developer-friendly ephemeral code execution. Powered by Firecracker microVMs, it offers strong hardware-level isolation, sub-second startup times (~150ms), and seamless API/SDK integration.
Grok Purpose-built for AI agents with Firecracker microVMs for strong kernel-level isolation, excellent Python/JS SDKs, fast boot (~150ms), pause/resume capabilities, and widespread real-world adoption (e.g., Perplexity, enterprise use); open-source core makes it accessible and integrable for typical practitioners needing secure untrusted LLM code execution.
Where it falls shortper GPT Meaningful production concurrency and 24-hour sessions require a $150/month Pro plan, making sustained workloads expensive.
per Claude Self-hosting the open-source stack is genuinely hard (it's designed around their cloud), and long-lived, stateful, or GPU-heavy workloads push you toward pricier or awkward territory — it's optimized for ephemeral interpreter-style runs.
per Gemini Designed primarily for ephemeral session-based execution, making it poorly suited for persistent, long-running agent workspaces that require complex state persistence across sessions.
per Grok Session/time limits (e.g., 24h max on many plans) and no native GPU support limit it for long-running or compute-heavy ML tasks.
- 2GPT #3Claude #2Gemini #3Grok #3
Sandboxes as a primitive inside a broader serverless platform — gVisor isolation, sub-second starts, easy GPU attachment, image building in code, and massive burst scale, so agent code execution and the rest of your AI infra (batch jobs, inference, training) live in one system with excellent Python DX.
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Claude Sandboxes as a primitive inside a broader serverless platform — gVisor isolation, sub-second starts, easy GPU attachment, image building in code, and massive burst scale, so agent code execution and the rest of your AI infra (batch jobs, inference, training) live in one system with excellent Python DX.
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.
Gemini The premier choice for compute-heavy and GPU-intensive agent execution, providing gVisor-based isolation and friction-free serverless access to high-performance GPUs (from T4s to H200s) directly within the secure sandbox.
Grok Python-first serverless with gVisor isolation, seamless GPU access, dynamic environments, and proven scaling for ML/data pipelines; high value for practitioners doing code execution alongside inference/training in one ecosystem.
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 Proprietary and Python-centric; if all you need is a lightweight code interpreter, you're adopting a whole compute platform, and gVisor's syscall emulation occasionally breaks weird binaries that a real VM would run.
per Gemini Lacks agent-native features like real-time filesystem-watching, live browser previews, or built-in Git workspace state management, requiring significant integration boilerplate.
per Grok Less specialized for untrusted AI agent code (gVisor vs microVM) and no easy BYOC.
- 3GPT #2Claude #3Gemini #4Grok #5
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.
Claude Fastest sandbox creation in the field (sub-100ms class), open-source core, declarative images, snapshots, and an API designed agent-first rather than retrofitted; the strongest open alternative if you want control over your stack without building on raw Firecracker yourself.
Gemini The strongest open-source, self-hostable option for stateful agent workspaces, featuring rapid resume times (~27-90ms) and built-in support for environment snapshots and branching.
Grok Fast starts, reproducible/stateful environments with good persistence for development-like agent sessions; solid alternative for speed-focused code execution without heavy overhead.
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 Youngest track record of the top three — smaller ecosystem, fewer proven at-scale deployments, and its default container-based isolation is a weaker boundary than microVMs for genuinely hostile multi-tenant code.
per Gemini Uses Docker-based isolation by default, which shares the host OS kernel and offers weaker security boundaries compared to hardware-level microVMs.
per Grok Weaker isolation (primarily Docker/containers) vs microVM options for highest security needs.
- 4GPT #4Claude —Gemini —Grok #4
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 Perpetual sandboxes with sub-25ms resume from standby (zero compute idle cost), microVM isolation, and state preservation tailored for responsive production AI agents; strong for low-latency practitioner workflows.
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 Newer entrant with less proven long-term enterprise track record compared to leaders.
- 5GPT —Claude —Gemini —Grok #2
Production-grade microVM options (Firecracker/Kata/gVisor) with unlimited sessions, BYOC/self-hosting, full platform integration (DBs, GPUs, deployments), and strong compliance/isolation for scaling agent workflows beyond prototypes; serves practitioners needing reliable infra.
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Grok Production-grade microVM options (Firecracker/Kata/gVisor) with unlimited sessions, BYOC/self-hosting, full platform integration (DBs, GPUs, deployments), and strong compliance/isolation for scaling agent workflows beyond prototypes; serves practitioners needing reliable infra.
Where it falls shortper Grok Broader platform scope means steeper learning curve for simple code-execution-only use cases.
- 6GPT —Claude —Gemini #2Grok —
Replaces complex workspace state management by bringing serverless developer mechanics to agent sandboxes, offering durable block storage and auto-scaling to zero when idle with instant wake-ups.
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Gemini Replaces complex workspace state management by bringing serverless developer mechanics to agent sandboxes, offering durable block storage and auto-scaling to zero when idle with instant wake-ups.
Where it falls shortper Gemini Not built for high-performance or GPU-backed compute, making it inappropriate for intensive local model training, inference, or heavy mathematical simulations.
- 7GPT —Claude #4Gemini —Grok —
Sandboxed code execution co-located with the edge platform many agents already run on — per-sandbox container isolation orchestrated by Durable Objects, preview URLs, a code-interpreter API, and zero extra vendor if you're already on Workers; pricing and global placement are hard to beat for lightweight interpreter workloads.
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Claude Sandboxed code execution co-located with the edge platform many agents already run on — per-sandbox container isolation orchestrated by Durable Objects, preview URLs, a code-interpreter API, and zero extra vendor if you're already on Workers; pricing and global placement are hard to beat for lightweight interpreter workloads.
Where it falls shortper Claude Only compelling inside the Cloudflare ecosystem — container resource caps and platform limits make it wrong for heavy, long-running, or GPU workloads, and it's the least mature entry here.
- 8GPT —Claude #5Gemini —Grok —
The enterprise pick — Hyper-V-isolated session pools with sub-second allocation from warm pools, per-second billing, native LangChain/Semantic Kernel integrations, and the compliance/network-integration story (VNet, Entra) that regulated shops require before letting LLM-generated code run anywhere. Rank assumes the practitioner values governance over DX; on pure ergonomics it trails the top three.
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Claude The enterprise pick — Hyper-V-isolated session pools with sub-second allocation from warm pools, per-second billing, native LangChain/Semantic Kernel integrations, and the compliance/network-integration story (VNet, Entra) that regulated shops require before letting LLM-generated code run anywhere. Rank assumes the practitioner values governance over DX; on pure ergonomics it trails the top three.
Where it falls shortper Claude Azure-shaped friction everywhere — clunkier APIs and slower iteration than the startup platforms, and little reason to choose it if you're not already committed to Azure.
- 9GPT —Claude —Gemini #5Grok —
Optimized for AI coding agents that need to build, test, and run full-stack web applications, featuring built-in browser previews, live filesystem watching, and instant snapshotting.
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Gemini Optimized for AI coding agents that need to build, test, and run full-stack web applications, featuring built-in browser previews, live filesystem watching, and instant snapshotting.
Where it falls shortper Gemini Higher resource overhead and cost compared to lightweight sandboxes, making it overkill for basic scripting or non-web tasks.
- 10GPT #5Claude —Gemini —Grok —
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.
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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.
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 |
| Modal | #2 | #4 | #3 |
| Daytona | #3 | #1 | #2 |
| Blaxel | #4 | #3 | #8 |
| Northflank | #5 | #7 | — |
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 Fly.io Machines — excellent fast-boot Firecracker VMs and a real contender, but it's raw infrastructure — you build the sandbox lifecycle, snapshotting, and agent ergonomics yourself
Gemini Runloop — offers strong VM environments but suffers from higher creation latency and cost compared to E2B and Upstash Box · Blaxel — features impressive sub-25ms startup latency but has a smaller community ecosystem and higher potential idle costs
Grok Fly.io Sprites — strong persistence but narrower ecosystem · Vercel Sandbox — ecosystem-tied, good for web/JS but less general AI agent focus
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.Azure Container Apps dynamic sessions
Gemini
- 1.E2B
- 2.Upstash Box
- 3.Modal
- 4.Daytona
- 5.CodeSandbox SDK
Grok
- 1.E2B
- 2.Northflank
- 3.Modal
- 4.Blaxel
- 5.Daytona
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, Modal, Daytona. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-15. 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 (2026-07-15): Blaxel climbed 3 spots, Azure Container Apps dynamic sessions climbed 2 spots; Runloop dropped 2 spots; Northflank and Upstash Box 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 →
Cite this ranking
ModelsAgree, “Best code execution sandbox for AI agents” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-15. 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