Agentic Index
JetBrains AI vs OpenAI Codex (2026)
JetBrains AI last verified ·OpenAI Codex last verified
Both give you an agent plus a CLI, and they differ on where the work executes. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
JetBrains keeps it local and in the IDE, Junie running a plan then execute then verify loop with bring your own key and MCP, from $10 a month on AI Pro. Codex pushes work to cloud sandboxes for pull request review, multi file edits and test execution, included in ChatGPT plans from Free, with Plus at $20 a month and Pro from $100. Local and predictable against asynchronous and parallel.
On the Agentic Index coding agent ranking, neither JetBrains AI nor OpenAI Codex clears the bar, which asks for all five merge loop capabilities documented in full. Neither documents knowledge grounding and RAG in full, nor testing, debugging and optimization. 3 of the 96 vendors in the lane clear it. See the coding agent ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. JetBrains AI and OpenAI Codex are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose JetBrains AI if
- You want the agent working in your IDE on your machine, not in somebody's cloud sandbox.
- Flat monthly pricing matters more to you than elastic parallelism.
- Bring your own key gives you model control that a bundled agent does not.
Choose OpenAI Codex if
- You want to fire off work and review finished pull requests rather than supervise a loop.
- Cloud sandboxes running tests in parallel match how your team ships.
- Your team already pays for ChatGPT and the agent comes with it.
| Feature | J JetBrains AI |
O OpenAI Codex |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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JetBrains AIIntegrations & Tool Calling The agent acts through the IDE's own machinery. It edits across files, runs builds, tests and terminal commands, drives the real IDE debugger through a bundled Debugger MCP Toolset and reaches external systems through configured MCP servers, and the IDE itself is exposed as an MCP server to other clients. Sourcejetbrains.com/help/ai-assistant/junie-agent; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexIntegrations & Tool Calling MCP connects outside services, with approved server lists under enterprise control, and plugins and connectors extend the agent with tool calls that have side effects, which require approval when they advertise destructive annotations. Third party integrations cover GitHub, GitLab, Slack and Linear. The agent runs shell commands under an OS sandbox, uses a web search tool with cached, live and disabled modes, and reaches the browser and computer use surfaces. Sourcelearn.chatgpt.com/codex/extend/mcp, /codex/third-party and /codex/agent-approvals-securityread 2026-08-30 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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JetBrains AIWorkflow Orchestration Junie autonomously plans and executes complex multi step actions from a single prompt, making large scale edits across files, running tests and terminal commands and using external tools while reporting progress. It works in a plan, execute and verify loop where the plan is an editable document approved before code is written, and the agent iterates when tests fail. Sourcejetbrains.com/help/ai-assistant/junie-agent and junie.jetbrains.com/docs; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexWorkflow Orchestration The agent sustains long autonomous sessions across many sequential tool calls, with configurable subagents, git worktrees for parallel work, projects and chats organizing multi step work, long running work and scheduled tasks, and the desktop app running agent threads side by side. The cloud agent works asynchronously in isolated containers so many tasks proceed in parallel, and multi agent tooling is among the stable CLI surfaces. Sourcelearn.chatgpt.com/codex/agent-configuration/subagents, /codex/environments/git-worktrees and /codex/long-running-workread 2026-08-30 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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JetBrains AITriggers & Channel Coverage The same agent engine is invoked from IDE chat, the Junie tool window, a standalone CLI in any terminal, CI/CD pipelines via GitHub Actions and GitLab, and third party ACP clients. Remote and asynchronous runs let a long task started on a workstation be monitored elsewhere. Sourcejunie.jetbrains.com/docs and jetbrains.com/help/ai-assistant/junie-agentread 2026-08-30 |
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OpenAI CodexTriggers & Channel Coverage Codex runs in the ChatGPT desktop app, a Codex CLI, an IDE extension, ChatGPT on the web, Codex cloud, and a Remote mode driven from a phone. Scheduled tasks run work on a timetable, long running work is supported, a GitHub Action and non-interactive mode cover CI, and third party integrations reach GitHub, GitLab in beta, Slack and Linear, with notifications closing the loop. Sourcelearn.chatgpt.com/codex/automations, /codex/github-action and /codex/third-partyread 2026-08-30 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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JetBrains AIKnowledge Grounding & RAG The open file and selection reach Junie as automatic IDE context, with other files attached by @ mention, AGENTS.md guidelines added to every task and MCP servers for outside data. The Junie plugin also has an opt in setting for embeddings based codebase indexing. How that index persists or stays current is not described, so context is assembled per task. Sourcejetbrains.com/help/ai-assistant/junie-agent and junie.jetbrains.com/docs/junie-plugin-project-settings; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-10-25 |
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OpenAI CodexKnowledge Grounding & RAG Context is assembled for each run. The agent reads the repository, resolves @ mentioned files, calls MCP servers for outside sources and uses a web search tool whose cached mode draws on a web index OpenAI maintains. AGENTS.md and Rules give the agent standing instructions. OpenAI does not describe an index that Codex keeps over the customer's own code or documents. Sourcelearn.chatgpt.com/docs/agent-configuration/agents-md, /docs/extend/mcp and the docs llms.txt indexread 2026-09-29 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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JetBrains AIMemory & State Persistence Persistent project context is carried in an AGENTS.md instructions file that Junie adds to every task and that can be version controlled, and sessions can be managed and their status checked through slash commands. There is no cross session agent memory or learned state store. Sourcejetbrains.com/help/ai-assistant/junie-agent; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexMemory & State Persistence Codex writes its own memories: once enabled, it turns useful context from eligible prior chats into local memory files under CODEX_HOME, redacting secrets and updating in the background after a chat goes idle. Memories live in the local Codex home, /memories chooses per chat whether a chat may read existing memories or feed future ones, and managed configuration can pin the feature on or off. Unused memories retire from consolidation after memories.max_unused_days, 30 by default and capped at 365, memories.max_rollout_age_days bounds the chats considered, and the files can be opened for review. The feature is off by default. AGENTS.md holds standing rules, separate from memory. Sourcelearn.chatgpt.com/docs/customization/memories and /docs/config-file/config-referenceread 2026-09-29 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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JetBrains AIHuman Oversight & Guardrails Junie asks permission by default before running bash commands, changing files, performing file operations or using external tools, with approve, reject or always allow per command, and the user can open the diff or the full command in the editor before deciding. Changes can be rolled back per file or all at once, and .aiignore restricts which files the agent may process. Brave mode is an explicit opt in that JetBrains warns can cause data loss. Sourcejetbrains.com/help/ai-assistant/junie-agent; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexHuman Oversight & Guardrails Two independent layers govern the agent: sandbox mode controlling what it can technically do, and approval policy controlling when it must ask, with presets from read-only through workspace-write to danger-full-access and a granular policy that keeps chosen approval categories interactive while auto rejecting others. An Auto-review reviewer agent can evaluate eligible approval requests against a published policy checking for data exfiltration, credential probing, security weakening and destructive actions, failing closed on parse or session errors. Destructive MCP and app tool calls always require approval, .git, .agents and .codex are protected read-only inside writable roots, and enterprise managed requirements override local settings. Sourcelearn.chatgpt.com/codex/agent-approvals-security and /codex/sandboxing/auto-reviewread 2026-08-30 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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JetBrains AISecurity, Identity & Governance User level controls cover .aiignore, which excludes files and directories from agent processing, AGENTS.md, which governs agent behavior under version control, and per command approval gates and .junie/mcp scoping, which limit what the agent may do. JetBrains offers an AI Enterprise tier but does not say what it adds. There is no SOC 2 or ISO attestation specific to the AI product, and no SSO, RBAC or admin governance surface. Sourcejetbrains.com/help/ai-assistant/junie-agent; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexSecurity, Identity & Governance OpenAI's trust portal at trust.openai.com lists SOC 2 Type 2, SOC 3, ISO/IEC 27001, 27017, 27018, 27701 and 42001, CSA STAR and FedRAMP 20x, with the SOC 2 report scoped to the API Platform, ChatGPT Enterprise, ChatGPT Edu and ChatGPT Team, the plans and API key path Codex runs on, though the portal does not name Codex separately. Access controls include managed configuration that overrides local settings, roles and workspace permissions, SCIM and EKM on Enterprise, workload identity federation, service accounts, plugin, connector and skill controls, approved MCP server lists, a Compliance API with audit events, Private Link, IP allowlisting and mutual TLS. Sourcetrust.openai.com and learn.chatgpt.com/docs/enterprise/managed-configurationread 2026-09-29 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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JetBrains AIObservability & Auditability Junie CLI keeps each session's events log and a readable transcript on disk. It saves the full prompt, response and model usage history for resumable sessions and stores subagent transcripts, so a run can be reconstructed afterwards. These are local files rather than a central audit log. In the IDE, changes stay reviewable as diffs and can be rolled back one by one. Sourcejunie.jetbrains.com/docs/junie-cli; junie.jetbrains.com/docs/junie-cli.htmlread 2026-10-25 |
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OpenAI CodexObservability & Auditability Opt in OpenTelemetry export emits an event catalog that includes codex.tool_decision, recording approved or denied and whether the source was configuration or the user, codex.tool_result with duration, success and output snippet, codex.api_request, codex.sse_event and codex.user_prompt, with counter and duration histogram metrics, exportable over OTLP to a collector the customer controls. Enterprise adds a Compliance API and audit events, workspace analytics and an Analytics API. Telemetry is off by default and prompt text is redacted unless explicitly enabled. Sourcelearn.chatgpt.com/codex/agent-approvals-security and /codex/enterprise/compliance-apiread 2026-08-30 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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JetBrains AIDeployment & Data Residency Junie runs locally in the IDE or terminal, and Junie CLI can use Junie Local, a local inference engine for Apple Silicon, or a local Ollama, LM Studio or LiteLLM server, so code and inference stay on the customer's own machines. The Junie GitHub Action runs entirely on the customer's GitHub runners. JetBrains does not say what organization level hosting options AI Enterprise adds. Sourcejunie.jetbrains.com/docs/junie-local, custom-llm-ollama and junie-on-github; junie.jetbrains.com/docs/junie-local.htmlread 2026-09-29 |
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OpenAI CodexDeployment & Data Residency Local environments run entirely on the developer's machine through the CLI, IDE extension and desktop app under an OS enforced sandbox using Seatbelt on macOS, bwrap and seccomp on Linux and a native Windows sandbox or WSL2, with dev container support and a published secure devcontainer reference; cloud environments run in isolated OpenAI managed containers with configurable internet access and domain allow lists. Enterprise adds Private Link, IP allowlisting, mutual TLS, IP egress ranges and Amazon Bedrock as a model provider. Sourcelearn.chatgpt.com/codex/environments/modes, /codex/agent-approvals-security and /codex/amazon-bedrockread 2026-08-30 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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JetBrains AIPrebuilt Agents, Templates & Packs Several ready to use coding agents are selectable in AI Assistant chat, namely Junie, Claude Agent, Codex and the Copilot agent, and Junie ships a dedicated code review agent beside its coding agent. Most of these agents do the same job. Sourcejetbrains.com/help/ai-assistant/agents and junie.jetbrains.com/docs/junie-review-agent; jetbrains.com/help/ai-assistant/agents.htmlread 2026-09-29 |
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OpenAI CodexPrebuilt Agents, Templates & Packs Skills and plugins extend Codex, with dedicated build guides, a plugin architecture and submission path, enterprise plugin management, and plugin and skill controls for administering approved catalogs, and a Claude Code plugin can be submitted as well. Subagents are configurable per project, and Codex Micro ships as a packaged capability. Sourcelearn.chatgpt.com/codex/skills-and-plugins, /codex/build-plugins and /codex/enterprise/plugin-managementread 2026-08-30 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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JetBrains AIModel Flexibility & Routing The model is selected from a list in the chat input with a configurable reasoning level, spanning frontier models from OpenAI, Anthropic, Google and xAI. The CLI adds bring your own key, including OpenRouter, and local models can be served through Ollama, LiteLLM or LMStudio. Sourcejetbrains.com/help/ai-assistant/junie-agent and junie.jetbrains.com/docs; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-08-30 |
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OpenAI CodexModel Flexibility & Routing Local Codex clients let the customer choose the model maker, accepting custom model providers in config.toml, each defined by base URL, wire API and authentication, and OpenAI's own example configures Mistral's API and a local Ollama endpoint beside an OpenAI proxy. OSS mode runs Codex against a local open source provider, Ollama or LM Studio, chosen per run with --local-provider or set as the default. Within OpenAI's own family the model is selectable per session and per workspace. Amazon Bedrock is a built in provider that serves OpenAI models on AWS. Sourcelearn.chatgpt.com/docs/config-file/config-advanced (Custom model providers, OSS mode) and /docs/modelsread 2026-09-29 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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JetBrains AIAPIs, SDKs & MCP Extensibility MCP client support spans the IDE and CLI with project and user scoped mcp.json configs, a registry of pre configured servers and an MCP Installation Assistant. The IDE itself runs as an MCP server exposing IDE tools to external clients including Claude Code, Codex and Copilot CLI, a bundled Debugger MCP Toolset exposes debugger control, and the agent speaks ACP so the same engine drives IDE chat, the tool window and the CLI. Sourcejunie.jetbrains.com/docs/junie-cli-mcp-configuration and jetbrains.com/help/ai-assistant/mcp; junie.jetbrains.com/docs/junie-cli-mcp-configuration.htmlread 2026-08-30 |
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OpenAI CodexAPIs, SDKs & MCP Extensibility Developers build on a Codex SDK, an App Server, an MCP Server, a GitHub Action and a non-interactive mode, alongside MCP client support with approved server lists, hooks that fire on lifecycle events, Site tools via WebMCP, Record and Replay, and a Python SDK with auth. The CLI is open source in Rust at github.com/openai/codex with a published default reviewer policy, and the documentation offers an llms.txt index and a markdown version of every page. Sourcelearn.chatgpt.com/codex/codex-sdk, /codex/mcp-server and /codex/open-sourceread 2026-08-30 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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JetBrains AITesting, Debugging & Optimization Debug mode drives the IDE debugger against the customer's running program to inspect runtime state, and the agent runs tests and terminal commands as it works. That is debugging, with the agent checking its own changes against the customer's existing gates. There is no harness, scored test set or optimization loop for evaluating Junie's behavior, and the benchmark results JetBrains publishes are its own measurements. Sourcejetbrains.com/help/ai-assistant/junie-agent; jetbrains.com/help/ai-assistant/junie-agent.htmlread 2026-09-29 |
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OpenAI CodexTesting, Debugging & Optimization Codex reviews code changes before commit, a Codex Security plugin and CLI run scans, deep scans and bulk scans in CI with a security workbench and finding export, and the agent runs tests and iterates on failures; Auto-review evaluates individual agent actions against a risk policy at runtime. Those tools check the customer's code and gate the agent's actions, and there is no harness to evaluate, replay or regression test the agent's own behavior. Sourcelearn.chatgpt.com/codex/code-review and /codex/securityread 2026-08-30 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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JetBrains AIBrowser & Computer Use Junie CLI's /demo command launches the app in a Docker virtual machine with Chromium and a desktop. A computer use model drives the interface and records screenshots and video. It is a demo workflow and needs Docker. In the IDE, the agent works through the IDE, terminal and debugger APIs instead. Sourcejunie.jetbrains.com/docs/junie-cli-demo; junie.jetbrains.com/docs/junie-cli-demo.htmlread 2026-10-25 |
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OpenAI CodexBrowser & Computer Use Browser and Computer Use let the agent work in a browser and operate a computer, alongside a browser extension for signed in browser workflows and Appshots. Browser and Computer Use activity is treated as its own traffic surface outside the command network proxy, with its own feature settings and workspace policies, and managed configuration lets administrators set feature requirements for browsers and Computer Use. Sourcelearn.chatgpt.com/codex/computer-use, /codex/browser and /codex/agent-approvals-securityread 2026-08-30 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | J JetBrains AI |
O OpenAI Codex |
|---|---|---|
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Entry price Lowest public entry point |
AI Free at $0, then AI Pro from $10 a month. | Included in ChatGPT plans. Free is $0 and Go is $8 a month, with GPT-6 Luna in the desktop app subject to rollout. Plus is $20, Pro starts at $100 ($100, $200 or $500), and Business is $20 per user a month billed annually ($25 monthly). Enterprise and Edu are sold through sales, and an API key pays per token. |
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Pricing confidence How public the numbers are |
Public, exact | Public, partial |
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Billing Primary billing axis |
A monthly subscription per tier that carries a quota of AI credits, each worth $1, with extra credits bought beyond the quota. | A ChatGPT plan subscription with an included Codex usage quota, plus usage beyond the quota paid with ChatGPT credits or, on the API key path, at per token API rates. |
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Variable cost Workload / overage exposure |
Low variable cost | High variable cost |
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Free tier / trial Try before you buy |
Free tierTrial
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Free tier
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Buying motion Self-serve vs sales call |
Self-serve | Self-serve |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
JetBrains AI
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· Deployment / data residency
Junie Local, the fully on device version of the Junie coding agent, moved from experimental to stable on Windows PCs with NVIDIA RTX GPUs of at least 24 GB and added native support for NVIDIA RTX Spark PCs on Windows on Arm.
Source -
· Pricing / packaging
Junie Lite is a new free tier for the Junie coding agent, running on a free model in every JetBrains account with no trial or card required.
Source -
· Integrations
JetBrains updated its AI Assistant to natively integrate Claude Agent, powered by Anthropic's Agent SDK, directly into the IDE chat.
Source
OpenAI Codex
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· MCP / tool calling / API
Codex CLI 0.164.0 lets enterprise MCP connections authenticate with eligible Codex account grants or a dedicated sign in, including OAuth clients that require secrets.
Source -
· Deployment / data residency
Codex CLI 0.161.0 lets the Bedrock Mantle provider accept AWS GovCloud regions, so Codex can send inference to OpenAI models hosted in Bedrock GovCloud.
Source -
· Agent capability
OpenAI shipped GPT-6.1 Sol into Codex on desktop and CLI, describing it as near top tier performance at lower cost for long running work, and made it the default model in the CLI.
Source
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