Coding · Head-to-head
Ollama vs OpenAI Codex
Ollama (freemium, AI Score 8.8/10) vs OpenAI Codex (freemium, AI Score 8.8/10). Side-by-side pricing, features, pros and cons, and which to pick.
The verdict
Pick Ollama if…
- →you want our editor's pick for this category
- →your primary use case is anyone who wants open-weight models running on their own hardware for privacy, offline work or zero marginal cost, with a cloud fallback for models that will not fit.
Pick OpenAI Codex if…
- →your primary use case is teams already standardised on chatgpt who want the coding agent bundled with the seats they pay for, and who want the same agent in a terminal, an editor and a pull request.
Side-by-side specs
| Spec | Ollama | OpenAI Codex |
|---|---|---|
| Category | Coding | Coding |
| Pricing model | freemium | freemium |
| Headline pricing | Free, with Pro at $20/mo and Team at $25/seat/mo for cloud usage | Included with ChatGPT Free, Go, Plus, Pro, Business, Edu and Enterprise plans |
| Free tier | Yes, and it is the main event. Local inference is free forever on your own hardware. The paid tiers exist to buy cloud capacity, and their allowances are published only as multipliers of an unstated base. | Yes. OpenAI documents Codex as included on the ChatGPT Free plan, with access described as restricted to basic tasks. |
| AI Score | 8.8/10 | 8.8/10 |
| Best for | Anyone who wants open-weight models running on their own hardware for privacy, offline work or zero marginal cost, with a cloud fallback for models that will not fit. | Teams already standardised on ChatGPT who want the coding agent bundled with the seats they pay for, and who want the same agent in a terminal, an editor and a pull request. |
| Editor's pick | ✓ Yes | — |
| Use cases | development agents productivity | development agents productivity |
| Date added | 2026-08-02 | 2026-08-02 |
Pros and cons
🦙
Ollama
Coding · freemium
Pros
- ✓Local inference costs nothing per token and keeps prompts and files on the machine
- ✓Works offline, which Ollama calls out specifically for mission-critical work
- ✓Acts as the runtime under other agents, including ones named on its own front page, so it slots into existing stacks
- ✓Cloud fallback covers models too large for a laptop without switching to a different tool or vendor
- ✓States that prompt and response data is never logged or trained on, with zero data retention on the Team plan
Cons
- ×Cloud allowances are published only as multipliers ("50x more than Free", "5x more than Pro") against a base quantity Ollama never states
- ×The $100 Max tier is currently listed as paused for new sign-ups, so the top individual plan may not be available
- ×Local model quality is capped by your RAM and GPU, and open weights still trail frontier hosted models on the hardest tasks
- ×Comfort with a terminal, model files and quantisation choices is assumed, which puts it out of reach for non-technical users
🧑💻
OpenAI Codex
Coding · freemium
Pros
- ✓Included in ChatGPT plans from the free tier upward, so most organisations already own it
- ✓Covers the terminal, the editor, a hosted cloud runner, the ChatGPT apps and GitHub pull request review from one entitlement
- ✓AGENTS.md keeps agent configuration in the repo and under version control rather than in someone's local settings
- ✓Three GPT-5.6 variants let a team route cheap work to Luna and hard work to Sol instead of paying one rate for everything
- ✓Overflow credits are priced per million tokens, which makes the marginal cost of extra work legible
Cons
- ×Published limits are ranges per rolling five-hour window that span two orders of magnitude, so the included allowance is not knowable in advance
- ×Usage depends on which model variant a task routes to, and routing is not fully under the user's control
- ×Pro pricing is presented as tiers rather than a single figure, so the real cost of the heavy plan needs checking at the point of sale
- ×The agent is tied to a ChatGPT account, which is awkward for teams that standardised on a different assistant vendor
Related comparisons
Updated 2026-08-02. Spec data sourced from official product pages and tracked in our public directory at /tools.