LongCat-2.0 vs OpenAI Codex: Pricing and Ratings Compared (2026)
Entry prices and free plans as read on each vendor's pricing page, and user ratings as published on review sites, for LongCat-2.0 and OpenAI Codex side by side. This page compares data; it is not a hands-on review.
On the data: On the data below the two are close or not directly comparable, so we do not name a winner for this pair.
Short on time? Here's the quick answer
Based on published vendor pricing and features and on public review data, here's who should pick what:
LongCat-2.0
1.6T MoE model for long-horizon agentic coding
OpenAI Codex
Delegate coding tasks in plain language, get pull requests
| At a Glance | LongCat-2.0 | OpenAI Codex |
|---|---|---|
Starts at | Free plan | Free plan |
Category | AI Coding AI Agents | AI Coding Developer Tools |
Rating | n/a | 4.7/5 G2 4.5 · SourceForge 5.0 5 reviews |
Free plan | Yes | Yes |
Choose LongCat-2.0 or OpenAI Codex?
Choose LongCat-2.0 if
- You need AI coding features specifically.
Choose OpenAI Codex if
- You need AI coding features specifically.
In-Depth Analysis
LongCat-2.0
1.6T MoE model for long-horizon agentic coding
Strengths
- Pro: Open weights under a permissive MIT license, free to download, fine-tune, and self-host
- Pro: Frontier-scale 1.6T Mixture-of-Experts with a 1-million-token context for large codebases and long agent runs
- Pro: Proven real-world usage, having led OpenRouter charts as 'Owl Alpha'
- Pro: Strong focus on autonomous, long-horizon coding and engineering tasks
- Pro: API makes context-cache hits free, lowering cost on repetitive long-context work
Weaknesses
- Con: Running a 1.6T model locally is impractical without serious GPU or accelerator infrastructure
- Con: The hosted API routes data through a China-based provider, a compliance consideration for some teams
- Con: Newer model with a smaller tooling and fine-tune ecosystem than established models
Key features
OpenAI Codex
Delegate coding tasks in plain language, get pull requests
Weaknesses
- Con: Meaningful use requires a paid ChatGPT subscription such as Plus, Pro, Business, or Enterprise, since access on the Free and Go tiers is very limited.
- Con: The cloud sandbox runs with internet access disabled during task execution by default, so any task that needs to install dependencies or call external APIs requires you to manually configure domain allowlists and permitted HTTP methods per environment.
- Con: Codex executes autonomously in isolated containers and hands back a finished pull request, so you cannot steer or correct it mid-task the way you can with interactive assistants like Cursor; you only review the changes after the run completes.
- Con: The Codex CLI assumes a Unix-style environment, so Windows users must run it through WSL2 rather than natively, adding setup friction.
Key features
Key Takeaways
LongCat-2.0
- Worth considering for AI coding
OpenAI Codex
- Worth considering for AI coding
What the data shows
On the data below the two are close or not directly comparable, so we do not name a winner for this pair.
- Only OpenAI Codex has a published user rating (4.7/5 from 5 reviews); LongCat-2.0 has none we can show.
- Both have a free plan.
Criteria: user rating (only above 20 reviews on both sides, gap of 0.2 or more), entry price (only on the same billing basis), free plan. How we rate
Frequently Asked Questions
How do LongCat-2.0 and OpenAI Codex compare in 2026?
On the data below the two are close or not directly comparable, so we do not name a winner for this pair. Only OpenAI Codex has a published user rating (4.7/5 from 5 reviews); LongCat-2.0 has none we can show. Both have a free plan.
What are LongCat-2.0 and OpenAI Codex used for?
LongCat-2.0: 1.6T MoE model for long-horizon agentic coding. OpenAI Codex: Delegate coding tasks in plain language, get pull requests.
What does LongCat-2.0 cost vs OpenAI Codex?
LongCat-2.0 has no public paid-plan price on file. OpenAI Codex has no public paid-plan price on file.
What's the main difference between LongCat-2.0 and OpenAI Codex?
LongCat-2.0's main strength: open weights under a permissive MIT license, free to download, fine-tune, and self-host. OpenAI Codex focuses on delegate coding tasks in plain language, get pull requests.
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