OpenAI Codex reached 74.9% accuracy on SWE-bench Verified benchmarks in 2025, powering GitHub Copilot’s expansion to over 20 million users. The platform achieved 90% adoption among Fortune 100 companies while developers reported 55% faster task completion rates. This analysis examines verified performance data, market positioning, and enterprise implementation results from 2024-2025.
OpenAI Codex Key Statistics
- OpenAI Codex achieved 74.9% on SWE-bench Verified using GPT-5-Codex architecture as of 2025
- GitHub Copilot surpassed 20 million users in July 2025, adding 5 million users in three months
- Developers using Codex-powered tools completed tasks 55% faster with 50% quicker time-to-merge
- The AI code tools market reached $6.04 billion in 2024, projected to hit $26.03 billion by 2030
- 76% of developers now use or plan to use AI coding tools according to Stack Overflow’s 2024 survey
OpenAI Codex Training Data and Architecture
OpenAI trained the original Codex model on 159 GB of Python code extracted from 54 million public GitHub repositories. The dataset included unique Python files under 1 MB, with filtering applied to remove auto-generated content and files with excessively long lines.
The 2025 iteration, codex-1, builds upon OpenAI’s o3 architecture with reinforcement learning optimization specifically for software engineering tasks. The model supports 12 programming languages and processes up to 192,000 tokens in context length.
| Training Parameter | Value |
|---|---|
| Python Training Data Size | 159 GB (179 GB before deduplication) |
| GitHub Repositories Used | 54 million public repositories |
| Programming Languages Supported | 12+ languages |
| Base Architecture | GPT-3 fine-tuned (original), o3/o4-mini (2025) |
| Maximum Context Length | 192,000 tokens (codex-1) |
OpenAI Codex Performance Benchmarks
The codex-1 model scored 72.1% on SWE-bench Verified, surpassing the previous o3 model’s 71.7% score. At a pass rate of 8, configuration accuracy reaches 83.86%, demonstrating substantial improvement through iterative attempts.
GPT-5-Codex advanced these metrics to 74.9% on SWE-bench Verified, representing a 20.3 percentage point improvement over GPT-4’s 54.6%. Internal SWE engineering exams showed a 75% success rate for codex-1.
| Benchmark | Codex-1 Score | GPT-5-Codex Score |
|---|---|---|
| SWE-bench Verified (pass@1) | 72.1% | 74.9% |
| SWE-bench Verified (pass@8) | 83.86% | 85% |
| Internal SWE Engineering Exams | 75% | N/A |
| First-Attempt Success Rate | 37% | N/A |
| Success with Retry (100 samples) | 70.2% | N/A |
GitHub Copilot Adoption Statistics
GitHub Copilot surpassed 20 million all-time users by July 2025, adding 5 million users within three months. The platform maintains a 90% adoption rate among Fortune 100 companies.
Enterprise customer usage increased by approximately 75% quarter-over-quarter. By early 2025, the tool achieved a fourfold increase in total users compared to the previous year.
| Adoption Metric | 2024 Data | 2025 Data |
|---|---|---|
| Total Users | 15 million | 20+ million |
| Paid Subscribers | 1.3 million | 1.3+ million |
| Enterprise Organizations | 50,000 | 50,000+ |
| Fortune 100 Adoption Rate | 90% | 90% |
| Year-over-Year Growth | 400% | 33% (Q2-Q3) |
OpenAI Codex Developer Productivity Metrics
Developers using Codex-style assistants completed tasks 55% faster with approximately 50% quicker time-to-merge. The code suggestion acceptance rate reached 88% across implementations.
Cisco reported cutting code review times by 50% after deploying Codex across its engineering organization. Internally at OpenAI, nearly all engineers now use Codex, resulting in 70% more pull requests merged weekly.
| Productivity Metric | Value |
|---|---|
| Task Completion Speed Improvement | 55% faster |
| Time-to-Merge Reduction | ~50% quicker |
| Code Suggestion Acceptance Rate | 88% |
| Code Review Time Reduction (Cisco) | 50% |
| Pull Request Increase (OpenAI Internal) | 70% more per week |
| Developer Satisfaction Improvement | 60-75% |
AI Coding Tools Market Growth
The global AI code tools market was valued at $6.04 billion in 2024 and is projected to reach $37.34 billion by 2032, growing at a CAGR of 25.62%. Alternative projections estimate the market reaching $26.03 billion by 2030.
North America holds a dominant position with a market share of 36-43%, driven by the concentration of technology infrastructure and significant investments in AI research. Cloud deployment accounts for 81.20% of the market share.
| Market Metric | 2024 | 2030 Projection |
|---|---|---|
| Global AI Code Tools Market Size | $6.04 billion | $26.03 billion |
| Compound Annual Growth Rate (CAGR) | N/A | 27.1% |
| North America Market Share | 36-43% | N/A |
| Cloud Deployment Share | 81.20% | N/A |
Developer AI Tool Usage Trends
Stack Overflow’s 2024 Developer Survey comprising responses from over 65,000 developers across 185 countries revealed that 76% of developers use or plan to use AI tools in their development process, up from 70% in 2023.
Among AI tool users, 82% employ these tools for writing code. GitHub Copilot represents the second most widely used AI tool at 41% adoption. Productivity emerged as the top AI benefit identified by 81% of respondents.
| Survey Metric | Percentage |
|---|---|
| Developers Using or Planning to Use AI Tools | 76% |
| Professional Developers Currently Using AI Tools | 62% |
| Developers Using AI for Writing Code | 82% |
| GitHub Copilot Usage Among AI Tool Users | 41% |
| Productivity as Top AI Benefit Identified | 81% |
| Developers Not Viewing AI as Job Threat | 70% |
OpenAI Codex Pricing and Availability
Codex is included in ChatGPT Plus, Pro, Business, Edu, and Enterprise plans. Business and Enterprise plans offer additional credit purchases for extended usage.
The Codex CLI, IDE extension, and web interfaces all support authentication through existing ChatGPT accounts across supported subscription tiers.
| Access Tier | Monthly Cost | Codex Access |
|---|---|---|
| ChatGPT Plus | $20 | Included |
| ChatGPT Pro | $200 | Included + Enhanced Compute |
| ChatGPT Business/Enterprise | Custom | Included + Admin Tools |
| ChatGPT Edu | Institutional | Included |
Enterprise Implementation Results
Cisco deployed Codex across its entire engineering organization, achieving a 50% reduction in code review times and compressing project timelines from weeks to days.
At Duolingo, engineers new to the codebase experienced a 25% speed increase while median code review turnaround dropped by 67%. Pull request volume increased by 70%.
Accenture announced plans to roll out GitHub Copilot to 50,000 developers, reflecting enterprise-scale confidence in Codex-powered tooling. License utilization when made available reached 80%.
| Enterprise Implementation | Reported Outcome |
|---|---|
| Cisco Code Review Implementation | 50% reduction in review time |
| Duolingo Onboarding (New Engineers) | 25% speed increase |
| Duolingo Code Review Turnaround | 67% reduction |
| Duolingo Pull Request Volume | 70% increase |
| Accenture Planned Deployment | 50,000 developers |
| License Utilization When Made Available | 80% |
OpenAI Codex Security Considerations
An internal GitHub study found that approximately 0.1% of the generated code contained direct copies from the training data. Research from New York University found that approximately 40% of code generated by Copilot in scenarios relevant to high-risk Common Weakness Enumerations included vulnerabilities or exploitable design flaws.
Despite these concerns, developers retain 88% of Copilot-generated code in their final submissions. Python code analysis showed that 29.1% of generated code contained potential security weaknesses.
| Security Metric | Value |
|---|---|
| Training Data Direct Copy Rate | 0.1% |
| Python Code with Potential Security Weaknesses | 29.1% |
| Code Retained in Final Submissions | 88% |
| Vulnerable Code in High-Risk CWE Scenarios | ~40% |
FAQ
How many users does GitHub Copilot have?
GitHub Copilot surpassed 20 million users by July 2025, adding 5 million users in three months. The platform maintains 1.3 million paid subscribers and serves over 50,000 enterprise organizations.
What is OpenAI Codex’s accuracy on coding benchmarks?
OpenAI Codex achieved 74.9% on SWE-bench Verified using GPT-5-Codex architecture. The codex-1 model scored 72.1% on the same benchmark, with accuracy reaching 83.86% at pass@8 configuration.
How much faster do developers work with OpenAI Codex?
Developers using Codex-powered tools completed tasks 55% faster with approximately 50% quicker time-to-merge. Cisco reported a 50% reduction in code review times after deployment.
What is the AI coding tools market size?
The global AI code tools market reached $6.04 billion in 2024 and is projected to grow to $26.03 billion by 2030, representing a compound annual growth rate of 27.1%.
How many developers use AI coding tools?
76% of developers use or plan to use AI tools according to Stack Overflow’s 2024 survey of 65,000 developers. Among AI tool users, 82% employ these tools for writing code.