Advanced Pre-training Solution
Garlic reportedly solves pre-training bottlenecks, allowing smaller models to be injected with more knowledge while improving efficiency.
Overview of Garlic Model
The Garlic Model is OpenAI's internal codename for an advanced large language model currently under development. According to exclusive reporting from The Information in December 2025, OpenAI's Chief Research Officer Mark Chen shared details about this model internally, noting strong results in company benchmarks for coding and reasoning tasks.
This development comes amid CEO Sam Altman's reported "code red" initiative to accelerate ChatGPT improvements following Google's significant AI advancements with Gemini 3. The Garlic Model represents OpenAI's strategic response to maintain competitive leadership in the rapidly evolving AI landscape.
A key technical breakthrough reported for Garlic is its solution to pretraining challenges, allowing smaller models to be injected with more knowledge. This approach improves efficiency and potentially reduces costs compared to larger models like GPT-4.5, while maintaining or exceeding performance benchmarks.
Latest Signals (Dec 2025)
Fresh, sourced highlights to anchor the Garlic narrative—kept concise for decision-makers.
Strategic Alert
“Code Red” declared
Performance Claim
Beats Gemini 3
Architecture
Pre-training breakthrough
Release Window
Q1 2026
Market Pressure
Gemini 650M vs ChatGPT 800M
Deployment Goal
Lower $/token
Development Timeline
Track the key milestones and expected release schedule for OpenAI's Garlic Model.
December 2025
Internal Announcement
Mark Chen, OpenAI's Chief Research Officer, shares Garlic Model details with colleagues, noting strong performance in internal benchmarks.
December 2, 2025
"Code Red" Initiative
Sam Altman reportedly declares "code red" to accelerate ChatGPT improvements in response to Google's Gemini 3 success.
Q4 2025 - Q1 2026
Post-Training & Testing
Garlic undergoes post-training with curated data, internal testing, and safety evaluations before public release.
Q1 2026 (Expected)
Public Release
OpenAI plans to release a version of Garlic as soon as possible, potentially branded as GPT-5.2 or GPT-5.5.
Release Watch: What to Monitor
Actionable signals to track as Garlic moves from internal testing to public availability.
Post-training checkpoints
Look for OpenAI mentions of curated post-training runs and safety evals in blog posts or release notes.
Source: The Information, Dec 2025
Model naming decision
Watch for API docs or changelogs that clarify whether Garlic lands as GPT-5.2 or GPT-5.5.
Versioning remains unconfirmed
API & pricing signals
Keep an eye on OpenAI pricing pages; Garlic is positioned as a lower-cost, high-performance option versus GPT-4.5.
Cost efficiency is a stated goal
Benchmark disclosures
Expect official SWE-Bench, GPQA, and ARC-style metrics once OpenAI publishes external evaluations.
No public metrics yet
Competitive Snapshot
Positioning Garlic against current frontier models. All Garlic data is reported/expected until official release.
| Model | Coding | Reasoning | Efficiency | Release |
|---|---|---|---|---|
| Garlic (GPT-5.2/5.5) | Reported advantage | Reported advantage | Cost focus | Q1 2026 target |
| Gemini 3 Pro | Strong | Strong | High-capacity | Released Nov 2025 |
| Claude 4.5 Opus | Reliable | Reasoning-focused | Safety-led | Released |
| GPT-5.1 | Balanced | Balanced | Premium | Released mid-2025 |
Performance Benchmarks
Comparison of Garlic Model against leading AI models. Note: Garlic-specific metrics are based on reported internal evaluations; official benchmarks pending release.
| Benchmark | Garlic (Expected) | Gemini 3 Pro | GPT-5.1 | Claude 4.5 Opus |
|---|---|---|---|---|
| SWE-bench Verified | TBD (Target: >80%) | 76.2% | 76.3% | 80.9% |
| MMMU-Pro (Multimodal) | TBD | 81.0% | 80.8% | 68.0% |
| Humanity's Last Exam | TBD (Target: >37%) | 37.4% | 31.64% | — |
| Coding Tasks | Exceeds Gemini 3 | Baseline | Comparable | Strong |
| Reasoning Tasks | Exceeds Gemini 3 | Baseline | Comparable | Strong |
Note: Specific benchmark scores for the Garlic Model have not been publicly released. Performance claims are based on internal evaluations reported by The Information. Official benchmarks will be published upon model release.
Key Technical Features
Reported innovations and capabilities of the Garlic Model based on available information.
Superior Coding Performance
Designed to excel at coding tasks, outperforming Gemini 3 in OpenAI's internal code generation and debugging benchmarks.
Enhanced Reasoning Capabilities
Built for complex reasoning tasks, with reported improvements over current frontier models in logical analysis and problem-solving.
Cost Efficiency
Expected to offer lower inference costs compared to GPT-4.5 while maintaining or exceeding performance benchmarks.
Agentic Task Support
Aligned with OpenAI's frontier model strategy, supporting advanced agentic tasks with improved instruction-following capabilities.
Safety-First Development
Undergoing comprehensive safety evaluations and post-training refinements before public release.
Practical Use Cases
Where Garlic could unlock the most value once available—prioritized for immediacy and ROI.
Developers & Product Teams
- High-accuracy code generation, refactors, and safety reviews for CI gates.
- On-demand test authoring (unit/integration) to boost coverage with lower token spend.
- Agentic workflows for issue triage, reproduction steps, and suggested fixes.
Enterprises
- Cost-optimized reasoning for decision support, analytics narration, and policy checks.
- Document-heavy flows (contracts, SOPs) with structured extraction and risk flags.
- API-first assistants for ops runbooks, incident diagnostics, and knowledge retrieval.
Research & Data Teams
- Mathematical and scientific reasoning with smaller-model latency and cost.
- SQL/query synthesis plus validation against sandboxed datasets.
- Experiment report drafting with citations to upstream sources or repos.
Adoption Readiness Checklist
Concrete next steps to stay ready for a Q1 2026 drop without overcommitting.
Define eval harness: pick 8–10 coding and reasoning tasks that mirror production use.
Budget planning: reserve API spend for side-by-side trials vs Gemini 3 and current stack.
Latency targets: set SLOs now to measure Garlic’s efficiency claims (cost + speed).
Governance: prep safety guardrails (PII filters, red-team prompts) for new model intake.
Access paths: monitor API/ChatGPT Plus/Enterprise channels for earliest availability.
Benchmark watch: track coding (SWE-bench, HumanEval) and reasoning (HLE, GPQA) updates.
Frequently Asked Questions
Common questions about OpenAI's Garlic Model answered based on current reporting.
What is the Garlic Model?
The Garlic Model is OpenAI's internal codename for a next-generation large language model focused on coding and reasoning tasks. According to reports, it's designed to compete with Google's Gemini 3 and Anthropic's Opus 4.5.
When will the Garlic Model be released?
According to The Information's reporting, OpenAI plans to release a version of Garlic as soon as possible, potentially as GPT-5.2 or GPT-5.5 in early 2026, likely in the first quarter.
Will Garlic be released as GPT-5.2 or GPT-5.5?
The exact version naming hasn't been confirmed. Mark Chen indicated it could be either GPT-5.2 or GPT-5.5. The choice may depend on the magnitude of improvements over GPT-5.1 and marketing considerations.
How does Garlic compare to Gemini 3?
According to OpenAI's internal benchmarks shared by Mark Chen, the Garlic Model outperforms Google's Gemini 3 in coding and reasoning tasks. Specific benchmark numbers have not been publicly released.
What makes Garlic different from GPT-5?
Garlic reportedly solves pre-training challenges, allowing smaller models to contain more knowledge efficiently. This could mean better performance at lower inference costs compared to larger models like GPT-4.5.
Is this website affiliated with OpenAI?
No. GarlicModel.com is an independent news and analysis website with no affiliation, endorsement, or official partnership with OpenAI, Inc. All information is sourced from public reports and media coverage.
Sources & References
All information on this page is based on reporting from verified news sources. We do not create or fabricate information about unreleased products.
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