This section provides an in-depth look into the primary developers, release statuses, underlying architectural principles, and officially stated core capabilities of GPT-5.3, Gemini 3 Deep Think, and GLM-5, offering foundational technical details for each.
GPT-5.3, developed by OpenAI, is presented in various forms, including GPT-5.3-Codex, GPT-5.3-Codex-Spark, and a rumored general-purpose model internally codenamed "Garlic" 1. OpenAI is the primary developer behind all these variants 2.
Developer and Release Status: GPT-5.3-Codex was officially launched on February 5, 2026, making it available across all Codex platforms (app, CLI, IDE extension, web) for paid ChatGPT plans, with API access anticipated in the weeks following its initial release 2. A specialized, faster version, GPT-5.3-Codex-Spark, was released as a research preview on February 12, 2026 3. This optimized model, designed for faster inference, is accessible to ChatGPT Pro users, with API access rolling out to select design partners 3. The more general GPT-5.3, codenamed "Garlic," remains officially unannounced by OpenAI, with information stemming from leaked reports suggesting a focus on cognitive density rather than solely parameter count 1. Rumors indicate potential preview access for partners in late January 2026, full API availability in February 2026, and free-tier integration by March 2026 1.
Key Architectural Principles: GPT-5.3-Codex is built upon the GPT-5 architecture, integrating the advanced coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT-5.2 into a single, faster model 2. The rumored "Garlic" model adopts a "High-Density Philosophy," moving away from increasing parameter counts to emphasizing "cognitive density," aiming for a "smarter and denser" architecture 1. This approach is reported to achieve "GPT-6 level" reasoning while being faster and more cost-effective than its predecessor, GPT-5.2 1.
Enhanced Pre-Training Efficiency (EPTE), a rumored feature for "Garlic," allows for approximately 6x more knowledge density per byte through intelligent pruning of redundant neural pathways, active knowledge compression, and the use of curated training data such as verified scientific papers, high-level code repositories, and synthetic data from previous reasoning models 1. The "Garlic" model is also said to integrate a Dual-Branch Development strategy, involving "Shallotpeat" for efficiency and "Garlic Branch" for experimental compression and density techniques 1. An Auto-Router System, incorporating a "Reflex Mode" for simple queries and "Deep Reasoning" for complex problems, dynamically allocates computational resources based on task complexity 1.
For coding, GPT-5.3-Codex demonstrates State Awareness by internally simulating code execution to identify and correct runtime errors and logic flaws before generating output 4. It also features Repository-Level Understanding, moving beyond individual files to grasp entire repositories and the broader impact of code changes 4. An Implicit Chain-of-Thought Debugging process enables the model to outline logic, potential edge cases, and security vulnerabilities, and to critique its own plan, specifically targeting OWASP Top 10 vulnerabilities 4. Dynamic Documentation Synchronization ensures the model uses the latest framework versions and syntax by connecting to live documentation indexes 4. GPT-5.3-Codex is 25% faster than its predecessor due to infrastructure improvements 2, while GPT-5.3-Codex-Spark is optimized for ultra-low latency, achieving over 1,000 tokens per second using Cerebras' Wafer-Scale Engine 3. GPT-5.3-Codex boasts a 400,000-token context window 5, a feature also rumored for "Garlic" with "Perfect Recall" to prevent information loss in long contexts 1. GPT-5.3-Codex-Spark, however, has a 128,000-token context window and is text-only at launch 6. Both the rumored "Garlic" model and GPT-5.3-Codex are reported to have a 128,000-token output limit, enabling generation of complete software libraries or multi-file code 1.
Officially Stated Core Capabilities: GPT-5.3-Codex is described as OpenAI's most capable agentic coding model, optimized for long-horizon, tool-using tasks 2. It functions as a "colleague that uses a computer," capable of operating repositories, running terminal commands, iterating fixes, managing workflows, and producing diverse artifacts like presentations or spreadsheets 7. It excels in agentic software engineering benchmarks such as Terminal-Bench 2.0 and OSWorld-Verified 3. The rumored "Garlic" model includes built-in tool use for API calls and automatic unit test generation 1.
In code generation, GPT-5.3-Codex achieves state-of-the-art performance, scoring 56.8% on SWE-Bench Pro and 77.3% on Terminal-Bench 2.0 2. It also demonstrates advanced web development capabilities, including automatic production-quality website creation 8. The "Garlic" model is rumored to achieve 94.2% on HumanEval+, while GPT-5.3-Codex achieves 93% on HumanEval 1.
For advanced reasoning, GPT-5.3-Codex inherits strong capabilities from GPT-5.2 2. It shows strong performance on GDPval (70.9% wins or ties) 2 and demonstrates high scores in various reasoning benchmarks like GPQA (81%), MMLU (93%), MATH (96%), and GSM8k (99%) 5. The model supports multimodal interpretation, integrating text, data points, or structured commands 9, and lists "TextImageAudioVideo" as modalities with vision capabilities 5.
The rumored "Garlic" model focuses on reduced hallucination and self-verification, employing "epistemic humility" to recognize knowledge gaps and express explicit uncertainty 1. In cybersecurity, GPT-5.3-Codex is classified as "High" capability, incorporating OpenAI's most comprehensive safety stack 2. It is specifically trained to identify and fix software vulnerabilities and is effective at identifying zero-day exploits 10. The model also offers interactive collaboration, allowing real-time steering during multi-file tasks 2. Notably, OpenAI states that GPT-5.3-Codex was "instrumental in creating itself," with early versions used to debug training and manage deployment, acting as a site reliability engineer 2.
Gemini 3 Deep Think is a specialized reasoning mode developed by Google DeepMind 11. It is not a standalone model but an enhanced reasoning mode within the Gemini 3 series 12.
Developer and Release Status: Google DeepMind developed Gemini 3 Deep Think 11. It was initially made available to Google AI Ultra subscribers in December 2025, with a significant upgrade rolled out in February 2026 13. This experimental capability is accessible within the Gemini app for Google AI Ultra subscribers and via the Gemini API for select researchers and enterprises through an early access program 14. Due to its experimental nature, Deep Think may be discontinued or suspended without prior notice 15.
Architectural Principles: Gemini 3 Deep Think's core architectural principles prioritize advanced reasoning over speed and breadth 16. It employs Inference-Time Compute Scaling, allocating additional computational resources during inference to process complex problems thoroughly, favoring depth and accuracy 17. The model constructs internal, multi-step Extended Reasoning Chains, performing in-depth analysis, self-verification, and error correction loops to enable structured decomposition of complex problems 17. It also utilizes Parallel Hypothesis Exploration, simultaneously exploring multiple potential solution paths in a latent space, comparing intermediate results, and identifying the most promising one 17.
A critical aspect is its Self-Verification Loops, where the model explicitly checks its conclusions for logical consistency and potential errors, which can involve verifying mathematical conditions and calculating outcomes 17. Deep Think supports a "System 2 Thinking" paradigm, emphasizing deliberate, analytical, step-by-step processing rather than rapid pattern matching 16. It also has a Backtracking Capability, allowing it to identify and abandon unproductive reasoning paths to pursue more viable approaches 18. The underlying Multimodal Architecture is transformer-based, incorporating a multimodal encoder that integrates visual data, speech, and text, facilitated by a cross-modal attention network 11. Based on the Gemini 2.5 models, Deep Think utilizes a sparse Mixture-of-Experts (MoE) architecture, which dynamically routes tokens to specific "expert" parameters, decoupling total model capacity from per-token computation cost 19. Gemini Deep Think was trained using Google's Tensor Processing Units (TPUs) and developed with JAX and ML Pathways 19. Its pre-training dataset included a diverse collection of web documents, code, images, audio, and video, with a knowledge cutoff of January 2025 19.
Announced Core Capabilities: Gemini 3 Deep Think excels in tasks requiring advanced reasoning, scientific discovery, and complex problem-solving. In advanced reasoning, it sets new benchmarks, achieving 48.4% on Humanity's Last Exam (without tools) 13 and 84.6% on ARC-AGI-2 14. It scored 93.8% on GPQA Diamond for advanced question answering 12 and 81.5% on MMMU-Pro for multimodal understanding and reasoning 17.
For scientific and mathematical discovery, the model has demonstrated gold medal-level performance in simulations of the International Physics Olympiad (87.7%) and International Chemistry Olympiad (82.8%) 13. It also scored 50.5% on the CMT-Benchmark for advanced theoretical physics expertise 13 and achieved gold-medal standard at the International Mathematics Olympiad (IMO) in 2025 with 81.5% 20. Notably, Deep Think successfully identified subtle logical inconsistencies in a highly technical mathematics paper that human peer reviewers missed 13.
In engineering and code generation, it achieved an Elo rating of 3455 on Codeforces, placing it in the top competitive programming tier 14, and a gold-medal standard at the International Collegiate Programming Contest 20. Deep Think can model physical systems through code, accelerate the design of physical components, and even translate a sketch into a 3D-printable reality 21. Its "vibe coding" capabilities translate high-level architectural intent into executable code 22. Deep Think supports rich multimodal inputs, including video analysis and image-and-text reasoning, and its enhanced agentic capabilities provide improved tool use and better planning and verification when interacting with external tools and simulations 12.
GLM-5 is a large language model developed by Zhipu AI (Z.ai), a company that spun out of Tsinghua University in 2019 23.
Developer and Release Status: Zhipu AI officially released GLM-5 on February 11, 2026 24. The model was initially soft-launched as "Pony Alpha," a stealth model that processed over 40 billion tokens on its first day of community testing 24. GLM-5 is available for both commercial and non-commercial use under an MIT License 25. Its weights are publicly accessible on platforms like Hugging Face 24 and ModelScope 26, and it is also available via Z.ai's API platform and other third-party aggregators 24.
Architectural Principles: GLM-5 is a Transformer-based Mixture-of-Experts (MoE) model 27. It features approximately 744 billion total parameters, with 40 billion active parameters per token during inference 25. This represents a significant scaling from its predecessor, GLM-4.5, which had 355 billion total parameters and 32 billion active parameters 25. The architecture includes 256 total experts, with 8 activated per token 28. The number of transformer layers was reduced from 92 to 78 compared to GLM-4.7, likely to optimize inference costs and latency 29.
GLM-5 integrates DeepSeek Sparse Attention (DSA), which efficiently handles long contexts by reducing computational complexity from quadratic to linear, thereby lowering deployment costs 25. The model was pre-trained on a substantial 28.5 trillion tokens, an increase from 23 trillion tokens used for GLM-4.5 25. Zhipu AI developed a novel asynchronous reinforcement learning (RL) infrastructure called "slime" to enhance post-training efficiency and throughput, particularly for complex agentic behaviors 27. Notably, GLM-5 was trained entirely on Huawei Ascend chips using the MindSpore framework, demonstrating zero dependency on NVIDIA hardware and highlighting China's self-reliant AI infrastructure capabilities 24. The model weights are released in BF16 precision, with FP8 and other quantized variants also available for more efficient deployment 25.
Officially Stated Core Capabilities: GLM-5 is designed for complex systems engineering and long-horizon agentic tasks, aiming to transition from "vibe coding" to "agentic engineering" 24. It supports a 200,000-token context window 25 and a maximum output of 128,000 tokens 25.
In terms of reasoning and intelligence, GLM-5 achieves best-in-class performance among open-source models 27. It scores 50 on the Artificial Analysis Intelligence Index, establishing it as the new leading open-weights model 25. The model demonstrates industry-leading reliability with an AA-Omniscience Index score of -1, indicating a very low hallucination rate and a strong ability to abstain from answering when confidence is low 25. It particularly excels at long-term planning, resource management, and multi-step logical reasoning 26.
For coding and agentic engineering, GLM-5 achieves 77.8% on SWE-bench Verified and 56.2% on Terminal-Bench 2.0, making it a top-performing open-source model in these benchmarks 24. It is capable of automated software development, backend refactoring, deep debugging, and generating end-to-end test cases 30. Its "Agent Mode" allows it to autonomously decompose tasks, orchestrate tools, and execute workflows to produce finished files like .docx, .pdf, and .xlsx documents directly from prompts 26. While primarily text-only, the broader GLM family includes specialized models for image generation and multimodal vision-language understanding 24. GLM-5 also supports multi-turn conversations, thinking mode, tool calling, structured JSON output, and context caching 27.
| Model Name | Developer | Official Release Date | Architecture & Parameters | Context/Output Limits | Key Capabilities & Features |
|---|---|---|---|---|---|
| GPT-5.3 (Codex) | OpenAI | Feb 5, 2026 | GPT-5 architecture, 'Cognitive Density' focus (no params specified) | Context: 400K tokens, Output: 128K tokens | Multimodal (TextImageAudioVideo); Coding: SWE-Bench Pro (56.8%), Reasoning: MMLU (93%); Agentic, Cybersecurity, Self-improvement |
| Gemini 3 Deep Think | Google DeepMind | Dec 2025 | Reasoning mode within Gemini 3 (MoE base), System 2 Thinking (no params specified) | Context/Output: Not specified | Multimodal; Coding: Codeforces (Elo 3455), Reasoning: GPQA Diamond (93.8%); Self-verification, Scientific Discovery |
| GLM-5 | Zhipu AI (Z.ai) | Feb 11, 2026 | Transformer-MoE, DeepSeek Sparse Attention; Total: 744B, Active: 40B | Context: 200K tokens, Output: 128K tokens | Text-only; Coding: SWE-bench Verified (77.8%), Reasoning: AAII (50); Agentic Engineering, Low Hallucination |
The performance landscape for leading large language models like GPT-5.3, Gemini Deep Think, and GLM-5 reveals a highly competitive and rapidly evolving field. All three models demonstrate frontier capabilities, with benchmarks increasingly showing convergence at the top. This convergence makes the "actual experience" and specific use cases more critical than raw scores alone for model selection 31.
In the domain of complex problem-solving and reasoning, Gemini Deep Think frequently leads or matches its competitors, especially when utilizing its "Deep Think" mode. It exhibits superior performance in abstract visual reasoning benchmarks like ARC-AGI-2 and broad challenging evaluations such as Humanity's Last Exam . Gemini Deep Think also possesses strong innate mathematical intuition, even without the aid of tools 32.
GPT-5.3 (and its variants) excels in traditional competition-level mathematics (e.g., AIME 2025) and advanced scientific questions (GPQA Diamond), often achieving perfect scores with tool assistance. Its "Thinking" mode significantly boosts performance across complex tasks .
GLM-5 performs commendably in reasoning, with strong scores in AIME 2026, GPQA Diamond, and Humanity's Last Exam (particularly with tools), thus narrowing the performance gap with proprietary systems .
For coding and agentic tasks, GPT-5.3 Codex demonstrates dominance in speed-oriented operations, especially in command-line environments (Terminal-Bench 2.0: 77.3%), and is optimized for rapid, interactive coding 31. It also shows strong performance in the SWE-Bench Verified benchmark 31.
Gemini Deep Think excels at agentic coding workflows, long-horizon planning, and multi-step tool use. It exhibits superior skill in algorithmic problem-solving, as evidenced by its LiveCodeBench Pro score .
GLM-5 is highly competitive in general software engineering benchmarks, achieving 77.8% on SWE-Bench Verified . It is specifically designed for complex systems engineering and long-horizon agentic tasks, showing particular strength in document generation 26.
Gemini Deep Think holds a significant advantage with its native multimodal architecture, which seamlessly integrates text, images, video, audio, and PDFs for comprehensive reasoning . This capability makes it exceptionally effective for integrated tasks such as analyzing video lectures or complex UI screenshots, and it performs particularly well in UI understanding and multilingual tasks .
GPT-5.3 Codex supports images and screenshots for frontend development 33 and demonstrates robust long-context comprehension, as seen in BrowseComp Long Context and MRCR2 Needle benchmarks 33.
In contrast, GLM-5 is a text-only model but exhibits strong general web comprehension capabilities, performing well in BrowseComp .
GLM-5 stands out as the most cost-efficient option among the three, being open-source and significantly cheaper per token than both proprietary counterparts . It also demonstrates improved token efficiency in benchmark runs 34.
GPT-5.3-Codex-Spark offers exceptional speed, delivering over 1000 tokens per second . However, its "Thinking" mode can increase actual costs due to higher token billing for processing 35.
Gemini Deep Think is noted for being 82% cheaper per task for ARC-AGI-2 . Its pricing structure includes increases for contexts exceeding 200K tokens 36.
| Feature | GPT-5.3 Codex | Gemini Deep Think (Gemini 3 Pro) | GLM-5 |
|---|---|---|---|
| Release Date | September 15, 2025 | February 13, 2026 (Deep Think V2) | February 11, 2026 |
| Context Window | 400K tokens (Input) | 1M tokens | 200K tokens |
| Max Output Tokens | 128K tokens | 64K tokens | 128K tokens |
| Open Source | No | No | Yes (MIT License) |
| Input Cost (per 1M tokens) | ~$1.75 | $2.00 (base, increases beyond 200K) | ~$0.11 |
| Output Cost (per 1M tokens) | $10.00 | $12.00 (base, increases beyond 200K) | $3.20 |
| ARC-AGI-2 score | 54.2% (GPT-5.2 Pro) | 84.6% (Deep Think V2, SOTA) | N/A |
| Humanity's Last Exam (no tools) score | 24.8% (GPT-5 Codex) | 48.4% (Deep Think V2, SOTA) | 30.5% (Thinking mode) |
| GPQA Diamond score | 85.7% (GPT-5 Codex, no tools) | 93.8% (with Deep Think) | 86% (Thinking mode) |
| SWE-Bench Verified score | 78.2% (GPT-5.3) | 76.2% (Gemini 3 Pro) | 77.8% |
| Terminal-Bench 2.0 score | 77.3% (GPT-5.3 Codex) | 54.2% (Gemini 3 Pro) | 56.2% / 60.7% |
| Speed metric | >1000 tokens/s (GPT-5.3-Codex-Spark) | Faster than average (Artificial Analysis) | Faster than average (Artificial Analysis) |
| Multimodality support | Supports images/screenshots | Native multimodal (text, images, video, audio, PDFs) | Text-only |
| Key Strength | Ultra-low-latency coding, reliability, agentic software engineering | Advanced reasoning, native multimodal understanding, long-horizon agentic capabilities | Cost-efficiency, open-source, hallucination reduction, complex systems engineering |
This section identifies the unique features, innovative approaches, and philosophical differences that set GPT-5.3, Gemini Deep Think, and GLM-5 apart. For each model, its primary target applications and specific use cases where it is designed to excel are discussed, alongside the strategic direction or vision of their respective developers.
OpenAI's GPT-5.3, particularly its rumored general-purpose model "Garlic," marks a significant shift in AI development by focusing on "cognitive density" rather than merely increasing parameter count. This philosophy aims for a "smarter and denser" architecture, reportedly achieving "GPT-6 level" reasoning while being faster and more cost-effective than its predecessor, GPT-5.2 . GPT-5.3-Codex, a primary variant, is optimized as OpenAI's most capable agentic coding model, specifically designed for long-horizon, tool-using tasks . It is envisioned as a "colleague that uses a computer," capable of operating complex repositories and managing workflows .
The "Garlic" model's architectural innovations include an Enhanced Pre-Training Efficiency (EPTE) that achieves approximately 6x more knowledge density per byte through intelligent pruning of neural pathways, active knowledge compression, and curated training data 1. It features an Auto-Router System with "Reflex Mode" for simple queries and "Deep Reasoning" for complex problems, dynamically allocating computational resources 1. GPT-5.3-Codex integrates advanced capabilities such as State Awareness, simulating code execution internally to identify errors 4; Repository-Level Understanding, which comprehends entire codebases 4; and Implicit Chain-of-Thought Debugging, critically reviewing its own code generation plan for logic and vulnerabilities, including OWASP Top 10 issues 4. The model also employs Dynamic Documentation Synchronization, ensuring up-to-date framework and syntax usage 4. Both "Garlic" and GPT-5.3-Codex boast a substantial 400,000-token context window, with "Garlic" promising "Perfect Recall" to prevent information loss , and an impressive 128,000-token output limit . Furthermore, GPT-5.3-Codex is classified as "High" in cybersecurity capability, being specifically trained to identify and fix software vulnerabilities and perform production-grade security audits .
GPT-5.3-Codex is tailored for advanced agentic software engineering, excelling in benchmarks like Terminal-Bench 2.0 and OSWorld-Verified . Its capabilities extend to managing diverse software development tasks, such as running terminal commands, iterating fixes, managing workflows, and producing a variety of artifacts beyond just code, including presentations and spreadsheets . The model also demonstrates advanced web development prowess, capable of automatically creating production-quality websites and building complex games and applications . OpenAI's strategic vision involves deploying models that can autonomously contribute to their own development, as evidenced by GPT-5.3-Codex being "instrumental in creating itself" through debugging training, managing deployment, and diagnosing test results .
Gemini 3 Deep Think, developed by Google DeepMind, is a specialized reasoning mode rather than a standalone model, operating within the broader Gemini 3 series . Its core philosophy centers on "System 2" thinking, emphasizing deliberate, analytical, step-by-step processing over rapid, intuitive pattern matching 16. This approach prioritizes depth and accuracy, dedicating more computational resources to complex problem-solving 12. A standout feature is its native multimodal architecture, a transformer-based network that integrates visual data, speech, and text through a cross-modal attention network . This allows for coherent cross-modal reasoning, seamlessly processing text, images, video, audio, and PDFs within a unified framework .
Architecturally, Deep Think employs Inference-Time Compute Scaling, allocating additional computational resources for complex tasks 17. It constructs Extended Reasoning Chains, performing in-depth analysis, self-verification, and error correction loops, and uses Parallel Hypothesis Exploration to concurrently explore multiple solution paths . A critical component is its explicit Self-Verification Loops, where the model checks its conclusions for logical consistency . The model also possesses a Backtracking Capability, allowing it to abandon unproductive reasoning paths 18. Based on Gemini 2.5's Mixture-of-Experts (MoE) architecture , it was trained on Google's Tensor Processing Units (TPUs) using JAX and ML Pathways 19. DeepMind's strategic focus for Deep Think is to excel in advanced reasoning, scientific discovery, and complex problem-solving across various domains .
Deep Think is tailored for tasks requiring advanced intellectual capabilities. In scientific and mathematical discovery, it has demonstrated gold medal-level performance in simulations of the International Physics, Chemistry, and Mathematics Olympiads, and has even identified subtle logical inconsistencies in technical mathematics papers that human peer reviewers missed . Its engineering and code generation capabilities are formidable, achieving an Elo rating of 3455 on Codeforces, placing it in the top competitive programming tier . It can model physical systems through code, accelerate the design of physical components, and translate a sketch into a 3D-printable reality . Deep Think's enhanced agentic capabilities provide improved tool use and better planning and verification when interacting with external tools and simulations 12. Notably, it has solved 18 previously unsolved research problems and disproved a decade-old mathematical conjecture 37.
GLM-5, developed by Zhipu AI, distinguishes itself as a leading open-source model released under an MIT License, with its weights publicly accessible . This commitment to open-source offers significant cost-efficiency, being approximately 2.7x cheaper than GPT-5 Codex 33 and 45 times cheaper than Claude Opus 4.6 31. A core feature of GLM-5 is its industry-leading reliability and low hallucination rate, scoring -1 on the AA-Omniscience Index, which implies it effectively "knows when to say I don't know" . The model is fundamentally designed for complex systems engineering and long-horizon agentic tasks, aiming to bridge the gap between "vibe coding" and autonomous "agentic engineering" .
GLM-5 is a Transformer-based Mixture-of-Experts (MoE) model, featuring around 744 billion total parameters with 40 billion active parameters per token, utilizing 256 experts with 8 activated per token . A key architectural innovation is the integration of DeepSeek Sparse Attention (DSA), which reduces computational complexity from quadratic to linear, making it efficient for handling long contexts and lowering deployment costs . The model was pre-trained on a massive 28.5 trillion tokens . Zhipu AI developed a novel asynchronous reinforcement learning (RL) infrastructure called "slime" to enhance post-training efficiency, especially for complex agentic behaviors . Demonstrating a strategic move towards technical independence, GLM-5 was trained entirely on Huawei Ascend chips using the MindSpore framework, showing zero dependency on NVIDIA hardware . It supports a 200,000-token context window and a maximum output of 128,000 tokens . Zhipu AI's vision appears to be fostering a self-reliant AI ecosystem capable of developing advanced autonomous systems.
GLM-5 is highly proficient in automated software development, including backend refactoring, deep debugging, and generating end-to-end test cases . Its "Agent Mode" capability is particularly significant, allowing it to autonomously decompose tasks, orchestrate tools, and execute workflows to produce finished files such as .docx, .pdf, and .xlsx documents directly from prompts . This aligns with its strengths in long-term planning, resource management, and multi-step logical reasoning . The model also excels at structured data extraction and cross-lingual report synthesis, supporting diverse professional applications 30.
In summary, GPT-5.3 distinguishes itself with a focus on "cognitive density" and unparalleled capabilities in agentic coding, particularly for interactive and complex software engineering tasks, backed by robust cybersecurity features and a vision for self-improving AI. Gemini Deep Think stands out for its "System 2" reasoning approach, native multimodal integration, and profound strengths in scientific discovery and abstract problem-solving, aiming to push the boundaries of intellectual cognition. GLM-5 carves its niche through an open-source model offering cost-efficiency, an exceptionally low hallucination rate, and strong agentic engineering capabilities, while also demonstrating strategic independence in hardware infrastructure.
The current artificial intelligence landscape is marked by intense competition and rapid innovation, with leading large language models demonstrating increasingly convergent frontier capabilities. This report provides a comprehensive comparative analysis of OpenAI's GPT-5.3, Google DeepMind's Gemini Deep Think, and Zhipu AI's GLM-5, detailing their individual strengths, weaknesses, and potential future trajectories. The analysis synthesizes architectural foundations, performance benchmarks, and distinguishing features to offer a holistic perspective on their competitive positioning and broader impact on the AI ecosystem. Benchmarks increasingly show convergence at the top, making specific use cases and the "actual experience" more critical than generalized performance scores alone 31.
GPT-5.3, particularly its Codex variants, showcases formidable strengths in software engineering and efficiency. The GPT-5.3-Codex-Spark offers ultra-low-latency coding, capable of over 1,000 tokens per second, making it 10x faster than previous versions and optimized for ultra-low latency inference . GPT-5.3-Codex excels in agentic software engineering tasks such as refactoring, debugging, code review, and handling full projects, achieving 77.3% on Terminal-Bench 2.0 . It is also characterized by high reliability, with significantly reduced hallucination rates (under 1% on open-source prompts) and lower error rates, which is crucial for critical applications like health-related queries 38. The model demonstrates strong performance in complex problem-solving and mathematical benchmarks, such as a perfect 100% on AIME 2025 with Python tools and 89.4% on GPQA Diamond . Notably, GPT-5.3-Codex is the first model classified as "High" capability for cybersecurity under OpenAI's Preparedness Framework, trained to identify and fix software vulnerabilities and effective at identifying zero-day exploits . Furthermore, its self-improvement capabilities are remarkable, with early versions of GPT-5.3-Codex being instrumental in its own creation and deployment management .
However, GPT-5.3 has several inherent weaknesses and limitations. Its proprietary nature means it is not open-source, limiting transparency and customizability 33. The computational costs associated with its advanced "Thinking" modes can be significant due to billed "thinking tokens" 35. Concerns have been raised regarding a potential "Black Box Codebase," where developers might accept solutions without full understanding, leading to technical debt 4. Despite internal security auditing improvements, there's a risk of "poisoning attacks" that could introduce backdoors if user trust leads to complacency in code review 4. Moreover, the automation of tasks traditionally performed by junior developers raises questions about the training pipeline for future senior engineers 4.
The future trajectory for GPT-5.3 likely involves continued advancements in coding and agentic AI, with anticipated enhancements in safety features. Rumors surrounding the "Garlic" codename suggest a philosophical shift towards "cognitive density" over raw parameter count, aiming for smarter and denser architecture that achieves "GPT-6 level" reasoning while being faster and more cost-effective . This includes concepts like Enhanced Pre-Training Efficiency (EPTE) and "Perfect Recall" within its 400,000-token context window . These developments hint at models that are not only more capable but also more efficient and context-aware, while striving for reduced hallucination and self-verification through "epistemic humility" 1.
Gemini Deep Think, a specialized reasoning mode within the Gemini 3 series, distinguishes itself through unparalleled strengths in advanced abstract reasoning, scientific discovery, and native multimodal processing. It sets new benchmarks in challenging reasoning tasks, achieving an impressive 84.6% on ARC-AGI-2 and 48.4% on Humanity's Last Exam (without tools), both new state-of-the-art scores . Its scientific prowess is evident in gold medal-level performance in simulations of the International Physics and Chemistry Olympiads and its ability to identify subtle logical inconsistencies in technical papers that human peer reviewers missed . The model's "Deep Think" mode employs an inference-time compute scaling approach, constructing extended reasoning chains, performing parallel hypothesis exploration, and utilizing self-verification loops to achieve its deep analytical capabilities, explicitly supporting "System 2 Thinking" . Its native multimodal architecture is a significant advantage, allowing it to seamlessly process and reason across text, images, video, audio, and PDFs, making it highly effective for integrated tasks such as analyzing video lectures or complex UI screenshots . Deep Think also excels in long-horizon agentic planning and tool use, demonstrated by strong performance in Vending-Bench 2 and an Elo rating of 3455 on Codeforces . It offers a substantial 1-million-token context window, enabling the processing of massive documents like entire textbooks or legal briefs in one go .
Despite its advanced capabilities, Gemini Deep Think faces limitations. It is an experimental capability, meaning it may be discontinued or suspended without notice 15. Its accessibility is currently restricted to Google AI Ultra subscribers and select researchers via an early access API program . Like GPT-5.3, it is a proprietary model. While noted for being 82% cheaper per task for ARC-AGI-2 , the cost can increase for contexts beyond 200,000 tokens 36. Its maximum output tokens (64,000) are also lower than those of GPT-5.3 and GLM-5 35.
The future trajectory for Gemini Deep Think positions it as a key driver in pushing the boundaries of scientific research, complex problem-solving, and integrated multimodal AI. Its unique "Deep Think" mode will likely continue to evolve, offering even more sophisticated reasoning and hypothesis exploration capabilities, further solidifying its role in tackling previously unsolvable problems 37.
GLM-5, developed by Zhipu AI, presents a compelling alternative with its distinct strengths in cost-efficiency, open-source accessibility, and robust agentic engineering capabilities. Released under an MIT License, GLM-5 is an open-weights model, providing full access to its weights, enabling fine-tuning, and offering greater data sovereignty and customizability . It stands out for its record-low hallucination rate, scoring -1 on the AA-Omniscience Index, indicating a strong ability to abstain from answering when confidence is low and improved reliability . The model is engineered for complex systems engineering and long-horizon agentic tasks, capable of autonomously decomposing tasks, orchestrating tools, and producing finished professional documents like .docx, .pdf, and .xlsx directly from prompts . GLM-5 is highly competitive in coding benchmarks, achieving 77.8% on SWE-Bench Verified and 56.2% on Terminal-Bench 2.0 . Furthermore, its training entirely on Huawei Ascend chips using the MindSpore framework highlights China's self-reliant AI infrastructure and demonstrates hardware independence from NVIDIA . With a 200,000-token context window and a maximum output of 128,000 tokens, it supports extensive context processing and detailed output generation . Its cost-effectiveness is a major advantage, being roughly 2.7 times cheaper than GPT-5 Codex and 45 times cheaper than Claude Opus 4.6 .
The primary limitation of GLM-5 is its nature as a text-only model . While its broader GLM family includes specialized multimodal models, GLM-5 itself does not natively support multimodal inputs like images, video, or audio, which is a significant differentiator compared to Gemini Deep Think and, to a lesser extent, GPT-5.3 . Although its reasoning capabilities are strong and competitive, they sometimes lag behind the absolute state-of-the-art proprietary models in certain complex benchmarks, such as Humanity's Last Exam (30.5% in Thinking mode versus Deep Think's 48.4%) .
GLM-5's future trajectory is poised to significantly impact the landscape of accessible and customizable AI solutions, particularly through its open-source advantage. Its focus on agentic engineering and continuous improvement in reliability and efficiency will make it a powerful tool for developers and enterprises seeking flexible, cost-effective, and transparent AI deployments. The existence of other multimodal models within the GLM family suggests a potential future for integrated multimodal capabilities within its core offerings, or via its open-source nature, community contributions.
The competitive landscape demonstrates a nuanced interplay of strengths among these leading models. While GPT-5.3 excels in speed-optimized coding and robust reliability, Gemini Deep Think leads in deep, abstract reasoning and native multimodal comprehension. GLM-5 carves out its niche through open-source cost-efficiency, superior hallucination reduction, and strong agentic engineering for complex systems.
| Benchmark/Feature | GPT-5.3 Codex | Gemini Deep Think (Gemini 3 Pro) | GLM-5 |
|---|---|---|---|
| Release Date | September 15, 2025 33 | February 13, 2026 (Deep Think V2) | February 11, 2026 39 |
| Context Window | 400K tokens (Input) 33 | 1M tokens | 200K tokens 39 |
| Max Output Tokens | 128K tokens 33 | 64K tokens 35 | 128K tokens 39 |
| Open Source | No 33 | No | Yes (MIT License) 39 |
| Input Cost (per 1M tokens) | $1.25 33 | $2.00 (base, increases beyond 200K) 36 | $1.00 33 |
| Output Cost (per 1M tokens) | $10.00 33 | $12.00 (base, increases beyond 200K) 36 | $3.20 33 |
| ARC-AGI-2 | 54.2% (GPT-5.2 Pro) 35 | 84.6% (Deep Think V2, SOTA) 40 | N/A |
| Humanity's Last Exam (no tools) | 24.8% (GPT-5 Codex) 33 | 48.4% (Deep Think V2, SOTA) 40 | 30.5% (Thinking mode) 39 |
| GPQA Diamond | 85.7% (GPT-5 Codex, no tools) 33 | 93.8% (with Deep Think) 32 | 86% (Thinking mode) 39 |
| SWE-Bench Verified | 78.2% (GPT-5.3) 31 | 76.2% (Gemini 3 Pro) 41 | 77.8% 39 |
| Terminal-Bench 2.0 | 77.3% (GPT-5.3 Codex) 31 | 54.2% (Gemini 3 Pro) 35 | 56.2% / 60.7% 39 |
| Speed | >1000 tokens/s (GPT-5.3-Codex-Spark) 40 | Faster than average (Artificial Analysis) 42 | Faster than average (Artificial Analysis) 42 |
| Multimodality | Supports images/screenshots 33 | Native multimodal (text, images, video, audio, PDFs) 32 | Text-only 42 |
| Key Strength | Ultra-low-latency coding, reliability, agentic software engineering | Advanced reasoning, native multimodal understanding, long-horizon agentic capabilities | Cost-efficiency, open-source, hallucination reduction, complex systems engineering |
In specific benchmark comparisons:
The emergence of GPT-5.3, Gemini Deep Think, and GLM-5 signifies a transformative period in AI development, highlighting both opportunities and challenges. The overall impact on the AI development landscape is one of accelerated progress, where the pursuit of higher intelligence, efficiency, and broader application is relentless 31. The tightening competition at the top end of model capabilities suggests that future differentiation will increasingly rely on specialized strengths, domain expertise, and effective deployment strategies rather than generalized performance metrics alone 31.
The dynamic between proprietary and open-source AI ecosystems is a critical aspect. OpenAI and Google continue to push the boundaries with cutting-edge proprietary models, offering advanced features and robust support but retaining control over their development and deployment. This closed approach can lead to concerns about "Black Box Codebase" for GPT-5.3 4. In contrast, GLM-5, as a leading open-source model, champions accessibility, transparency, and customizability, empowering a broader community of developers and fostering innovation through shared resources . This dichotomy shapes the future of AI accessibility, democratizing powerful tools for diverse applications.
Ethical considerations are paramount as these models become more capable. The potential for bias, stemming from training data or inherent algorithmic structures, remains a constant concern across all models, necessitating ongoing research and mitigation strategies. Transparency, particularly in the proprietary models, is a challenge, making it difficult to fully understand their internal workings and decision-making processes. The implications for workforce displacement are significant, especially with models like GPT-5.3-Codex raising concerns about the future training pipeline for junior developers by automating routine coding tasks 4. This suggests a need for re-skilling initiatives and evolving educational frameworks. Furthermore, while models like GLM-5 demonstrate a remarkable ability to reduce hallucination and GPT-5.2 includes self-verification mechanisms 35, the risk of factual confabulation or "poisoning attacks" (as seen with GPT-5.3-Codex) persists and demands rigorous safety protocols and user vigilance .
The evolving nature of AI intelligence is marked by several key trends. The emphasis on "cognitive density" over mere parameter count, as rumored for GPT-5.3's "Garlic" codename, suggests a shift towards more efficient and "smarter" architectures . The native multimodal capabilities of Gemini Deep Think highlight a future where AI can seamlessly interpret and reason across diverse data types, mirroring human perception . All three models underscore the accelerating trend towards sophisticated agentic capabilities, where AI systems can autonomously plan, execute, and adapt to complex tasks across long horizons, moving beyond simple prompt-response interactions . These developments collectively point towards an AI future that is increasingly integrated, intelligent, and influential across all facets of human endeavor.