Grok (xAI)

Grok is a family of large language models, multimodal models, reasoning models, coding models, and agent-oriented systems developed by xAI. The name is also used for xAI's consumer chatbot and assistant integrated with the social network X, formerly Twitter. xAI was founded by Elon Musk in 2023, acquired X in March 2025, and was itself acquired by SpaceX in February 2026.

The first publicly released model, Grok-1, used a sparsely activated Mixture-of-Experts (MoE) Transformer with 314 billion total parameters, eight experts, and two experts selected for each token. Its base-model weights and architecture were released under the Apache License 2.0 in March 2024. Grok-2 weights were released later under a more restrictive custom community licence. Parameter counts and complete architectural specifications for Grok-3, Grok-4, Grok-4.5, and most other production models have not been officially disclosed.

As of 10 July 2026, Grok 4.5, released on 8 July 2026, is xAI's latest publicly announced general-purpose language, coding, and agent model. The hosted API accepts text and image inputs, generates text, supports function calling and structured output, and provides a 500,000-token context window with configurable reasoning effort. It is distinct from xAI's separate Imagine image-and-video-generation services and its Voice, text-to-speech, and speech-to-text systems.

Grok can use tools for web search, X search, code execution, and external function calls. The underlying model does not automatically know events that occurred after its training data was collected. Current information is obtained through enabled search tools; this is different from continuously retraining the model on a live X feed.

The original product was marketed as having wit and a “rebellious streak”, inspired in part by The Hitchhiker's Guide to the Galaxy. This was a product-positioning and response-style choice rather than a disclosed technical architecture or a guarantee that the system would answer every request.

History and corporate background

Foundation of xAI

Elon Musk announced the formation of xAI in July 2023. The company initially described its objective as understanding the nature of the universe and developing artificial-intelligence systems with strong mathematical, scientific, and reasoning capabilities.

The first Grok announcement was published on 3 November 2023. xAI stated that the model had been developed after approximately two months of training and described it as a very early beta product. Access was initially limited principally to subscribers of X's Premium+ tier.

The announcement also retrospectively described an internal 33-billion-parameter prototype named Grok-0. Grok-0 was not released as an independent public checkpoint and is not normally treated as a production member of the release sequence.

xAI initially developed Grok using a custom infrastructure stack based on JAX, Rust, and Kubernetes. Later models were trained using Colossus, xAI's large accelerator cluster in Memphis, Tennessee.

Relationship with X and SpaceX

In March 2025, xAI acquired X in an all-stock transaction. This placed the social network, Grok product, model-development organization, and associated data and infrastructure within one corporate structure.

SpaceX acquired xAI in February 2026. Grok continued to be developed and documented under the xAI name, while xAI's public-facing services increasingly used SpaceX-associated corporate branding. The acquisition should not be interpreted as evidence that Grok and SpaceX's aerospace systems use one shared technical architecture.

Release chronology

Principal Grok language, reasoning, vision-understanding, and coding releases

Model or system	First public release	Officially disclosed parameters	Published context window or prompt limit	Main characteristics	Availability and status
Grok-1	3 November 2023 — consumer early beta 17 March 2024 — open release	314B total; eight MoE experts, two selected per token	8,192 tokens	First public Grok model. Text-only base architecture with sparse MoE feed-forward layers.	The consumer system was proprietary. Raw base-model weights and architecture were later released under Apache 2.0; the open checkpoint was not the complete dialogue-aligned production chatbot.
Grok-1.5	28 March 2024	Not disclosed	128,000 tokens	Improved mathematical, coding, and long-context performance.	Proprietary staged rollout to existing Grok users and early testers.
Grok-1.5V	12 April 2024 — preview	Not disclosed	Not disclosed	First Grok model publicly demonstrated with image, diagram, chart, screenshot, photograph, and document-page understanding.	Proprietary preview. xAI did not announce an independent generally available API checkpoint at that time.
Grok-2	13 August 2024 — consumer beta 14 December 2024 — grok-2-1212 API checkpoint	Not disclosed	API checkpoint: 131,072-token maximum prompt	Improved dialogue, coding, reasoning, tool use, and visual understanding.	Proprietary at launch. A post-trained 2024 Grok-2 checkpoint was later released as open weights under the custom xAI Community License.
Grok-2 mini	13 August 2024	Not disclosed	Not disclosed	Smaller member of the Grok-2 family intended to trade some capability for lower latency.	Proprietary consumer model.
Grok-2 Vision	14 December 2024	Not disclosed	API checkpoint: 32,768-token maximum prompt	Image input and text output; improved instruction following and multilingual performance over the initial API beta.	Proprietary legacy API checkpoint.
Grok 3	17 February 2025 — launch presentation 19 February 2025 — written beta announcement 3 April 2025 — API	Not disclosed	Consumer product: up to 1,000,000 tokens Initial API: 131,072-token maximum prompt	General model with improved mathematics, coding, world knowledge, visual understanding, and tool use.	Proprietary. The original grok-3 API identifier was retired on 15 May 2026 and redirected to Grok 4.3.
Grok 3 mini	17–19 February 2025 3 April 2025 — API	Not disclosed	Initial API: 131,072-token maximum prompt	Lower-cost reasoning model with adjustable reasoning effort.	Proprietary. “Fast” API names were serving aliases rather than independently disclosed model architectures.
Grok 4	9 July 2025	Not disclosed	API: 256,000 tokens	Reinforcement-learning-oriented reasoning model with native tool use, code execution, and web and X search.	Proprietary. The original grok-4-0709 identifier was retired on 15 May 2026 and redirected to Grok 4.3.
Grok 4 Heavy	9 July 2025	Not separately disclosed	Not separately disclosed	Test-time-compute configuration using several parallel reasoning trajectories or hypotheses.	Proprietary subscription feature at launch. It was not documented as a separate generally available API foundation model.
Grok Code Fast 1	28 August 2025	Not disclosed	256,000 tokens	Purpose-built agentic coding model trained with code-heavy data and terminal, file-editing, and repository-search tools.	Proprietary API model. Retired on 15 May 2026 and redirected to Grok Build 0.1.
Grok 4 Fast	19 September 2025	Not disclosed	2,000,000 tokens	Lower-cost model supporting reasoning and non-reasoning configurations, together with web and X search.	Proprietary. Original API identifiers were retired on 15 May 2026 and redirected to Grok 4.3.
Grok 4.1	17 November 2025	Not disclosed	Not disclosed	Improvements in creative writing, emotional sensitivity, collaboration, style, and interpretation of user intent.	Proprietary consumer release following an evaluation rollout between 1 and 14 November 2025.
Grok 4.1 Fast	19 November 2025	Not disclosed	2,000,000 tokens	Enterprise-oriented model for long-horizon tool calling and multi-turn agent workflows.	Proprietary. Original API identifiers were retired on 15 May 2026 and redirected to Grok 4.3.
Grok 4.20	10 March 2026	Not disclosed	1,000,000 tokens	Text-and-image input, text output, structured output, tool calling, and reasoning and non-reasoning configurations.	Proprietary API family.
Grok 4.20 Multi-Agent	10 March 2026	Not separately disclosed	1,000,000 tokens	Orchestrates several collaborating agents in parallel for research and multi-step work.	Proprietary API beta. It is an orchestration configuration rather than a separately disclosed foundation-model architecture.
Grok 4.3	17 April 2026 — consumer beta; subsequently documented for the API	Not disclosed	1,000,000 tokens	Text-and-image model with improved instruction following and agentic tool use. Supports reasoning levels none, low, medium, and high.	Proprietary. Became the redirect target for several retired Grok 3, Grok 4, Grok 4 Fast, and Grok 4.1 Fast API identifiers.
Grok Build 0.1	May 2026	Not disclosed	256,000 tokens	Coding model for agentic software engineering, repository work, function calling, and structured output; supports text and image input.	Proprietary API model and replacement for Grok Code Fast 1.
Grok 4.5	8 July 2026	Not disclosed	500,000 tokens	Flagship language, coding, engineering, and agent model. Supports text and image input, text output, function calling, structured output, and configurable reasoning.	Proprietary. Released through the xAI API, Grok Build, and Cursor, and made the default model in Grok Build.

Naming and status notes

• Grok-0 was an internal 33-billion-parameter prototype, not a public release.
• The initial API beta identifiers grok-beta and grok-vision-beta were replaced by the December 2024 Grok-2 checkpoints.
• Elon Musk referred to a later open-weight Grok-2 checkpoint as “Grok 2.5”, while xAI's official repository labels it Grok 2. The name should not be treated as proof of a separately documented architecture.
• A model provisionally discussed as Grok 3.5 was subsequently renamed Grok 4 before public release.
• “Think”, “DeepSearch”, “Auto”, reasoning-effort levels, SuperGrok subscription tiers, and “fast” aliases are modes, agents, products, or serving configurations. They are not automatically distinct foundation-model architectures.
• No separate official production release named Grok 4.4 was identified in xAI's public chronology through 10 July 2026.
• Retirement of an API identifier refers to its availability and routing in the developer platform. It does not necessarily mean that the same name immediately disappeared from every consumer interface.

Technical foundations

Autoregressive language modelling

Grok language models generate text autoregressively. For a token sequence $x_1,\dots ,x_T$, the probability is factorized as:

$p(x_1,\dots ,x_T)=\prod _{t=1}^T{p}_{\theta }(x_t\mid x_1,\dots ,x_{t-1}),$

where $\theta$ denotes the model parameters.

A conventional Transformer attention operation can be written as:

$\mathrm{Attention}(\mathbf{𝐐},\mathbf{𝐊},\mathbf{𝐕})=\mathrm{softmax}\left(\frac{\mathbf{𝐐}{\mathbf{𝐊}}^{\mathsf{T}}}{\sqrt{d_k}}\right)\mathbf{𝐕},$

where $\mathbf{𝐐}$, $\mathbf{𝐊}$, and $\mathbf{𝐕}$ are query, key, and value matrices.

Grok-1 architecture

Grok-1 is the only early flagship for which xAI published a complete base-model repository and an official total parameter count. It is a decoder-only Transformer with sparse MoE feed-forward layers.

For token representation $\mathbf{𝐡}$, a simplified MoE transformation is:

$\mathrm{MoE}(\mathbf{𝐡})=\sum _{i\in {𝒮}(\mathbf{𝐡})}{g}_i(\mathbf{𝐡})E_i(\mathbf{𝐡}),$

where:

• $E_i$ is an expert feed-forward network;
• ${𝒮}(\mathbf{𝐡})$ is the routed subset of experts;
• $g_i(\mathbf{𝐡})$ is the routing weight assigned to expert $i$.

Grok-1 has eight experts and selects two experts for each token. This means that two of eight expert feed-forward branches are used per token; it does not mean that exactly one quarter of all model computation or all model parameters are active, because attention, embeddings, normalization, routing, and other shared components are also evaluated.

Parameter	Grok-1
Total parameters	314 billion
Architecture	Decoder-only Transformer with sparse MoE feed-forward layers
Transformer layers	64
Hidden-state dimension	6,144
Query heads	48
Key–value heads	8
Routed experts	8
Experts selected per token	2
Vocabulary size	131,072 tokens
Maximum sequence length	8,192 tokens
Positional representation	Rotary position embeddings

The March 2024 release contained raw base-model weights. It did not contain the exact production dialogue checkpoint, system prompts, safety configuration, search tools, or complete consumer-product stack.

Grok-2 open configuration

xAI later released a Grok-2 checkpoint that had been trained and used internally during 2024. The repository contains approximately 500 GB of model files and documents a deployment configuration using tensor parallelism across eight accelerators with more than 40 GB of memory each.

The released configuration specifies:

Parameter	Released Grok-2 checkpoint
Official total parameter count	Not disclosed
Transformer layers	64
Hidden-state dimension	8,192
Query heads	64
Key–value heads	8
Routed experts	8
Experts selected per token	2
Maximum position setting	131,072 tokens

File size cannot be converted reliably into an exact parameter count without knowing the precision and storage format of every tensor. Unofficial estimates of Grok-2's size should therefore not be presented as official specifications.

Architecture of later models

xAI has not released complete layer-by-layer configurations or official parameter counts for Grok-3, Grok-4, Grok 4.3, Grok 4.5, or the main production reasoning models. Claims that Grok-3 contains 2.7 trillion parameters, or that a later model necessarily retains Grok-1's exact MoE topology, are speculative.

The public materials for later releases focus on:

• training-compute scale;
• reinforcement-learning methods;
• context limits;
• reasoning modes;
• tool use;
• multimodal inputs;
• benchmark results;
• serving characteristics.

The absence of a parameter count does not imply either a dense or an MoE architecture. It indicates only that xAI has not publicly specified the value.

Reasoning and test-time computation

Grok 3 introduced consumer modes marketed as Think and DeepSearch. Think allocated additional test-time computation to difficult mathematical, scientific, and coding questions. DeepSearch combined a model with web-search, source-selection, and synthesis tools; it was an agentic product configuration rather than a separately disclosed base-model architecture.

Grok 4 Heavy used several parallel reasoning trajectories or hypotheses before synthesizing an answer. This is a test-time-compute strategy: it increases the number of model inferences used for one request rather than necessarily changing the underlying checkpoint.

Later API releases expose configurable reasoning effort. A higher reasoning setting can increase latency, output length, and cost and does not guarantee that the answer will be correct.

Any reasoning text or summary returned by an API is generated model output. It should not be treated as a complete, independently verifiable record of all internal numerical computation.

Tool use and current information

Depending on the endpoint and product configuration, Grok can use:

• Web Search;
• X Search;
• code execution;
• user-defined functions;
• structured JSON output;
• Model Context Protocol or equivalent external tools;
• multi-agent orchestration.

Search tools provide current information at inference time. Without enabled search, the model answers from the information represented in its trained parameters and supplied context. It does not automatically receive every new X post or web page in real time.

A tool-using answer reflects the combined performance of the model, search system, ranking method, browser or API tool, prompt, context-management policy, and source-selection process.

Context windows

The maximum published context has changed across releases:

Model or interface	Published context or prompt limit
Grok-1	8,192 tokens
Grok-1.5	128,000 tokens
Grok-2 API	131,072 tokens
Grok 3 consumer product	Up to 1,000,000 tokens
Initial Grok 3 API	131,072 tokens
Grok 4 API	256,000 tokens
Grok 4 Fast and Grok 4.1 Fast	2,000,000 tokens
Grok 4.20 and Grok 4.3	1,000,000 tokens
Grok Build 0.1	256,000 tokens
Grok 4.5	500,000 tokens

A newer version does not necessarily have a larger context window than every predecessor. Context limits are deployment choices involving model quality, latency, memory, and cost.

Consumer context claims and API prompt limits describe different products and should not be treated as interchangeable. A nominal context limit also does not guarantee perfect recall or reasoning over every token.

Multimodal understanding

Grok-1.5V was the first publicly demonstrated Grok model with visual inputs. Later Grok language models can process combinations of text and images, including screenshots, diagrams, charts, and rendered document pages.

As of July 2026, the public Grok 4.5 API is documented as:

• text and image input;
• text output.

Although the Grok 4.5 training corpus included video and audio data, the public language-model API does not document native audio or video input for that endpoint.

PDF analysis is generally implemented by the surrounding product through text extraction, page rendering, or both. A PDF container is not itself a separate neural modality.

Image, video, and speech systems

Image generation in the August 2024 Grok product initially used a separate integration with Black Forest Labs' FLUX.1 system. It was not an intrinsic output head of the Grok-2 language model.

In December 2024, xAI introduced Aurora, its own autoregressive image-generation model.

The later Grok Imagine family provides image generation, image editing, video generation, and related media functions through separate endpoints.

Voice Agent, text-to-speech, and speech-to-text systems are also separately documented API families. Grok Voice Think Fast 1.0, released in April 2026, is a real-time voice-agent model rather than an output mode of the Grok 4.5 text endpoint.

Training data and infrastructure

Early infrastructure

The original Grok development stack used JAX for large-scale numerical computation, Rust for performance-sensitive infrastructure, and Kubernetes for distributed orchestration.

xAI subsequently built the Colossus supercomputer cluster in Memphis. The Grok 3 announcement stated that the model used approximately ten times more training computation than the preceding generation.

For Grok 4, xAI described a reinforcement-learning programme operating on a Colossus installation containing approximately 200,000 accelerators. Such a hardware count describes the cluster available to the project; it does not reveal the exact number of accelerator-hours used for every individual training stage.

Grok 4.5 training content

xAI's July 2026 public training-content summary states that Grok 4.5 training incorporated more than:

• ten trillion text tokens;
• one billion images;
• one million hours of audio;
• one million hours of video.

The described data categories include:

• publicly accessible web material;
• scientific and technical text;
• legal and official documents;
• social-media posts;
• source code;
• licensed, third-party, and private datasets;
• data from users and contractors, subject to applicable settings and controls;
• internally generated and synthetic data.

The xAI Web Crawler operated between January 2024 and June 2026 for the training programme described in the summary. Data collection was followed by filtering, deduplication, quality scoring, domain balancing, safety processing, and further model development.

The presence of audio and video in the training mixture does not mean that the public Grok 4.5 API accepts raw audio or video. Training modalities, learned internal representations, and exposed interface modalities are different concepts.

Reinforcement learning and agent training

xAI states that Grok 4.5 was post-trained through reinforcement learning across hundreds of thousands of tasks, including software engineering, terminal use, information gathering, and other agent-oriented workloads.

The programme included asynchronous multi-hour agent rollouts. In these settings, a model interacts with tools or an execution environment over many steps, receives task-specific or verifier-based feedback, and is updated from the resulting trajectories.

Asynchronous rollouts can improve hardware utilization because workers do not always need to wait for the longest trajectory. They also create engineering challenges involving stale policies, variable-duration episodes, environment reproducibility, and attribution of rewards to individual actions.

User and X data

xAI states that interactions with Grok and content from X can be used for product improvement or model training subject to privacy settings, controls, applicable requests, and opt-out mechanisms.

Several distinctions are important:

• using historical X content in training is not the same as continuously updating model weights;
• retrieving an X post through X Search is an inference-time tool action;
• a public post may contain personal data even when it is accessible without authentication;
• settings and legal requirements may differ between jurisdictions.

Reported benchmark results

Benchmark results in this section are developer-reported unless otherwise stated. They depend on prompts, sampling count, reasoning budget, context length, tool availability, test-set revision, judge model, and execution harness.

Grok-1.5 and Grok-1.5V

xAI reported the following Grok-1.5 results:

Benchmark	Grok-1.5
GSM8K	90.0%
MATH	50.6%
HumanEval	74.1%

For Grok-1.5V, xAI introduced RealWorldQA, a benchmark intended to test spatial understanding and interpretation of real-world visual material. The company reported 68.7% for Grok-1.5V and 61.4% for its GPT-4V comparison under the published setup.

These figures reflect 2024 checkpoints and evaluation protocols and should not be compared directly with later model results without controlling for benchmark versions and prompting.

Grok 3

For the reasoning configuration described in the Grok 3 announcement, xAI reported:

Benchmark	Grok 3 Think
AIME 2025	93.3% under the reported Cons@64 setting
GPQA	84.6%
LiveCodeBench	79.4%

The AIME score used repeated sampling and selection rather than a single deterministic answer. It should not be compared directly with Pass@1 results.

Grok 4

xAI reported a score of 50.7% for Grok 4 Heavy on the text-only subset of Humanity's Last Exam under its published evaluation setup.

Grok 4 Heavy used more test-time computation than the standard model, so its results measure a model-plus-inference-strategy system rather than a single one-pass completion.

Grok 4.5

Selected results reported in the July 2026 Grok 4.5 release are:

Benchmark	Grok 4.5
DeepSWE 1.0	62.0%
DeepSWE 1.1	53.0%
SWE Marathon	29.0%
Terminal-Bench 2.1	83.3%
SWE-Bench Pro	64.7%

xAI positions these results as evidence of improved project-scale software engineering, terminal work, debugging, and autonomous execution. DeepSWE and SWE Marathon are comparatively new or specialized evaluations, and their scores have less historical comparability than long-established fixed benchmarks.

The release also reports generation throughput of approximately 80 output tokens per second in the cited hosted configuration. Actual speed varies with request length, region, load, reasoning effort, tool use, and service tier.

Interpretation of model comparisons

Statements that a Grok release “surpassed GPT-4”, Claude, Gemini, or another model should identify:

• the exact Grok checkpoint;
• the exact comparison checkpoint;
• the benchmark revision;
• the date;
• the sampling and reasoning configuration;
• whether tools were available;
• whether the result came from the developer or an independent evaluator.

A model can lead on one mathematics or coding benchmark while performing worse on factuality, multilingual use, instruction following, latency, safety, or another task.

Licensing and open-weight releases

Grok does not have one licence applying to every model.

Release	Licence or status	Important qualification
Grok-1 base checkpoint	Apache License 2.0	Raw base weights and architecture; not the complete production chatbot
Grok-2 released checkpoint	xAI Community License Agreement	Custom, revocable licence with use and redistribution conditions
Grok-1.5 and later production models	Proprietary unless a specific release states otherwise	API or consumer access does not imply downloadable weights

The Grok-2 community licence permits certain commercial use subject to xAI's acceptable-use policy. It also includes restrictions on using the model materials, derivatives, or outputs to train other general-purpose or foundation models and requires “Powered by xAI” attribution for qualifying distributions.

Because the Grok-2 licence contains field-of-use and downstream-training restrictions, the checkpoint should be described as open-weight or source-available, not as an unrestricted open-source model under the conventional Open Source Initiative definition.

As of 10 July 2026, xAI had not released the weights of Grok 3, Grok 4, Grok 4.3, or Grok 4.5.

Safety, privacy, and regulation

xAI Frontier AI Framework

xAI's Frontier Artificial Intelligence Framework, effective 30 June 2026, describes governance for advanced-model development and deployment.

The framework identifies four principal risk domains:

• chemical, biological, radiological, and nuclear risks;
• offensive cybersecurity;
• loss of control;
• harmful manipulation.

It describes lifecycle evaluations, annual systemic-risk assessments, trigger-point reviews, red-team testing, and post-deployment monitoring and refers to frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.

The document is a developer policy. It is not an independent certification that every Grok deployment is safe or compliant.

Grok 4 model card

The Grok 4 model card discusses evaluations of abuse potential, concerning model propensities, dual-use capabilities, and the effect of safeguards. xAI reported low residual risk under its internal assessment after applying mitigations.

The assessment should be interpreted as the developer's evaluation. Risk can change when safeguards are removed, tools are added, the model is fine-tuned, or the deployment environment grants additional permissions.

Data protection proceedings

In August 2024, Ireland's Data Protection Commission stated that X had agreed to suspend processing of public posts from European Economic Area users collected between 7 May and 1 August 2024 for the purpose of training Grok.

In April 2025, the commission opened a formal inquiry into the lawfulness and transparency of processing public X posts from European users for Grok training.

The opening of an inquiry is not a final finding that a violation occurred.

European Union platform investigation

In January 2026, the European Commission opened formal proceedings under the Digital Services Act concerning X's deployment of Grok and its management of systemic risks involving illegal or harmful content.

The proceedings concern platform-risk management and compliance obligations. They should not be represented as a final judicial or regulatory determination.

Non-consensual sexualized imagery inquiry

In February 2026, Ireland's Data Protection Commission opened a separate inquiry concerning the creation and publication through X's Grok account of non-consensual sexualized images, including material alleged to depict minors.

This was an investigation into alleged processing and product behavior, not a concluded finding of liability.

Privacy controls

Users and organizations should examine:

• whether prompts and outputs are retained;
• whether interactions are eligible for model training;
• opt-out and deletion controls;
• X account privacy settings;
• organization-level data-sharing terms;
• storage region and subprocessors;
• handling of uploaded documents, images, source code, and credentials.

A model's consumer privacy settings may differ from the API or enterprise terms.

Research directions

Published xAI releases suggest several continuing research directions:

• Project-scale software engineering. Improving consistency across large repositories, long debugging sessions, migrations, and test-driven implementation.
• Long-horizon agents. Training models to plan, use tools, recover from errors, and verify outcomes over many actions.
• Test-time computation. Allocating reasoning effort dynamically and coordinating multiple candidate trajectories.
• Long-context systems. Improving retrieval, memory, prompt-injection resistance, and chronology across hundreds of thousands or millions of tokens.
• Search-grounded generation. Improving source selection, citation accuracy, freshness, and synthesis across web and X content.
• Multimodal reasoning. Combining textual and visual evidence while keeping image, video, and voice generation controllable and attributable.
• Efficient serving. Reducing latency and cost for reasoning, search, and agent workflows.
• Frontier-model evaluation. Developing reproducible tests for cyber capability, manipulation, loss of control, and other systemic risks.
• Secure tool use. Enforcing least privilege, sandboxing, provenance, and human approval for consequential actions.

xAI had not published a binding specification or release schedule for a model after Grok 4.5 as of 10 July 2026. Claims about a future Grok 5 architecture, parameter count, or launch date would therefore be speculative.

External links

• xAI official website
• Grok consumer service
• xAI developer documentation
• Current xAI model catalogue
• xAI organization on GitHub
• xAI organization on Hugging Face
• Grok-1 open-release repository
• Grok-2 open-weight checkpoint

See also

• Large language model

Literature

• Vaswani, A. et al. Attention Is All You Need. Advances in Neural Information Processing Systems, 2017.
• Shazeer, N. et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. arXiv:1701.06538, 2017.
• Lepikhin, D. et al. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. arXiv:2006.16668, 2020.
• Fedus, W. et al. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. Journal of Machine Learning Research, 2022.
• Wei, J. et al. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 2022.
• Li, K. et al. MME-RealWorld: Could Your Multimodal LLM Challenge High-Fidelity Real-World Data?. arXiv:2408.13257, 2024.
• Tran, P. et al. Search Arena: Analyzing Search-Augmented Large Language Models. arXiv:2506.05334, 2025.