Grok 4.1 Fast
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Last reviewed
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Grok 4.1 Fast is a tool-calling specialist large language model from xAI, the AI company founded by Elon Musk. It was announced on November 19, 2025, alongside the Agent Tools API, and xAI describes it as the company's best model for agentic tool use, built for enterprise scenarios such as customer support, finance, and deep research.[1]
The model ships with a 2 million-token context window and is offered in two variants: a reasoning version that pauses to think before acting, and a low-latency non-reasoning version for instant replies. Both are accessible through the xAI API and through OpenAI-compatible surfaces such as the OpenAI Responses API. Pricing starts at $0.20 per million input tokens and $0.50 per million output tokens, which makes Grok 4.1 Fast one of the cheapest frontier-class agentic models available at launch.[1][7]
Grok 4.1 Fast was trained with reinforcement learning inside simulated tool environments covering dozens of domains. xAI says the goal was to make tool use a core competency rather than an add-on, so the model can plan, invoke multiple tools in parallel, and continue across many turns until it has enough evidence to answer.[1]
Grok 4.1 Fast sits in the Grok 4 generation as a smaller, cheaper, agent-focused sibling to the flagship Grok 4 reasoning model. The full release sequence places it as the eighth public Grok model.
| Model | Release date | Notes |
|---|---|---|
| Grok 1 | November 3, 2023 | First Grok model. Weights open-sourced March 2024. |
| Grok 1.5 | March 28, 2024 | Context window expanded to 128k tokens. |
| Grok 1.5V | April 12, 2024 | First xAI model with image understanding. |
| Grok 2 | August 14, 2024 | Multimodal input, image generation via Black Forest Labs. |
| Grok 3 | February 17, 2025 | Reasoning-focused, debuted on the X platform. |
| Grok 4 | July 9, 2025 | Frontier reasoning model with native tool calling. |
| Grok 4 Fast | September 19, 2025 | First Fast variant, optimized for cost and latency. |
| Grok 4.1 | November 17, 2025 | Conversational refresh with improved emotional intelligence and lower hallucination. |
| Grok 4.1 Fast | November 19, 2025 | Agent-tuned variant, paired with the Agent Tools API launch. |
Grok 4.1 Fast inherits the conversational and factual improvements of Grok 4.1 but is purpose-built for autonomous, tool-using workflows rather than open-ended chat.[1][6]
The model is published under two API identifiers, separating reasoning behaviour into distinct surfaces.
| Specification | grok-4-1-fast-reasoning | grok-4-1-fast-non-reasoning |
|---|---|---|
| Release date | November 19, 2025 | November 19, 2025 |
| Context window | 2,000,000 tokens | 2,000,000 tokens |
| Maximum output | 30,000 tokens | 30,000 tokens |
| Reasoning mode | Extended chain of thought | Direct response |
| Input modalities | Text and image | Text and image |
| Output modality | Text | Text |
| Tool calling | Native | Native |
| Structured outputs | Yes | Yes |
| Cached input pricing | Yes | Yes |
| Time to first token (Artificial Analysis) | 8.69 seconds | 0.56 seconds |
| Output speed (Artificial Analysis) | 113.6 tokens per second | 133.4 tokens per second |
On the xAI API the variants are selected by model name, while on the OpenRouter and Oracle Cloud surfaces the same names appear with provider prefixes such as xai.grok-4-1-fast-reasoning on Oracle and x-ai/grok-4.1-fast on OpenRouter, with reasoning enabled through a request parameter.[2][3][7]
xAI trained Grok 4.1 Fast with long-horizon reinforcement learning inside synthetic, fully simulated tool environments. Each episode required the model to chain many tool calls together, recover from errors, and maintain state across the full 2 million-token context. The training set covered tools across dozens of domains, so that performance on any single tool surface generalises rather than overfitting to a narrow benchmark.[1]
Two design choices shape the model's behaviour. First, both variants share the same backbone, with reasoning toggled at inference rather than baked into a separate weight set, which keeps latency predictable when developers switch modes mid-conversation. Second, xAI trained the model to prefer conservative tool selection: it tends to call only the tools it believes it needs, then waits for the results before deciding what to do next. The launch post claims this reduces wasted calls in long agent sessions and helps keep cost under control.[1]
The company also reports that Grok 4.1 Fast cuts hallucination rates roughly in half compared to Grok 4 Fast on internal FActScore evaluations, while staying competitive with the larger Grok 4 on the same metric.[1]
xAI's launch post highlights agentic tool-use evaluations rather than traditional knowledge benchmarks. The headline numbers come from the company's own measurements and from third-party evaluators such as Artificial Analysis.
| Evaluation | Result | Source |
|---|---|---|
| tau2-bench Telecom (dual-control agent) | Top score among major closed models | xAI launch post[1] |
| Berkeley Function Calling Leaderboard v4 | Top score among major closed models | xAI launch post[1] |
| FActScore (factuality) | About half the hallucination rate of Grok 4 Fast | xAI launch post[1] |
| Artificial Analysis Intelligence Index (reasoning) | 39 | Artificial Analysis[8] |
| Artificial Analysis Intelligence Index (non-reasoning) | 24 | Artificial Analysis[7] |
The tau-bench Telecom split is a dual-control evaluation in which both an agent and a simulated customer can edit shared state, which makes it a stress test for long-horizon planning under partial information. The Berkeley Function Calling Leaderboard v4 evaluates how reliably a model selects, formats, and parallelises function calls across more than 2,000 question and tool pairs that include multi-turn and parallel call cases.[1][9][10]
On traditional academic benchmarks Grok 4.1 Fast is competitive but not class-leading. Independent measurements report MMLU Pro around 74.3 percent, GPQA Diamond around 63.7 percent, AIME 2025 around 34.3 percent, and LiveCodeBench around 39.9 percent, which place it below the much larger Grok 4 on pure coding and math but above many similarly priced peers.[5]
The Agent Tools API launched on the same day as Grok 4.1 Fast and is the model's natural companion. It packages a set of server-side tools that xAI hosts, so developers do not need to manage their own search APIs, code sandboxes, or vector stores.[1][11]
| Tool | Purpose |
|---|---|
| Web search | Real-time queries across the open web with citations. |
| X search | Real-time queries against posts on the X platform. |
| Code execution | Python in a secure sandbox for data analysis and simulation. |
| Collections search | Retrieval over user-uploaded document collections. |
| Remote MCP | Connections to third-party servers that speak the Model Context Protocol. |
The model decides when and how to invoke each tool, often calling several in parallel during a single turn. xAI charges separately for token use and for each tool invocation, although it offered both Grok 4.1 Fast and the Agent Tools API free of charge for the first two weeks after launch, with the model also free during that window on OpenRouter.[1][11]
The Agent Tools API is also the foundation for the grok-4.20-multi-agent model, which orchestrates either four or sixteen specialist agents that share the same tool stack and synthesise findings through a designated leader agent.[12]
Grok 4.1 Fast is positioned as a low-cost option compared with peer agentic models from OpenAI, Anthropic, and Google.
| Tier | Price |
|---|---|
| Input tokens | $0.20 per 1M |
| Output tokens | $0.50 per 1M |
| Cached input tokens | About $0.05 per 1M |
| Blended (3:1) rate | $0.28 per 1M |
Pricing is identical for the reasoning and non-reasoning variants. On OpenRouter the model carries the same headline rates, and during the launch promotion the :free route allowed unlimited use at no charge for two weeks. Cached input tokens, which are billed at roughly a quarter of the fresh input rate, make Grok 4.1 Fast especially attractive for repetitive agent loops where the same system prompt and tool schemas are sent on every call.[1][7][13]
Developers can call the model through several surfaces.
| Surface | Notes |
|---|---|
| xAI API (native) | Direct access via grok-4-1-fast-reasoning and grok-4-1-fast-non-reasoning. |
| OpenAI-compatible Responses API | Same endpoints accept Grok 4.1 Fast as a drop-in. |
| OpenRouter | Routes to xAI with optional fallback providers. |
| Oracle Cloud Generative AI | Hosted as xai.grok-4-1-fast-reasoning and xai.grok-4-1-fast-non-reasoning. |
| AI/ML API and other aggregators | Available with provider-prefixed model names. |
The OpenAI-compatible surface is important because it lets teams that already use the OpenAI Responses API point at Grok 4.1 Fast with little more than a base URL change. The xAI native SDK adds first-class support for the Agent Tools stack and for streaming, structured output, and asynchronous batch jobs.[1][2][3]
The launch post and early third-party reviews focus on a few concrete patterns.
Customer support automation is the canonical example. The dual-control nature of the tau2-bench Telecom benchmark mirrors a real call centre, where the agent and the customer both edit shared account state, and Grok 4.1 Fast was tuned to handle exactly that pattern of long, conversational, tool-mediated work.[1]
Finance and agentic search make up the second main category. Because the model can fan out across web search, X search, and collections search at the same time, it can pull together a stock dossier or a competitive analysis in a single turn, then keep refining as the user pushes back. xAI cites finance specifically as a target domain, alongside customer support.[1]
Long-running multi-turn tasks benefit from the 2 million-token context. The model can keep an entire incident transcript, a long codebase, or a multi-day chat history in working memory without forced summarisation. The Artificial Analysis evaluations measured performance across the full window, which xAI presents as a contrast to other agentic models that degrade past a few hundred thousand tokens.[1][7]
Browser automation and data analysis round out the use cases. The hosted code execution tool gives the model a Python sandbox for spreadsheet work, plotting, and quick calculations, while remote MCP connections let it drive third-party browsers, ticket systems, or databases without custom glue code.[1][11]
Grok 4.1 Fast competes with a small cluster of agent-focused offerings from the major frontier labs.
| Model | Context window | Headline tool stack | Input or output price (per 1M) |
|---|---|---|---|
| Grok 4.1 Fast (xAI) | 2,000,000 | Web, X, code, collections, remote MCP via Agent Tools API | $0.20 in, $0.50 out |
| OpenAI o4-mini with Responses API | 200,000 | Web search, file search, code interpreter, computer use | Mid-tier o-series pricing |
| Anthropic Claude Sonnet 4.5 with agent tools | 200,000 | Computer Use, web search, code execution, MCP | Mid-tier Claude pricing |
| Google Gemini 2.5 Flash with function calling | 1,000,000 | Function calling, Google Search grounding, code execution | Low Flash pricing |
| Mistral Le Chat with function calling | Up to 256,000 | Function calling, web search, code interpreter | Mid-tier Mistral pricing |
The headline differentiators for Grok 4.1 Fast are the size of the context window, the bundling of an MCP-compatible tool stack at the API level, and the unusually low blended price for a model that posts top scores on agent benchmarks. Its main weaknesses are also visible from the table: it is newer than its peers, the xAI ecosystem has fewer pre-built integrations than the OpenAI Responses API or Anthropic Claude Computer Use stack, and it is less battle-tested in production agent deployments.[1][7][8]
The launch drew steady coverage in the developer-focused press. Artificial Analysis ranked the reasoning variant 17th overall and the non-reasoning variant 18th in its non-reasoning class on the Intelligence Index at release, while singling out the model for its unusually long context and aggressive pricing.[7][8] OpenRouter promoted the free two-week window heavily and saw it climb its trending charts during that promotion period.[13] Independent reviewers on Medium and developer blogs echoed the headline claims about agent benchmark dominance but noted that on raw coding and math the model still trails the larger Grok 4 and the top tier of OpenAI and Anthropic models.[5]
Social reaction was driven in part by xAI's own posts on X, which framed Grok 4.1 Fast as the first frontier model designed specifically for autonomous tool use rather than chat. Posts comparing its tau2-bench Telecom scores to GPT-5.1, Claude Opus 4.5, and Gemini 3 Pro circulated widely in the agent-development community, although direct apples-to-apples comparisons depend heavily on the exact benchmark version and prompt template.[6][13]
The model is real but young, and its limitations matter for anyone planning a production deployment.
First, it is xAI ecosystem specific. The Agent Tools API, the multi-agent orchestrator, and the deepest integrations with the X platform all live inside xAI's hosted infrastructure. Teams that want to run their own search or code sandboxes get less benefit from the model's tool-calling training, which is biased towards the xAI tool surface shapes.
Second, it is newer than its peer agent models. The OpenAI Responses API, Anthropic's Computer Use stack, and Google's function-calling tooling all have a longer track record in production, more SDK support, and broader third-party tooling. Grok 4.1 Fast launched with strong benchmarks but with a thinner integration ecosystem.
Third, the model is conservative outside xAI's hosted tools. Independent reviewers report that when developers wire up custom function schemas and run the model outside the Agent Tools API, the gap between Grok 4.1 Fast and the top OpenAI and Anthropic models narrows substantially. The reinforcement learning curriculum was tuned on simulated environments that closely resemble the hosted tool stack.[5]
Finally, on raw academic and coding benchmarks Grok 4.1 Fast trails the larger flagship models, including the original Grok 4. For workloads that depend on hard math, deep code generation, or exotic knowledge tasks rather than tool-mediated workflows, the larger reasoning models from xAI, OpenAI, Anthropic, and Google still post higher scores at the cost of much higher prices.[5][8]