[ad_1]
xAI introduced Grok-4-Fast, a cost-optimized successor to Grok-4 that merges “reasoning” and “non-reasoning” behaviors into a single set of weights controllable via system prompts. The model targets high-throughput search, coding, and Q&A with a 2M-token context window and native tool-use RL that decides when to browse the web, execute code, or call tools.
Architecture note
Previous Grok releases split long-chain “reasoning” and short “non-reasoning” responses across separate models. Grok-4-Fast’s unified weight space reduces end-to-end latency and tokens by steering behavior via system prompts, which is relevant for real-time applications (search, assistive agents, and interactive coding) where switching models penalizes both latency and cost.
Search and agentic use
Grok-4-Fast was trained end-to-end with tool-use reinforcement learning and shows gains on search-centric agent benchmarks: BrowseComp 44.9%, SimpleQA 95.0%, Reka Research 66.0%, plus higher scores on Chinese variants (e.g., BrowseComp-zh 51.2%). xAI also cites private battle-testing on LMArena where grok-4-fast-search (codename “menlo”) ranks #1 in the Search Arena with 1163 Elo, and the text variant (codename “tahoe”) sits at #8 in the Text Arena, roughly on par with grok-4-0709.
Performance and efficiency deltas
On internal and public benchmarks, Grok-4-Fast posts frontier-class scores while cutting token usage. xAI reports pass@1 results of 92.0% (AIME 2025, no tools), 93.3% (HMMT 2025, no tools), 85.7% (GPQA Diamond), and 80.0% (LiveCodeBench Jan–May), approaching or matching Grok-4 but using ~40% fewer “thinking” tokens on average. The company frames this as “intelligence density,” claiming a ~98% reduction in price to reach the same benchmark performance as Grok-4 when the lower token count and new per-token pricing are combined.
Deployment and price
The model is generally available to all users in Grok’s Fast and Auto modes across web and mobile; Auto will select Grok-4-Fast for difficult queries to improve latency without losing quality, and—for the first time—free users access xAI’s latest model tier. For developers, xAI exposes two SKUs—grok-4-fast-reasoning and grok-4-fast-non-reasoning—both with 2M context. Pricing (xAI API) is $0.20 / 1M input tokens (<128k), $0.40 / 1M input tokens (≥128k), $0.50 / 1M output tokens (<128k), $1.00 / 1M output tokens (≥128k), and $0.05 / 1M cached input tokens.
5 Technical Takeaways:
- Unified model + 2M context. Grok-4-Fast uses a single weight space for “reasoning” and “non-reasoning,” prompt-steered, with a 2,000,000-token window across both SKUs.
- Pricing for scale. API pricing starts at $0.20/M input, $0.50/M output, with cached input at $0.05/M and higher rates only beyond 128K context.
- Efficiency claims. xAI reports ~40% fewer “thinking” tokens at comparable accuracy vs Grok-4, yielding a ~98% lower price to match Grok-4 performance on frontier benchmarks.
- Benchmark profile. Reported pass@1: AIME-2025 92.0%, HMMT-2025 93.3%, GPQA-Diamond 85.7%, LiveCodeBench (Jan–May) 80.0%.
- Agentic/search use. Post-training with tool-use RL; positioned for browsing/search workflows with documented search-agent metrics and live-search billing in docs.
Summary
Grok-4-Fast packages Grok-4-level capability into a single, prompt-steerable model with a 2M-token window, tool-use RL, and pricing tuned for high-throughput search and agent workloads. Early public signals (LMArena #1 in Search, competitive Text placement) align with xAI’s claim of similar accuracy using ~40% fewer “thinking” tokens, translating to lower latency and unit cost in production.
Check out the Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
[ad_2]
Source_link
