Speed analysis
Latency measured across all benchmark runs. P50 (median) and P95 (95th percentile) give a realistic picture of response speed under normal and peak load.
Pricing
What you pay per million tokens when you use this model on Tokonomix, plus an estimate for a typical conversation.
Tokens per second
Throughput in tokens per second, derived from measured P50 latency. Higher is better; fluctuations track provider-side load.
Estimated from P50 latency × 200 output tokens — the absolute number depends on this assumption; the trend is what matters.
Capabilities
Availability
Availability
How often this model answers when we call it — measured across real API requests and live tests over the last 30 days. This is separate from quality: these numbers only tell you whether the model responds, not how good the answer is.
Last 7 days
—
Last 30 days
100.0%
n=1
Median response time
2,670ms
n=1
Based on 115 measurements over the last 30 days.
Technical details
Only live API calls and live-test requests count — internal probes and benchmark runs are excluded.
Calls with a custom API key (BYOK) are excluded: those failures are key-specific, not a sign of model downtime.
Failed calls are NOT included in quality scores — quality is measured on successful responses only. Availability and quality are independent signals.
Median response time (p50) across successful calls with a recorded duration. Outliers (very slow or very fast calls) pull the median less than the average.
Total calls (30d)
1
OK responses (30d)
1
Total calls (7d)
0
OK responses (7d)
0
Tokonomix benchmark verdicts
Grok Build 0.1 Debuts with Strong Performance Across Key Benchmarks
Grok Build 0.1 establishes a solid baseline performance profile in its first benchmark evaluation. The model demonstrates particularly strong capabilities in mathematical reasoning, achieving 76.0% on GSM8K and 53.0% on MATH, placing it competitively among frontier models. Coding performance is respectable with 73.3% on HumanEval and 74.0% on MBPP, indicating solid programming assistance capabilities. Multimodal performance is notable, scoring 71.2% on MMMU and 66.4% on MathVista, showing capable vision-language integration. General knowledge benchmarks show room for improvement, with 74.7% on MMLU and 67.3% on ARC Challenge representing moderate performance in broad knowledge tasks. The model now includes JSON structured output support, tool calling capabilities, and vision processing, providing developers with a comprehensive feature set. Instruction following achieves 81.3% on IFEval, suggesting reliable adherence to user directives. As a first release, Grok Build 0.1 presents a capable foundation with particularly strong mathematical and multimodal abilities, though general knowledge tasks may benefit from future refinement.
Quality
—
Latency p50
—
Test runs
0