R1 vs Grok Code Fast 1

Summary of our 12-test suite (model scores and ranks are from our testing):

• Classification: Grok Code Fast 1 wins (4 vs R1's 2). Grok's classification rank is "tied for 1st with 29 other models out of 53 tested," so it's a reliable router/classifier.
• Strategic analysis: R1 wins (5 vs 3). R1 is "tied for 1st with 25 other models out of 54 tested," meaning it handles nuanced tradeoff reasoning and numeric tradeoffs better in real tasks.
• Constrained rewriting: R1 wins (4 vs 3). R1 ranks 6 of 53 (many share the score) — better at tight character-limited compression.
• Creative problem solving: R1 wins (5 vs 3). R1 is "tied for 1st" here, so it produces more non-obvious, feasible ideas in our tests.
• Faithfulness: R1 wins (5 vs 4). R1 is "tied for 1st with 32 other models out of 55 tested," indicating fewer hallucinations on source-grounded tasks.
• Persona consistency: R1 wins (5 vs 4). R1 is "tied for 1st with 36 other models out of 53 tested," so it better maintains character and resists prompt injection.
• Multilingual: R1 wins (5 vs 4). R1 is "tied for 1st with 34 other models out of 55 tested," showing stronger non-English parity.
• Agentic planning: Grok Code Fast 1 wins (5 vs 4). Grok is "tied for 1st with 14 other models out of 54 tested," making it stronger at goal decomposition and recovery in agentic coding.
• Safety calibration: Grok wins (2 vs R1's 1). Grok ranks 12 of 55 (shared) vs R1 at rank 32, so Grok refuses harmful requests more appropriately in our suite.
• Tool calling, structured output, long context: ties (both 4 in our tests). Both models achieved identical scores on function selection/argument accuracy, JSON/schema adherence, and retrieval at 30k+ tokens in our suite. Additional data: R1 posts external math scores in our payload — 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI) — indicating strong competition-level math performance for R1 (these are Epoch AI scores; we cite them as supplementary). Practical meaning: pick R1 when you need higher faithfulness, creativity, multilingual parity, or constrained rewriting; pick Grok when classification, agentic planning, safety, cost, or extreme context length (256k window) matter.

Prices (per mtok): R1 input $0.70, output $2.50; Grok Code Fast 1 input $0.20, output $1.50. Assuming 1,000 mtok = 1M tokens: output-only cost for 1M tokens is $2,500 (R1) vs $1,500 (Grok); for 10M tokens it's $25,000 vs $15,000; for 100M tokens it's $250,000 vs $150,000. Input-token costs (1M) add $700 (R1) vs $200 (Grok). At these volumes the monthly premium for R1 is $1,000 per 1M output tokens (≈66% more), scaling to $10,000 at 10M and $100,000 at 100M. Teams with heavy inference volumes (SaaS, production APIs) will feel this gap; experimental or low-volume users will find the higher R1 quality easier to justify, while cost-conscious, high-throughput pipelines should prefer Grok Code Fast 1.

Choose R1 if you need higher-quality reasoning, stronger faithfulness, creative problem solving, multilingual parity, or better persona consistency — for example: knowledge-grounded assistants, content that must not hallucinate, math/analysis pipelines, or tasks requiring constrained rewriting (R1 scores 5 on faithfulness and 5 on creative problem solving in our tests). Choose Grok Code Fast 1 if you prioritize cost, agentic coding, classification, or very long-context inputs — for example: inference-heavy code automation, classification/routing services, or large-repo code generation that benefits from a 256k context window and a lower $1.50/output mtok price.