R1 0528 vs Grok 4.1 Fast

Overview (our 12-test suite): R1 0528 wins 3 tests, Grok 4.1 Fast wins 2, and 7 tests tie (per our win/loss/tie listing). Detailed walk-through:

• Tool calling: R1 0528 scores 5 vs Grok 4 in our tests; R1 is tied for 1st of 54 models (tied with 16 others). This matters for function selection, argument accuracy, and sequencing — choose R1 where reliable tool orchestration is required.
• Safety calibration: R1 0528 scores 4 vs Grok's 1 in our tests; R1 ranks 6 of 55 (tied with 3 others) while Grok ranks 32 of 55. That gap indicates R1 refuses harmful requests and permits legitimate ones more reliably in our scenarios.
• Agentic planning: R1 0528 scores 5 vs Grok 4; R1 is tied for 1st (with 14 others) vs Grok at rank 16 of 54. For goal decomposition and failure recovery R1 has the edge in our testing.
• Structured output: Grok 4.1 Fast wins here (5 vs R1's 4) and is tied for 1st of 54 (with 24 others). If strict JSON/schema compliance is critical, Grok is the safer choice.
• Strategic analysis: Grok scores 5 vs R1's 4; Grok is tied for 1st (with 25 others) while R1 ranks 27 of 54. For nuanced tradeoff reasoning with numbers, Grok led in our tests.
• Ties: constrained_rewriting (4/4), creative_problem_solving (4/4), faithfulness (5/5), classification (4/4), long_context (5/5), persona_consistency (5/5), multilingual (5/5). On these tasks both models performed equivalently in our suite — e.g., both score 5 on long_context and persona_consistency, with R1 and Grok tied for 1st in those categories.
• Math/external benchmarks: R1 0528 reports 96.6% on math_level_5 and 66.4% on aime_2025 (Epoch AI) in our data; Grok has no math_level_5/aime_2025 scores in the payload. Those external results support R1 for high-level math tasks in our dataset. Context: ranks are from our rankings data (e.g., tool_calling: R1 tied for 1st of 54). A higher score here means a practical advantage (e.g., fewer hallucinations in faithfulness, stricter schema adherence in structured output).

Per the payload, R1 0528 costs $0.50 per mTok (input) and $2.15 per mTok (output); Grok 4.1 Fast costs $0.20 (input) and $0.50 (output). R1's output price is 4.3× Grok's (priceRatio = 4.3). Practical monthly examples (mTok = 1,000 tokens):

• For 1,000,000 tokens: R1 input = $0.50×1000 = $500; R1 output = $2.15×1000 = $2,150. Grok input = $0.20×1000 = $200; Grok output = $0.50×1000 = $500. If you split 1M tokens 50/50 input/output, cost = R1 $1,325 vs Grok $350.
• For 10,000,000 tokens (×10): 50/50 split cost = R1 $13,250 vs Grok $3,500.
• For 100,000,000 tokens (×100): 50/50 split cost = R1 $132,500 vs Grok $35,000. Who should care: high-volume deployments (chatbots, vector retrieval, analytics pipelines) will see large absolute differences—enterprises and any team with tens of millions of tokens/month should strongly consider Grok for cost-sensitive production. Teams that need R1's specific tool-calling, safety, or agentic planning strengths should budget the premium.

Choose R1 0528 if: you need best-in-class tool calling, stronger safety calibration, or top agentic planning (R1 scores 5/5 on tool_calling and agentic_planning and 4/5 on safety_calibration in our tests), and you can absorb a 4.3× token-cost premium. Choose Grok 4.1 Fast if: strict structured-output (5/5 structured_output) or strategic-analysis tasks matter, you need a multimodal 2M-token context window (modality: text+image+file->text, 2,000,000 context), or you must minimize token costs at scale (Grok is far cheaper per token). If you need both, evaluate hybrid flows: Grok for high-volume generation and R1 for safety- or tool-critical steps.