GPT-4.1 vs Grok Code Fast 1

We tested both models across our 12-test suite and report where each wins or ties in our testing. Summary from our results: GPT-4.1 wins in strategic analysis, constrained rewriting, tool calling, faithfulness, long context, persona consistency, and multilingual (7 wins). Grok Code Fast 1 wins safety calibration and agentic planning (2 wins). The two models tie on structured output, creative problem solving, and classification.

Detailed walk-through (score = our 1–5 scale unless noted):

• Faithfulness: GPT-4.1 scored 5 (tied for 1st of 55 models, tied with 32 others); Grok scored 4 (rank 34/55). In practice, GPT-4.1 is more likely to stick to source material in our tests — important for retrieval, citation, and factual tasks.
• Long context: GPT-4.1 scored 5 (tied for 1st of 55, tied with 36); Grok scored 4 (rank 38/55). This matters for multi-document retrieval and workflows over 30K+ tokens — GPT-4.1 is the clear choice in our testing.
• Tool calling: GPT-4.1 scored 5 (tied for 1st of 54, tied with 16); Grok scored 4 (rank 18/54). For function selection, argument accuracy, and sequencing in agent workflows, GPT-4.1 outperformed Grok in our tests.
• Agentic planning: Grok scored 5 (tied for 1st of 54, tied with 14); GPT-4.1 scored 4 (rank 16/54). For goal decomposition and failure recovery in our agentic planning tests, Grok is stronger.
• Safety calibration: Grok scored 2 (rank 12/55); GPT-4.1 scored 1 (rank 32/55). In our safety-calibration tests (refusing harmful requests while permitting legitimate ones), Grok performed better.
• Strategic analysis: GPT-4.1 scored 5 (tied for 1st of 54); Grok scored 3 (rank 36/54). For nuanced tradeoff reasoning with numbers, GPT-4.1 leads in our results.
• Constrained rewriting: GPT-4.1 scored 5 (tied for 1st of 53); Grok scored 3 (rank 31/53). When compressing or rewriting under strict character limits, GPT-4.1 produced higher-quality outputs in our tests.
• Structured output & classification: Both scored 4 and tied on ranking (structured output rank 26/54 for both; classification tied for 1st with many models). Both models produce reliable JSON/schema-compliant outputs and routing in our evaluations.
• Creative problem solving & persona consistency & multilingual: GPT-4.1 scored 3/5 creative, 5/5 persona consistency, 5/5 multilingual; Grok scored 3 creative, 4 persona consistency, 4 multilingual. GPT-4.1 is stronger on persona and multilingual tasks in our tests.

External/third-party signal (supplementary): GPT-4.1 achieved 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 (these are Epoch AI results and reported as supplementary external scores). These external results help explain GPT-4.1’s coding/math behavior in our suite but do not change the internal 1–5 comparisons.

Per the payload, GPT-4.1 costs $2.00 per 1k input tokens and $8.00 per 1k output tokens; Grok Code Fast 1 costs $0.20 per 1k input and $1.50 per 1k output. Combined input+output per 1k: GPT-4.1 = $10.00, Grok = $1.70 (price ratio 5.33×). At 1M tokens/month (1,000 mtoks) total monthly cost: GPT-4.1 ≈ $10,000 vs Grok ≈ $1,700. At 10M tokens: GPT-4.1 ≈ $100,000 vs Grok ≈ $17,000. At 100M tokens: GPT-4.1 ≈ $1,000,000 vs Grok ≈ $170,000. Who should care: high-volume chatbots, code-assistants, or SaaS platforms with heavy per-user token usage will see material savings with Grok; teams that require GPT-4.1’s long-context, multimodal inputs, and top-rung faithfulness may justify the 5.33× higher spend.

Choose GPT-4.1 if: you need the best long-context handling, top-tier faithfulness, robust tool calling, multilingual and persona-consistent outputs, or multimodal inputs (GPT-4.1 supports text+image+file->text). Examples: document retrieval across million-token corpora, multi-step tool-driven agents where accurate function choice matters, or production systems that prioritize accuracy over cost.

Choose Grok Code Fast 1 if: you need a fast, economical model for agentic coding and planning, or you operate at high token volumes and must control costs. Examples: high-volume code-assistants, CI-integrated code generation, or experimental agentic systems where visible reasoning traces and lower per-token costs ($1.50 vs $8.00 output per 1k) materially reduce monthly spend.