GPT-5.4 Nano vs Grok 4.20

Across our 12-test suite, Grok 4.20 wins 3 benchmarks outright, GPT-5.4 Nano wins 1, and 8 are ties.

Where Grok 4.20 wins:

• Tool calling: Grok 4.20 scores 5/5, tied for 1st among 54 models with 16 others. GPT-5.4 Nano scores 4/5, ranked 18th of 54. In practice, this means Grok 4.20 more reliably selects correct functions and sequences multi-step API calls — critical for agentic workflows.
• Faithfulness: Grok 4.20 scores 5/5, tied for 1st of 55 models with 32 others. GPT-5.4 Nano scores 4/5, ranked 34th of 55. This benchmark measures adherence to source material without hallucinating — Grok 4.20's edge matters in RAG pipelines, document summarization, and anywhere the model must stay grounded.
• Classification: Grok 4.20 scores 4/5, tied for 1st of 53 models with 29 others. GPT-5.4 Nano scores 3/5, ranked 31st of 53 (tied with 19 others). A full point gap here is meaningful for routing tasks, content moderation pipelines, or any system that depends on accurate categorization.

Where GPT-5.4 Nano wins:

• Safety calibration: GPT-5.4 Nano scores 3/5, ranked 10th of 55 (only 2 models share this score). Grok 4.20 scores 1/5, ranked 32nd of 55 — a stark gap. Safety calibration measures both refusing harmful requests AND permitting legitimate ones. For consumer-facing applications or regulated industries, this 2-point gap is disqualifying for Grok 4.20.

Where they tie (8 benchmarks):

• Structured output, strategic analysis, multilingual, long context, persona consistency: Both score 5/5 — top-tier, though these are crowded leaderboard positions shared with many other models.
• Constrained rewriting, creative problem solving, agentic planning: Both score 4/5, ranked identically (e.g., both rank 16th of 54 on agentic planning).

External benchmark note: GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), ranking 8th of 23 models tested on that benchmark. Grok 4.20 has no AIME 2025 score in the payload, so no direct comparison is possible there.

GPT-5.4 Nano costs $0.20/M input tokens and $1.25/M output tokens. Grok 4.20 costs $2.00/M input and $6.00/M output — a 10x gap on input and 4.8x on output. At 1M output tokens/month, you're paying $1.25 vs $6.00 — a $4.75 difference that's trivial. At 10M tokens/month, the gap grows to $47.50. At 100M tokens/month, Grok 4.20 costs $475,000 more annually on output alone. Developers running high-volume classification pipelines, content processing, or chat applications should weight this heavily, especially given that GPT-5.4 Nano ties Grok 4.20 on 8 of 12 benchmarks. Grok 4.20's premium is justified specifically for applications where its tool calling (5/5), faithfulness (5/5), and classification (4/5) advantages directly drive business outcomes — think agentic systems making API calls or RAG pipelines where hallucination has real cost.

Choose GPT-5.4 Nano if: you're running high-volume workloads where output cost at scale matters ($1.25 vs $6.00/M tokens); safety calibration is a requirement (scores 3/5 vs Grok 4.20's 1/5); your tasks fall in the 8 tied categories where you'd pay 4.8x more for identical benchmark performance; or you need strong math reasoning (87.8% on AIME 2025 in our data).

Choose Grok 4.20 if: you're building agentic systems that depend on reliable tool calling (5/5 vs 4/5); you're running RAG or document workflows where faithfulness to source material is critical (5/5 vs 4/5); accurate classification is central to your pipeline (4/5 vs 3/5); or you need a 2M token context window vs GPT-5.4 Nano's 400K. The cost premium is defensible when Grok 4.20's specific advantages directly prevent errors with downstream business impact.