GPT-4o vs Grok 4.20

Across our 12-test internal benchmark suite, Grok 4.20 wins 8 categories outright, ties 4, and wins 0 head-to-head losses. GPT-4o wins zero categories outright.

Where Grok 4.20 leads:

• Tool calling (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 17 models out of 54 tested. GPT-4o scores 4/5, tied for 18th among 29 models. For agentic workflows and function-calling pipelines, this gap is meaningful — accurate argument selection and call sequencing at 5/5 vs 4/5 reduces error rates in multi-step automations.
• Strategic analysis (5 vs 2): This is the largest gap in the suite. Grok 4.20 scores 5/5 (tied for 1st among 26 models), GPT-4o scores just 2/5, ranking 44th of 54 — in the bottom quintile of all models we've tested. For business intelligence, financial tradeoff reasoning, or any task requiring nuanced analysis with real numbers, GPT-4o trails badly here.
• Faithfulness (5 vs 4): Grok 4.20 scores 5/5 (tied for 1st among 33 models), GPT-4o scores 4/5 (ranked 34th of 55). Faithfulness measures how closely a model sticks to source material without hallucinating — critical for summarization, RAG pipelines, and document-grounded Q&A.
• Structured output (5 vs 4): Grok 4.20 scores 5/5 (tied for 1st among 25 models), GPT-4o scores 4/5 (ranked 26th of 54). JSON schema compliance and format adherence at max score matters for any API integration producing structured data.
• Long context (5 vs 4): Grok 4.20 scores 5/5 (tied for 1st among 37 models). GPT-4o scores 4/5, ranked 38th of 55. Grok 4.20 also has a 2,000,000-token context window vs GPT-4o's 128,000 tokens — a 15x difference that enables document-scale retrieval tasks GPT-4o can't handle at all.
• Multilingual (5 vs 4): Grok 4.20 scores 5/5 (tied for 1st among 35 models). GPT-4o scores 4/5, ranked 36th of 55. Non-English output quality is consistently stronger with Grok 4.20 in our testing.
• Creative problem solving (4 vs 3): Grok 4.20 scores 4/5 (ranked 9th of 54), GPT-4o scores 3/5 (ranked 30th of 54). Generating non-obvious, feasible ideas favors Grok 4.20.
• Constrained rewriting (4 vs 3): Grok 4.20 scores 4/5 (ranked 6th of 53), GPT-4o scores 3/5 (ranked 31st of 53). Compression within strict character limits is a clear Grok 4.20 strength.

Ties (both models perform equally):

• Classification (4/4): Both tied for 1st among 30 models out of 53. No difference here.
• Agentic planning (4/4): Both tied at rank 16 of 54 among 26 models. Neither model separates.
• Persona consistency (5/5): Both tied for 1st among 37 models out of 53.
• Safety calibration (1/1): Both score 1/5, tied at rank 32 of 55 along with 24 other models. This is a weak area for both — neither model meaningfully distinguishes between harmful and legitimate requests in our testing.

External benchmarks (Epoch AI data):

GPT-4o has external benchmark data available. On SWE-bench Verified (real GitHub issue resolution), GPT-4o scores 31% — ranking last (12th of 12) among models we have external data for, and well below the median of 70.8% across those models. On MATH Level 5 competition math, GPT-4o scores 53.3%, ranking 12th of 14 (below the 94.15% median). On AIME 2025, GPT-4o scores 6.4%, ranking 22nd of 23 (far below the 83.9% median). All three external scores place GPT-4o near the bottom of the field on math and coding tasks by these third-party measures. Grok 4.20 has no external benchmark scores in our current dataset, so a direct external comparison cannot be made — but GPT-4o's weak external scores remove any coding or math advantage it might otherwise claim.

GPT-4o costs $2.50/MTok input and $10/MTok output. Grok 4.20 costs $2.00/MTok input and $6/MTok output — 20% cheaper on input and 40% cheaper on output. In output-heavy workloads (the typical cost driver), the gap compounds fast: at 1M output tokens/month, GPT-4o costs $10 vs Grok 4.20's $6 — a $4 difference. At 10M tokens/month that's $40 saved. At 100M tokens/month — realistic for production API deployments — you're saving $400/month, or $4,800/year, while getting higher benchmark scores. For consumers or light API users the dollar gap is negligible. For developers running high-volume pipelines, the cost case for Grok 4.20 is straightforward: better performance at lower cost.

Choose Grok 4.20 if: you're building agentic or tool-calling pipelines (5/5 vs 4/5), need faithful summarization or RAG outputs (5/5 vs 4/5), work with very long documents (2M token context vs 128K), require strong strategic analysis (5/5 vs 2/5), produce structured data outputs (5/5 vs 4/5), work in non-English languages (5/5 vs 4/5), or simply want the stronger overall performer at a lower price ($6/MTok output vs $10/MTok).

Choose GPT-4o if: you're already integrated into the OpenAI ecosystem and switching costs outweigh the performance and cost gap, or you specifically need GPT-4o's supported parameters not available in Grok 4.20 (such as frequency_penalty, logit_bias, logprobs-based token control, presence_penalty, web_search_options, or top_logprobs). Note that GPT-4o's external benchmark scores (31% SWE-bench, 53.3% MATH Level 5, 6.4% AIME 2025 per Epoch AI) place it near the bottom of the field on coding and math — so neither model currently makes a strong case for math-heavy or autonomous coding tasks based on available evidence.