GPT-4.1 vs Grok 4.20

Across our 12-test suite, GPT-4.1 and Grok 4.20 are closely matched: Grok 4.20 wins 2 tests outright, GPT-4.1 wins 1, and 9 end in a tie.

Where Grok 4.20 wins:

• Structured output (5 vs 4): Grok 4.20 scores a perfect 5, ranking tied for 1st of 54 models on JSON schema compliance and format adherence. GPT-4.1 scores 4, ranking 26th of 54. For any application that relies on reliable JSON generation or schema-constrained outputs — API orchestration, data extraction pipelines, form parsing — this is a meaningful real-world gap.
• Creative problem solving (4 vs 3): Grok 4.20 ranks 9th of 54 on generating non-obvious, specific, feasible ideas. GPT-4.1 scores 3 and ranks 30th of 54, below the 50th percentile on this test. For brainstorming, product ideation, or open-ended generation tasks, Grok 4.20 is demonstrably stronger in our testing.

Where GPT-4.1 wins:

• Constrained rewriting (5 vs 4): GPT-4.1 scores 5 and ranks tied for 1st of 53 models (only 5 models share this score) on compression within hard character limits. Grok 4.20 scores 4 and ranks 6th of 53. This matters for headline generation, ad copy, social media formatting, and any task requiring strict output length control.

Where they tie (9 tests):

• Tool calling (both 5/5): Both models tie for 1st of 54 on function selection, argument accuracy, and sequencing — agentic workflows are equally well-served by either model.
• Strategic analysis (both 5/5): Tied for 1st of 54 on nuanced tradeoff reasoning with real numbers.
• Faithfulness (both 5/5): Tied for 1st of 55 on sticking to source material without hallucinating.
• Long context (both 5/5): Both tied for 1st of 55 on retrieval accuracy at 30K+ tokens — though Grok 4.20's 2M context window versus GPT-4.1's ~1M gives it a practical edge at the extreme end.
• Multilingual (both 5/5): Tied for 1st of 55.
• Persona consistency (both 5/5): Tied for 1st of 53.
• Classification (both 4/5): Tied for 1st of 53.
• Agentic planning (both 4/5): Both rank 16th of 54.
• Safety calibration (both 1/5): Both rank 32nd of 55 — neither model performs well here relative to the field, where the 75th percentile is only 2/5.

External benchmarks (Epoch AI data): GPT-4.1 has external benchmark scores available: 48.5% on SWE-bench Verified (rank 11 of 12 models tested), 83% on MATH Level 5 (rank 10 of 14), and 38.3% on AIME 2025 (rank 19 of 23). Grok 4.20 has no external benchmark scores in our dataset. The SWE-bench and AIME results place GPT-4.1 in the lower half of models we have external data for — useful context if you're comparing against the broader competitive field, but they don't change the head-to-head outcome on our internal 12-test suite where Grok 4.20 leads or ties on 11 of 12 tests.

Both models charge $2.00/MTok for input, so the cost gap is entirely on the output side: GPT-4.1 at $8/MTok vs Grok 4.20 at $6/MTok — a 33% premium for GPT-4.1 output tokens.

At real-world volumes that gap compounds quickly:

• 1M output tokens/month: $8 vs $6 — a $2 difference, negligible for most budgets.
• 10M output tokens/month: $80 vs $60 — $20/month, still minor for production APIs.
• 100M output tokens/month: $800 vs $600 — $200/month, a meaningful line item for high-volume applications.

For consumer apps, chatbots, or document pipelines generating hundreds of millions of tokens, Grok 4.20's lower output cost becomes a real operating expense advantage. For developers running occasional queries or low-volume prototypes, the $2/MTok difference is immaterial. The context window difference is also worth noting: Grok 4.20 offers a 2M-token context vs GPT-4.1's ~1M-token window, which may eliminate the need for chunking on very long documents — a cost savings that partially offsets any per-token price comparison. Who should care most about the cost gap: high-volume API consumers, SaaS products with user-generated content, and any pipeline processing large batches of long documents.

Choose Grok 4.20 if:

• Your application relies on structured output or JSON schema compliance — it scores 5 vs GPT-4.1's 4 in our testing.
• You need strong creative problem solving or ideation tasks — it scores 4 vs GPT-4.1's 3, ranking 9th vs 30th of 54 models.
• You're processing very long documents: its 2M-token context window gives it a practical edge over GPT-4.1's ~1M limit.
• Output cost is a factor at scale: at $6/MTok output vs $8/MTok, you save $200/month per 100M output tokens.
• You want access to include_reasoning or logprobs parameters — these are in Grok 4.20's supported parameter list but absent from GPT-4.1's.

Choose GPT-4.1 if:

• Your workflow requires tight character-constrained rewriting (ad copy, headlines, social posts) — it scores 5 vs Grok 4.20's 4, one of only 5 models at the top score on this test.
• You want to benchmark against external coding or math evaluations: GPT-4.1 has published SWE-bench Verified (48.5%) and MATH Level 5 (83%) scores from Epoch AI; Grok 4.20 has no external scores in our dataset.
• You're already integrated into the OpenAI ecosystem and the supported parameter overlap (tools, structured outputs, seed, temperature, etc.) means minimal migration friction.
• Your use case doesn't generate enough output tokens for the $2/MTok price difference to matter.