Grok 4.20 vs o3

Across our 12-test internal benchmark suite, Grok 4.20 wins 2 tests outright, o3 wins 1, and they tie on the remaining 9. Here's the test-by-test breakdown:

Where Grok 4.20 wins:

• Classification (4 vs 3): Grok 4.20 ranks tied for 1st of 53 models on this test; o3 ranks 31st of 53. That's a meaningful gap for use cases involving routing, content moderation, or intent detection — tasks where o3 sits below the field median.
• Long context (5 vs 4): Grok 4.20 ties for 1st of 55 models; o3 ranks 38th of 55. This is a significant differentiator. Retrieval accuracy at 30K+ tokens — the skill this test measures — matters for RAG systems, legal document review, and any workflow that feeds large inputs. o3's 200K context window also physically limits what you can attempt, while Grok 4.20 supports up to 2M tokens.

Where o3 wins:

• Agentic planning (5 vs 4): o3 ties for 1st of 54 models; Grok 4.20 ranks 16th of 54. Goal decomposition and failure recovery — what this test measures — are the foundation of autonomous agent workflows. If you're building multi-step agents that need to handle unexpected states, o3 has a real edge here.

Tests where both models tie: Both score identically on structured output (5/5), strategic analysis (5/5), constrained rewriting (4/5), creative problem solving (4/5), tool calling (5/5), faithfulness (5/5), safety calibration (1/5), persona consistency (5/5), and multilingual (5/5). The safety calibration score of 1/5 for both models reflects the same position — rank 32 of 55 — meaning neither is differentiated here, and both fall well below the field on refusing harmful requests while permitting legitimate ones.

Third-party benchmarks (o3 only, sourced from Epoch AI): The payload includes external benchmark data for o3 but not for Grok 4.20, so direct comparison isn't possible on these dimensions. o3 scores 97.8% on MATH Level 5 (rank 2 of 14 models, tied with 2 others — Epoch AI), 83.9% on AIME 2025 (rank 12 of 23 — Epoch AI), and 62.3% on SWE-bench Verified (rank 9 of 12 — Epoch AI). The MATH Level 5 score is particularly strong — above the field median of 94.15% and near the 97.5th percentile threshold. The AIME 2025 score sits exactly at the field median (p50: 83.9%). The SWE-bench Verified score of 62.3% is below the field median of 70.8% among the 12 models tested on that benchmark, suggesting o3's real-world GitHub issue resolution lags behind the top coding-focused models. These scores add useful signal for math and coding decisions, but since Grok 4.20 has no corresponding external scores in the payload, they can't be used to declare a head-to-head winner on those dimensions.

Both models charge identical input costs at $2 per million tokens. The gap opens on output: Grok 4.20 at $6/M vs o3 at $8/M — a 33% premium for o3 output.

At real-world volumes, that difference compounds quickly:

• 1M output tokens/month: $6 vs $8 — a $2 gap, negligible for most teams.
• 10M output tokens/month: $60 vs $80 — $20/month savings with Grok 4.20.
• 100M output tokens/month: $600 vs $800 — $200/month, or $2,400/year. At this scale, the savings are meaningful enough to justify a pricing-driven decision if the two models meet your quality bar equally.

Who should care: high-volume API consumers — document processors, chatbot backends, bulk generation pipelines — will feel the output cost gap most acutely. For low-volume users or those running primarily input-heavy, output-light workloads (e.g., classification, routing), the cost difference is minimal. Note that Grok 4.20's 2M-token context window vs o3's 200K also affects cost math for long-document use cases — processing the same content may require fewer API calls with Grok 4.20.

Choose Grok 4.20 if:

• You need reliable performance on long documents (up to 2M tokens) — it ranks tied for 1st on long context vs o3's rank 38th.
• Your application involves classification, routing, or content categorization — Grok 4.20 scores 4/5 (tied for 1st) vs o3's 3/5 (rank 31st).
• You're running high output volumes and want to reduce costs — $6/M output vs $8/M saves $200/month at 100M tokens.
• You need broad general capability without a specific reason to pay the o3 premium.

Choose o3 if:

• You're building autonomous agents that require multi-step planning and failure recovery — o3 ties for 1st on agentic planning vs Grok 4.20's rank 16th.
• Competition-level math performance is critical — o3 scores 97.8% on MATH Level 5 according to Epoch AI, placing it near the top of models tested.
• You want an established OpenAI API model with predictable tooling and ecosystem integration.
• Your context needs fit within 200K tokens and you don't need Grok 4.20's extended window.