Grok 4.20 vs Llama 4 Maverick

Grok 4.20 has benchmark scores across 12 tests; Llama 4 Maverick has scores across 11 (tool calling was not recorded — a 429 rate limit error during testing, noted as likely transient). Here's the test-by-test breakdown:

Where Grok 4.20 wins clearly:

• Strategic analysis: Grok 4.20 scores 5/5 (tied for 1st of 54 models with 25 others); Maverick scores 2/5 (rank 44 of 54). This is the widest gap in the comparison and matters most for business intelligence, competitive analysis, and nuanced tradeoff reasoning.
• Agentic planning: Grok 4.20 scores 4/5 (rank 16 of 54); Maverick scores 3/5 (rank 42 of 54). For goal decomposition and multi-step failure recovery — the backbone of agentic workflows — Grok 4.20 is substantially ahead.
• Long context: Grok 4.20 scores 5/5 (tied for 1st of 55) with a 2M-token context window; Maverick scores 4/5 (rank 38 of 55) with a 1M-token window. Both handle long documents, but Grok 4.20's retrieval accuracy and context capacity are both higher.
• Faithfulness: Grok 4.20 scores 5/5 (tied for 1st of 55); Maverick scores 4/5 (rank 34 of 55). Grok 4.20 sticks closer to source material — important for RAG applications and summarization where hallucination is costly.
• Multilingual: Grok 4.20 scores 5/5 (tied for 1st of 55); Maverick scores 4/5 (rank 36 of 55). Both handle non-English output, but Grok 4.20 maintains better quality.
• Structured output: Grok 4.20 scores 5/5 (tied for 1st of 54); Maverick scores 4/5 (rank 26 of 54). Both are solid for JSON schema compliance, but Grok 4.20 is more reliable at the margin.
• Creative problem solving: Grok 4.20 scores 4/5 (rank 9 of 54); Maverick scores 3/5 (rank 30 of 54).
• Classification: Grok 4.20 scores 4/5 (tied for 1st of 53); Maverick scores 3/5 (rank 31 of 53).
• Constrained rewriting: Grok 4.20 scores 4/5 (rank 6 of 53); Maverick scores 3/5 (rank 31 of 53).

Where Llama 4 Maverick wins:

• Safety calibration: Maverick scores 2/5 (rank 12 of 55); Grok 4.20 scores 1/5 (rank 32 of 55). Notably, both scores are below the field median (p50: 2/5), so neither model excels here — but Maverick is measurably better at refusing harmful requests while permitting legitimate ones.

Tied:

• Persona consistency: Both score 5/5, tied for 1st with 36 other models out of 53 tested. Neither has an edge in character maintenance.

Tool calling note: Grok 4.20 scores 5/5 (tied for 1st of 54 models). Maverick's tool calling score is absent due to a rate limit during testing — this is noted as likely transient, not a capability issue. Given Maverick's pattern across other tests, caution is warranted before assuming parity.

The overall picture: Grok 4.20 outscores Maverick on 10 of the 11 tests where comparison is possible. Maverick's single win (safety calibration) is at a score — 2/5 — that's still below what most production deployments would consider adequate from either model.

Llama 4 Maverick costs $0.15/M input tokens and $0.60/M output tokens. Grok 4.20 costs $2/M input and $6/M output — 13x more expensive on input, 10x on output. In practice:

• At 1M output tokens/month: Maverick costs $0.60, Grok 4.20 costs $6.00. A $5.40/month difference that's negligible for most teams.
• At 10M output tokens/month: Maverick is $6, Grok 4.20 is $60. A $54/month gap — still manageable for most applications.
• At 100M output tokens/month: Maverick is $600, Grok 4.20 is $6,000. A $5,400/month difference that demands justification.

The cost gap matters most to high-volume API consumers — document processors, customer service platforms, content pipelines. For developers prototyping or running low-to-medium traffic products, Grok 4.20's capabilities may be worth the premium. Enterprises pushing hundreds of millions of tokens monthly should evaluate whether Maverick's scores are good enough for their specific tasks before committing to Grok 4.20's pricing.

Choose Grok 4.20 if:

• Your application depends on strategic analysis, agentic planning, or complex tool orchestration — these are the areas with the largest score gaps in our testing.
• You need reliable long-context retrieval across very large documents (Grok 4.20 supports up to 2M tokens vs Maverick's 1M).
• Faithfulness to source material is critical, such as in RAG pipelines, legal document review, or citation-heavy summarization.
• You need structured output or JSON schema compliance at high reliability for downstream systems.
• Your volumes are low-to-medium (under 10M tokens/month), where the $5.40/month cost difference per million output tokens is not a business constraint.
• You need file input support — Grok 4.20's modality is text+image+file; Maverick handles text+image only.

Choose Llama 4 Maverick if:

• You're running high-volume workloads (50M+ output tokens/month) where a 10x cost difference becomes thousands of dollars monthly.
• Your task is routine classification, content generation, or persona-based chat — areas where Maverick's scores, while lower, may be sufficient for your acceptance threshold.
• Safety calibration is a priority: Maverick scored 2/5 vs Grok 4.20's 1/5 in our testing, making it the better option among these two for harm refusal.
• You want to self-host or run on multiple inference providers — Maverick's MoE architecture is designed for flexible deployment.
• You're building a prototype or cost-sensitive MVP and want to validate product-market fit before committing to premium API pricing.