Grok 3 vs Llama 4 Maverick

Grok 3 wins 8 of 11 benchmarks in our testing, ties 3, and loses none. Here's the test-by-test breakdown:

Where Grok 3 wins clearly:

• Strategic analysis: 5 vs 2. The largest gap in the comparison. Grok 3 ties for 1st among 54 models (with 25 others); Maverick ranks 44th of 54. This test covers nuanced tradeoff reasoning with real numbers — the kind of analysis required for financial modeling, competitive strategy, and policy evaluation. A 3-point gap here is significant.

• Agentic planning: 5 vs 3. Grok 3 ties for 1st among 54 models (with 14 others); Maverick ranks 42nd of 54. Agentic planning measures goal decomposition and failure recovery — critical for autonomous agents and multi-step workflows. Maverick's score puts it in the bottom quarter of tested models on this dimension.

• Long context: 5 vs 4. Grok 3 ties for 1st among 55 models (with 36 others); Maverick ranks 38th of 55. This is notable given that Maverick actually has a larger context window (1M tokens vs Grok 3's 131K). A bigger window doesn't automatically mean better retrieval — and in our 30K+ token retrieval tests, Grok 3 outperforms despite the smaller window.

• Faithfulness: 5 vs 4. Grok 3 ties for 1st among 55 models (with 32 others); Maverick ranks 34th of 55. Faithfulness measures how well a model sticks to source material without hallucinating — critical for summarization, RAG pipelines, and document Q&A.

• Multilingual: 5 vs 4. Grok 3 ties for 1st among 55 models (with 34 others); Maverick ranks 36th of 55. For non-English deployments, Grok 3 holds the edge.

• Classification: 4 vs 3. Grok 3 ties for 1st among 53 models (with 29 others); Maverick ranks 31st of 53. Routing and categorization tasks go to Grok 3.

• Structured output: 5 vs 4. Grok 3 ties for 1st among 54 models (with 24 others); Maverick ranks 26th of 54. JSON schema compliance and format adherence both favor Grok 3 — relevant for any developer building structured pipelines.

• Tool calling: 4 vs unscored. Grok 3 scored 4, ranking 18th of 54 models (tied with 28 others). Llama 4 Maverick's tool calling test hit a 429 rate limit on OpenRouter during our testing window (noted as likely transient), so no score is available for Maverick on this dimension. Do not treat this as a Maverick weakness — the test simply couldn't complete.

Where they tie:

• Constrained rewriting: 3 vs 3. Both rank 31st of 53. Neither model excels at compression within hard character limits — this is a relative weakness for both.
• Creative problem solving: 3 vs 3. Both rank 30th of 54. Tied at the median — neither distinguishes itself on generating novel, non-obvious ideas.
• Persona consistency: 5 vs 5. Both tie for 1st among 53 models (with 36 others). For chatbot personas and character maintenance, both are excellent.
• Safety calibration: 2 vs 2. Both rank 12th of 55 (tied with 19 others). Neither model stands out for calibrated refusals — both score below the field median of 2 in relative terms, though the absolute scores match.

One structural advantage Maverick holds outside our benchmark scores: it accepts image inputs (text+image->text modality), while Grok 3 is text-only. Maverick also has a 1M-token context window versus Grok 3's 131K. These are architectural differences that matter for specific use cases regardless of benchmark scores.

The cost gap here is substantial. Grok 3 is priced at $3.00/M input tokens and $15.00/M output tokens. Llama 4 Maverick runs $0.15/M input and $0.60/M output — a 20x gap on input and 25x gap on output.

At real-world volumes, those differences compound quickly:

• 1M output tokens/month: Grok 3 costs $15; Maverick costs $0.60. Difference: $14.40.
• 10M output tokens/month: Grok 3 costs $150; Maverick costs $6. Difference: $144.
• 100M output tokens/month: Grok 3 costs $1,500; Maverick costs $60. Difference: $1,440.

For individual developers or low-volume use cases, the absolute dollar gap is manageable and the quality premium from Grok 3 may well justify it. For product teams routing millions of requests per month, the $1,440/month-per-100M-tokens gap is hard to ignore. Maverick is also open-weight-style deployable through Meta's ecosystem, which matters for organizations exploring self-hosting to drive costs even lower. The decision isn't whether Grok 3 is better — in our tests it is — but whether the quality uplift is worth 25x the price at your usage level.

Choose Grok 3 if:

• You need strong agentic or multi-step planning (scored 5 vs Maverick's 3 in our tests; Maverick ranks 42nd of 54 on this dimension)
• Your work involves strategic analysis, financial modeling, or nuanced tradeoff reasoning (5 vs 2 in our testing)
• Faithfulness to source material matters — for RAG, summarization, or document Q&A (5 vs 4; Grok 3 ranks 1st, Maverick ranks 34th)
• You're building structured output pipelines that depend on reliable JSON schema compliance (5 vs 4)
• You need strong multilingual output quality (5 vs 4)
• Volume is low enough that the 25x output cost premium is acceptable — roughly under 10M tokens/month for most teams

Choose Llama 4 Maverick if:

• Your application requires image understanding — Maverick accepts image inputs; Grok 3 does not
• You're processing very long documents and need a 1M-token context window (vs Grok 3's 131K)
• You're operating at high volume where $15 vs $0.60/M output tokens matters — at 100M tokens/month, Maverick saves $1,440
• Your tasks fall in areas where both models score identically: persona consistency, creative problem solving, constrained rewriting
• You want flexibility to self-host or run inference through Meta's open ecosystem
• Budget constraints are the primary decision driver and the quality gap on your specific tasks is tolerable