Grok Code Fast 1 vs Llama 4 Maverick

Across our 12-test suite, Grok Code Fast 1 wins 4 benchmarks outright, Llama 4 Maverick wins 1, and 7 are tied. Neither model has been assigned an aggregate bench score in our data, so this test-by-test breakdown is the primary evidence.

Agentic Planning (5 vs 3): Grok Code Fast 1's biggest differentiator. It scores 5/5 — tied for 1st among 54 models in our testing — while Maverick scores 3/5, ranking 42nd of 54. Agentic planning measures goal decomposition and failure recovery: the core capability for autonomous coding agents, multi-step workflows, and tool-use pipelines. This gap is significant.

Classification (4 vs 3): Grok Code Fast 1 scores 4/5 (tied for 1st of 53 models), Maverick scores 3/5 (rank 31 of 53). For routing, triage, or labeling tasks, Grok Code Fast 1 is meaningfully better.

Tool Calling (4 vs unscored): Grok Code Fast 1 scores 4/5 on tool calling (rank 18 of 54, tied with 28 others). Llama 4 Maverick's tool calling test hit a 429 rate limit during our testing on 2026-04-13 and was not scored — noted as likely transient, but no score is available. Grok Code Fast 1 wins this category by default with verified data.

Strategic Analysis (3 vs 2): Grok Code Fast 1 scores 3/5 (rank 36 of 54); Maverick scores 2/5 (rank 44 of 54). Neither model excels here — both fall below the median of 4/5 in our score distribution — but Grok Code Fast 1 is clearly the better option for nuanced tradeoff reasoning.

Persona Consistency (4 vs 5): Maverick's only outright win. It scores 5/5 (tied for 1st of 53 models), while Grok Code Fast 1 scores 4/5 (rank 38 of 53). For roleplay, character-based applications, or assistant personas that must resist prompt injection, Maverick has a genuine edge.

Ties (7 benchmarks): Both models score identically on structured output (4/5), constrained rewriting (3/5), creative problem solving (3/5), faithfulness (4/5), long context (4/5), safety calibration (2/5), and multilingual (4/5). The safety calibration tie at 2/5 is worth noting — both models score below the 75th percentile (2) in our distribution, meaning neither is exceptional at refusing harmful requests while permitting legitimate ones. The long context tie at 4/5 is a relative strength for both, though Maverick's 1,048,576-token context window dwarfs Grok Code Fast 1's 256,000-token window — a structural advantage for document-heavy workloads not fully captured by our 30K+ retrieval test.

One notable structural difference: Grok Code Fast 1 exposes reasoning tokens in responses (uses_reasoning_tokens: true), giving developers visibility into the model's chain-of-thought — useful for debugging agentic pipelines. Maverick does not have this listed.

Grok Code Fast 1 costs $0.20/MTok input and $1.50/MTok output. Llama 4 Maverick costs $0.15/MTok input and $0.60/MTok output — making Maverick 2.5x cheaper on output tokens, which is typically where costs accumulate.

At real-world volumes, assuming a 1:3 input-to-output token ratio:

• At 1M output tokens/month: Grok Code Fast 1 costs ~$1.50 vs Maverick's ~$0.60 — a $0.90 difference that's negligible for most teams.
• At 10M output tokens/month: $15.00 vs $6.00 — a $9 gap worth considering for growing products.
• At 100M output tokens/month: $150 vs $60 — a $90/month difference that becomes a real budget line item for high-volume APIs.

The cost gap matters most to developers running high-throughput pipelines — content generation, classification at scale, or customer-facing chat. For low-volume agentic coding assistants where quality per call matters more than per-token cost, Grok Code Fast 1's premium is easier to justify. Maverick also supports image input (text+image->text modality), which could replace a separate vision model and reduce overall costs for multimodal pipelines.

Choose Grok Code Fast 1 if: You're building agentic coding workflows, autonomous agents, or multi-step tool-use pipelines. Its 5/5 agentic planning score (tied for 1st of 54 models in our testing) and solid 4/5 tool calling make it the clear choice for orchestration-heavy tasks. The visible reasoning traces also help developers debug and steer agent behavior. It's also the better pick for classification and routing tasks at scale.

Choose Llama 4 Maverick if: Cost efficiency at high output volumes is a priority ($0.60/MTok vs $1.50/MTok output), you need image input capability (Maverick supports text+image->text, Grok Code Fast 1 does not), you require a context window larger than 256K tokens (Maverick supports up to ~1M tokens), or your use case centers on persona-consistent assistants and character applications where Maverick scores 5/5. It's also the more practical choice for general-purpose AI tasks where the quality gap doesn't justify a 2.5x output cost premium.