Gemma 4 31B vs Llama 4 Maverick

Across the 11 benchmarks where both models received scores in our testing, Gemma 4 31B wins 9, and the remaining 2 are ties. Llama 4 Maverick wins none.

Where Gemma 4 31B dominates:

• Strategic analysis (5 vs 2): This is the widest gap in the comparison. Gemma 4 31B scores 5/5 (tied for 1st among 54 models) while Llama 4 Maverick scores 2/5 (rank 44 of 54). For tasks requiring nuanced tradeoff reasoning with real numbers — financial analysis, product decisions, risk assessment — Llama 4 Maverick is a significant step down.
• Tool calling (5 vs no score): Gemma 4 31B scores 5/5 and ties for 1st among 54 models on function selection, argument accuracy, and sequencing. Llama 4 Maverick's tool calling test hit a 429 rate limit during our testing (noted as likely transient), so we have no comparable score. Developers building agentic workflows should treat this as an unresolved data point for Maverick.
• Agentic planning (5 vs 3): Gemma 4 31B ties for 1st among 54 models; Llama 4 Maverick ranks 42nd of 54. For multi-step task execution and failure recovery, Gemma 4 31B is substantially stronger in our testing.
• Faithfulness (5 vs 4): Gemma 4 31B ties for 1st among 55 models on sticking to source material without hallucinating. Llama 4 Maverick scores 4/5 but ranks 34th of 55 — a notable drop for RAG and summarization tasks where accuracy to source matters.
• Structured output (5 vs 4): Gemma 4 31B ties for 1st among 54 models on JSON schema compliance. Llama 4 Maverick scores 4/5 at rank 26 of 54 — serviceable but not top-tier.
• Multilingual (5 vs 4): Gemma 4 31B ties for 1st among 55 models. Llama 4 Maverick scores 4/5 at rank 36 of 55, which sits below the field median for this test.
• Classification (4 vs 3): Gemma 4 31B ties for 1st among 53 models. Llama 4 Maverick ranks 31st of 53 — mid-field performance on routing and categorization.
• Creative problem solving (4 vs 3): Gemma 4 31B ranks 9th of 54; Llama 4 Maverick ranks 30th of 54.
• Constrained rewriting (4 vs 3): Gemma 4 31B ranks 6th of 53; Llama 4 Maverick ranks 31st of 53.

Where they tie:

• Long context (4 vs 4): Both models score 4/5 and share the same rank (38 of 55). Gemma 4 31B's 256K window handles this test equivalently — the practical gap only emerges for inputs above 256K tokens, where only Llama 4 Maverick's 1M window can help.
• Safety calibration (2 vs 2): Both score 2/5, ranking 12th of 55 — below the field median (p25 is 1, p50 is 2). Neither model distinguishes itself here.
• Persona consistency (5 vs 5): Both tie for 1st among 53 models. Character maintenance and injection resistance are equivalent.

Note on tool calling: The payload flags that Llama 4 Maverick's tool calling test hit a 429 rate limit on OpenRouter during our testing session (2026-04-13), described as likely transient. We have no tool calling score for Llama 4 Maverick as a result. This does not mean it fails at tool calling — only that we lack data.

Gemma 4 31B costs $0.13/MTok input and $0.38/MTok output. Llama 4 Maverick costs $0.15/MTok input and $0.60/MTok output. The output gap is the one that matters at scale: at 1M output tokens/month, you pay $0.38 vs $0.60 — a $0.22 difference that barely registers. At 10M output tokens, the gap is $2,200/month. At 100M output tokens, you're saving $22,000/month by choosing Gemma 4 31B. For high-volume production workloads — document processing pipelines, customer-facing chat, classification at scale — that difference is material. For prototyping or low-volume use, both models are inexpensive enough that cost shouldn't be the deciding factor. The meaningful question is whether Llama 4 Maverick's 1M context window is worth the premium; for most applications, it isn't, since Gemma 4 31B's 256K window handles the vast majority of real-world documents and conversations.

Choose Gemma 4 31B if:

• You're building agentic or tool-calling pipelines (scores 5/5, ties for 1st; Maverick has no comparable score in our testing)
• Your application requires strong strategic analysis or nuanced reasoning (5 vs 2 — the single largest gap in this comparison)
• You need reliable JSON schema compliance and structured outputs in production
• You work with multilingual content at scale (5 vs 4, Maverick ranks below median)
• You're running high-volume workloads and want to save ~37% on output costs
• Your documents fit within 256K tokens (the vast majority do)

Choose Llama 4 Maverick if:

• You have a hard requirement for context windows above 256K tokens — Maverick's 1M context window is a genuine structural advantage that Gemma 4 31B cannot match
• You want Meta's open-weights ecosystem and deployment flexibility (check licensing terms directly)
• You're experimenting with very long document processing (books, large codebases, extended conversations) where the 4x context advantage is the binding constraint

For the majority of production use cases — APIs, chat, classification, RAG, agents — Gemma 4 31B is the stronger and cheaper choice based on our benchmark data.