Gemma 4 26B A4B vs Llama 4 Maverick

Across our 12-test suite, Gemma 4 26B A4B wins 9 benchmarks, Llama 4 Maverick wins 1, and they tie on 2.

Where Gemma 4 26B A4B dominates:

• Tool calling (5 vs no score for Maverick): Maverick's tool calling test hit a 429 rate limit on OpenRouter during our testing — a transient infrastructure issue noted in the data — so direct comparison isn't possible here. Gemma scores 5/5, tied for 1st among 17 models out of 54 tested, meaning it reliably handles function selection, argument accuracy, and sequencing for agentic workflows.
• Strategic analysis (5 vs 2): This is the widest gap. Gemma scores 5/5, tied for 1st among 26 models out of 54 tested. Maverick scores 2/5, ranking 44th of 54 — well below the median of 4. For tasks requiring nuanced tradeoff reasoning with real numbers, Maverick is a poor fit.
• Structured output (5 vs 4): Gemma scores 5/5 (tied for 1st among 25 models out of 54 tested); Maverick scores 4/5 (rank 26 of 54). In practice, this matters for JSON schema compliance and format adherence in production pipelines — Gemma is more reliable.
• Faithfulness (5 vs 4): Gemma scores 5/5 (tied for 1st among 33 models out of 55 tested); Maverick scores 4/5 (rank 34 of 55). Gemma is less likely to hallucinate when staying grounded in source material.
• Long context (5 vs 4): Gemma scores 5/5 (tied for 1st among 37 models out of 55 tested); Maverick scores 4/5 (rank 38 of 55). Ironic given Maverick's much larger 1M context window — Gemma actually retrieves more accurately at 30K+ token depths in our tests.
• Multilingual (5 vs 4): Gemma scores 5/5 (tied for 1st among 35 models out of 55 tested); Maverick scores 4/5 (rank 36 of 55). Meaningful for non-English production deployments.
• Classification (4 vs 3): Gemma ranks tied for 1st (29 others, out of 53 tested); Maverick ranks 31st of 53. For routing and categorization tasks, Gemma is the better choice.
• Agentic planning (4 vs 3): Gemma ranks 16th of 54; Maverick ranks 42nd of 54 — a substantial gap for goal decomposition and failure recovery in agent architectures.
• Creative problem solving (4 vs 3): Gemma ranks 9th of 54; Maverick ranks 30th of 54.

Where Llama 4 Maverick wins:

• Safety calibration (2 vs 1): Maverick scores 2/5 (rank 12 of 55); Gemma scores 1/5 (rank 32 of 55). Both models score below the 75th percentile of 2 in this category, but Maverick is meaningfully better at refusing harmful requests while permitting legitimate ones. For applications with strict content moderation requirements, this gap is real.

Where they tie:

• Constrained rewriting (3 vs 3): Both rank 31st of 53 — middle of the pack for compression within hard character limits.
• Persona consistency (5 vs 5): Both tie for 1st among 37 models out of 53 tested. Neither has an edge for character maintenance or injection resistance.

Gemma 4 26B A4B costs $0.08/M input and $0.35/M output. Llama 4 Maverick costs $0.15/M input and $0.60/M output — roughly 1.9x more on input and 1.7x more on output. At 1M output tokens/month, that's $350 vs $600, a $250 difference. At 10M tokens/month, the gap grows to $2,500 ($3,500 vs $6,000). At 100M tokens/month — typical for production-scale API usage — you're paying $35,000 vs $60,000, a $25,000 annual difference just on output. Developers running high-volume pipelines (RAG, classification, structured extraction) should treat this cost gap seriously, especially given that Gemma 4 26B A4B outperforms Maverick on the benchmarks most relevant to those tasks. The only scenario where Maverick's pricing premium might be justified is if you specifically need its 1M-token context window, which far exceeds Gemma's 262K limit.

Choose Gemma 4 26B A4B if you're building agentic systems, structured data pipelines, RAG applications, or multilingual products — it wins on tool calling, agentic planning, structured output, faithfulness, strategic analysis, classification, long context, and multilingual quality. It also costs 42% less on output tokens, so at scale the savings are substantial. It handles text, images, and video as input modalities and supports reasoning parameters not available on Maverick.

Choose Llama 4 Maverick if your application requires a context window beyond 262K tokens (Maverick supports up to 1M) and that's a hard technical requirement, or if safety calibration is a primary concern for your use case — Maverick scores 2/5 vs Gemma's 1/5 on that dimension. Be aware that Maverick's tool calling score is unavailable from our testing due to a rate limit event, so factor that uncertainty into any agentic deployment decision.