Gemini 2.5 Pro vs Llama 4 Maverick

Gemini 2.5 Pro outscores Llama 4 Maverick on 9 of the 12 benchmarks in our testing. Here's what that looks like test by test:

Tool Calling (5 vs no score — rate limited): Gemini 2.5 Pro scored 5/5, tied for 1st among 17 models out of 54 tested. Maverick's tool calling test hit a 429 rate limit during our testing on 2026-04-13 and could not be scored — this is noted as likely transient, but it means no data for Maverick here. For agentic workflows where function selection and argument accuracy matter, Gemini 2.5 Pro is the only comparable data point.

Creative Problem Solving (5 vs 3): Gemini 2.5 Pro scores 5/5, tied for 1st with 7 other models out of 54. Maverick scores 3/5, ranking 30th of 54. This is a meaningful gap — 5/5 means consistently non-obvious, feasible, specific ideas; 3/5 puts Maverick below the field median of 4.

Long Context (5 vs 4): Gemini 2.5 Pro scores 5/5 at retrieval accuracy over 30K+ tokens (tied for 1st, 37 models). Maverick scores 4/5 but ranks 38th of 55 — indicating its 4 is at the lower end of the 4-scoring group. Gemini 2.5 Pro's 1M-token context window is the same as Maverick's, but performance within that window is stronger in our testing.

Faithfulness (5 vs 4): Gemini 2.5 Pro scores 5/5 (tied for 1st, 33 models out of 55). Maverick scores 4/5 but ranks 34th of 55 — again on the trailing edge of the 4-tier. For RAG pipelines and summarization tasks where sticking to source material is critical, this gap matters.

Structured Output (5 vs 4): Gemini 2.5 Pro scores 5/5 (tied for 1st, 25 models out of 54). Maverick scores 4/5, ranking 26th of 54. Both support structured outputs as a parameter, but Gemini 2.5 Pro's JSON schema compliance tested higher.

Strategic Analysis (4 vs 2): This is the widest gap in the set. Gemini 2.5 Pro scores 4/5, ranking 27th of 54. Maverick scores 2/5, ranking 44th of 54 — in the bottom 20% of models tested. For business analysis, complex tradeoff reasoning, or decision support, Maverick underperforms substantially.

Agentic Planning (4 vs 3): Gemini 2.5 Pro scores 4/5 (rank 16 of 54). Maverick scores 3/5 (rank 42 of 54). Goal decomposition and failure recovery both favor Gemini 2.5 Pro.

Classification (4 vs 3): Gemini 2.5 Pro scores 4/5, tied for 1st among 30 models out of 53. Maverick scores 3/5, ranking 31st of 53. For routing and categorization tasks, the gap is real.

Multilingual (5 vs 4): Gemini 2.5 Pro scores 5/5 (tied for 1st, 35 models out of 55). Maverick scores 4/5 (rank 36 of 55). Both handle non-English tasks well, but Gemini 2.5 Pro tested at peak quality.

Safety Calibration (1 vs 2): Maverick's sole win. Gemini 2.5 Pro scores 1/5, ranking 32nd of 55 — below the field median of 2. Maverick scores 2/5, ranking 12th of 55. Neither model is a standout on refusing harmful requests while permitting legitimate ones, but Maverick is measurably better here.

Constrained Rewriting and Persona Consistency: Both models tie at 3/5 and 5/5 respectively. Both hold persona and resist injection at peak quality (tied for 1st, 37 models). Neither excels at compression within hard character limits.

External Benchmarks (Epoch AI): On SWE-bench Verified, Gemini 2.5 Pro scores 57.6% — ranking 10th of 12 models with external scores in our dataset, below the field median of 70.8%. Llama 4 Maverick has no SWE-bench Verified score in our data. On AIME 2025, Gemini 2.5 Pro scores 84.2% — ranking 11th of 23 models, above the 50th percentile of 83.9% for that group. These external scores (sourced from Epoch AI, CC BY) suggest Gemini 2.5 Pro is competitive on competition math but not a top-tier coding model by real GitHub issue resolution. No external benchmark scores are available for Maverick in our data.

Gemini 2.5 Pro costs $1.25/Mtok input and $10/Mtok output. Llama 4 Maverick costs $0.15/Mtok input and $0.60/Mtok output. On output tokens alone — typically the dominant cost driver — the gap is 16.7x. At 1M output tokens/month, Gemini 2.5 Pro costs $10 vs Maverick's $0.60. Scale that to 10M tokens and you're paying $100 vs $6. At 100M tokens — a realistic volume for a production API app — the difference is $1,000 vs $60 per month, just on output. Input costs follow a similar ratio: $1.25 vs $0.15 per Mtok. For developers running batch jobs, inference pipelines, or any throughput-heavy application, Maverick's pricing is genuinely compelling. For users who need peak performance on complex reasoning, coding, or agentic tasks, the premium for Gemini 2.5 Pro is harder to avoid. Consumer subscribers and occasional users will feel the quality gap more than the price difference; high-volume API teams will feel both.

Choose Gemini 2.5 Pro if you need strong performance across reasoning, coding pipelines, agentic workflows, long-document analysis, or multilingual tasks — particularly where faithfulness to source material and tool use reliability matter. Its 5/5 scores on tool calling, creative problem solving, long context, faithfulness, structured output, persona consistency, and multilingual make it the higher-ceiling model by our benchmarks. Accept the $10/Mtok output cost as the price of that capability. Choose Llama 4 Maverick if cost is the primary constraint and your workload involves tasks where it scored competitively: persona-consistent chatbots, basic structured output, or applications requiring tighter safety calibration. At $0.60/Mtok output, Maverick is 16.7x cheaper — which means it can deliver adequate results at a fraction of the budget for classification, templated generation, or conversational interfaces. Do not use Maverick for strategic analysis (2/5, rank 44 of 54), complex agentic tasks (rank 42 of 54), or any workload demanding deep reasoning — the benchmark gaps are too large to ignore.