Gemini 2.5 Pro vs Llama 4 Scout

Across our 12-test benchmark suite, Gemini 2.5 Pro outscores Llama 4 Scout on 8 tests, ties on 3, and loses 1.

Where Gemini 2.5 Pro leads:

• Creative problem solving: 5 vs 3. Gemini 2.5 Pro ties for 1st among 8 models in our suite; Scout ranks 30th of 54. This measures non-obvious, feasible idea generation — a significant gap for any brainstorming or ideation use case.
• Agentic planning: 4 vs 2. Gemini 2.5 Pro ranks 16th of 54; Scout ranks 53rd of 54 — near the bottom of all tested models. For goal decomposition and multi-step task recovery, Scout is a poor fit.
• Strategic analysis: 4 vs 2. Gemini 2.5 Pro ranks 27th of 54; Scout ranks 44th of 54. The gap reflects Scout's weakness on nuanced tradeoff reasoning with real numbers.
• Tool calling: 5 vs 4. Both are competitive here, but Gemini 2.5 Pro ties for 1st among 17 models; Scout ranks 18th of 54 among 29 tied models. For function-calling accuracy and argument sequencing in agentic workflows, Gemini 2.5 Pro has a meaningful edge.
• Faithfulness: 5 vs 4. Gemini 2.5 Pro ties for 1st among 33 models; Scout ranks 34th of 55. This measures sticking to source material without hallucinating — relevant for RAG and summarization pipelines.
• Persona consistency: 5 vs 3. Gemini 2.5 Pro ties for 1st among 37 models; Scout ranks 45th of 53. A large gap for chatbot and roleplay applications requiring character stability.
• Multilingual: 5 vs 4. Both score well, but Gemini 2.5 Pro ties for 1st among 35 models; Scout ranks 36th of 55.
• Structured output: 5 vs 4. Gemini 2.5 Pro ties for 1st among 25 models; Scout ranks 26th of 54. Both are capable for JSON schema tasks, but Gemini 2.5 Pro has a slight edge.

Where Llama 4 Scout wins:

• Safety calibration: 2 vs 1. Scout ranks 12th of 55; Gemini 2.5 Pro ranks 32nd of 55. Gemini 2.5 Pro's score of 1 places it in the bottom quarter of all tested models on refusing harmful requests while permitting legitimate ones — a notable weakness.

Ties:

• Constrained rewriting: Both score 3, both rank 31st of 53. Neither model excels at compression within hard character limits.
• Classification: Both score 4, both tied for 1st among 30 models of 53 tested. Either is a strong choice for routing and categorization.
• Long context: Both score 5, both tied for 1st among 37 models of 55 tested. Both handle retrieval at 30K+ tokens equally well.

External benchmarks (Epoch AI): Gemini 2.5 Pro scores 84.2% on AIME 2025 (rank 11 of 23 models with scores) and 57.6% on SWE-bench Verified (rank 10 of 12). These place it in the upper-middle tier on math olympiad problems and below the median on real GitHub issue resolution among models with available scores. Llama 4 Scout has no reported external benchmark scores in our data.

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens. Llama 4 Scout costs $0.08 per million input tokens and $0.30 per million output tokens. At 1M output tokens per month, you pay $10.00 for Gemini 2.5 Pro versus $0.30 for Scout — a $9.70 difference that is barely noticeable. Scale to 10M output tokens and the gap becomes $97 per month. At 100M output tokens monthly — typical for a consumer app or high-throughput pipeline — Gemini 2.5 Pro costs $1,000 versus Scout's $30, a $970/month difference. For input-heavy workloads (retrieval, document analysis), the input ratio of $1.25 vs $0.08 (15.6×) also compounds quickly. Cost-sensitive developers building classification pipelines, document routing, or chat applications at scale should default to Scout. Teams building agentic systems, coding assistants, or multi-step reasoning workflows will likely find Gemini 2.5 Pro's performance gap justifies the premium.

Choose Gemini 2.5 Pro if:

• You are building agentic or multi-step workflows — Scout scored near-last on agentic planning (rank 53 of 54) in our testing.
• Your application depends on persona stability, chatbot consistency, or resisting prompt injection (5 vs 3 in our tests).
• You need strong tool calling and faithfulness for RAG pipelines or function-calling agents.
• You are running moderate token volumes (under 10M output tokens/month) where the cost premium stays manageable.
• You process audio, video, or files — Gemini 2.5 Pro supports text, image, file, audio, and video inputs; Scout is text and image only.
• Advanced math reasoning matters: Gemini 2.5 Pro scores 84.2% on AIME 2025 (Epoch AI).

Choose Llama 4 Scout if:

• You are running high-volume pipelines (100M+ output tokens/month) where the $0.30 vs $10.00 per million output token difference saves thousands of dollars monthly.
• Your task is classification, document routing, or long-context retrieval — Scout ties Gemini 2.5 Pro on all three in our testing.
• Safety calibration is a priority — Scout scores 2 vs Gemini 2.5 Pro's 1 in our tests, ranking 12th vs 32nd of 55 models.
• You need a cost-efficient baseline for workloads that don't require deep reasoning or agentic capability.