Gemini 3.1 Pro Preview vs Llama 4 Scout

Across our 12-test internal benchmark suite, Gemini 3.1 Pro Preview outscores Llama 4 Scout on 8 tests, ties on 3, and loses on 1.

Where Gemini 3.1 Pro Preview wins clearly:

• Agentic planning: 5 vs 2. Gemini ranks tied for 1st among 54 models; Scout ranks 53rd of 54. This is the widest gap in the suite and matters enormously for any multi-step automated workflow — poor agentic planning means fragile pipelines that fail to recover from errors.
• Strategic analysis: 5 vs 2. Gemini ties for 1st of 54; Scout ranks 44th. For nuanced tradeoff reasoning with real numbers, Scout is near the bottom of models we've tested.
• Creative problem solving: 5 vs 3. Gemini ties for 1st of 54; Scout ranks 30th. Non-obvious, feasible ideas are a Gemini strength.
• Faithfulness: 5 vs 4. Gemini ties for 1st of 55; Scout ranks 34th. Gemini is less likely to hallucinate details beyond its source material.
• Persona consistency: 5 vs 3. Gemini ties for 1st of 53; Scout ranks 45th. For chatbot and character-based applications, Scout's score signals meaningful drift risk.
• Structured output: 5 vs 4. Both clear the bar, but Gemini ties for 1st of 54 vs Scout's rank 26.
• Multilingual: 5 vs 4. Gemini ties for 1st of 55; Scout ranks 36th of 55.
• Constrained rewriting: 4 vs 3. Gemini ranks 6th of 53; Scout ranks 31st.

Where they tie:

• Tool calling: Both score 4/5, both rank 18th of 54 with 29 models sharing the score. Neither has an edge for function-calling pipelines.
• Long context: Both score 5/5, both tied for 1st of 55 with 36 models sharing the score. Both handle 30K+ token retrieval equally well in our testing, though Gemini's context window (1,048,576 tokens) is over 3x larger than Scout's (327,680 tokens).
• Safety calibration: Both score 2/5, both rank 12th of 55. Neither model is particularly well-calibrated on the refusal/permit balance in our tests.

Where Llama 4 Scout wins:

• Classification: 4 vs 2. Scout ties for 1st of 53; Gemini ranks 51st of 53. For routing and categorization tasks, Scout is dramatically better in our testing — Gemini's classification score is among the worst we've measured.

External benchmarks: On AIME 2025 (Epoch AI), Gemini 3.1 Pro Preview scores 95.6%, ranking 2nd of 23 models tested — an elite result for competition-level mathematics. Llama 4 Scout has no AIME 2025 score in our data payload.

The cost difference here is not marginal — it is structural. Gemini 3.1 Pro Preview costs $2.00 per million input tokens and $12.00 per million output tokens. Llama 4 Scout costs $0.08 input and $0.30 output per million tokens. At 1 million output tokens per month, you pay $12 for Gemini vs $0.30 for Scout — a $11.70 difference. At 10 million output tokens, that gap is $117. At 100 million output tokens, it is $1,170 per month just on output alone. For developers running high-frequency classification, retrieval, or chat pipelines where Gemini's quality advantages don't apply, Llama 4 Scout is the economically rational choice. For agentic workflows, multi-step reasoning, or customer-facing applications where failure recovery and strategic analysis matter, Gemini 3.1 Pro Preview's quality lead is likely worth the premium — but teams should run volume projections before committing.

Choose Gemini 3.1 Pro Preview if you are building agentic pipelines, autonomous workflows, or multi-step reasoning systems — its score of 5/5 on agentic planning (vs Scout's 2/5, ranked 53rd of 54) is a red-line differentiator. Also choose it for strategic analysis tasks, creative work requiring non-obvious outputs, applications needing faithful source adherence, persona-driven chatbots, multilingual deployments, or any context requiring more than 327K tokens. Its 95.6% on AIME 2025 (Epoch AI) also makes it a strong candidate for math-heavy workflows. Accept the $2/$12 per million token cost as the price of reliability.

Choose Llama 4 Scout if your primary workload is classification, routing, or categorization — it ties for 1st of 53 on that benchmark while Gemini ranks 51st. Scout is also the rational choice for high-volume applications where the quality gaps in agentic planning or strategic analysis don't apply to your task, and the 40x output cost savings ($0.30 vs $12 per million tokens) compound significantly at scale. It accepts text and image inputs, handles up to 327K context tokens, and ties Gemini on tool calling and long-context retrieval.