Gemini 3.1 Pro Preview vs Llama 4 Maverick

Across our 12-test internal suite, Gemini 3.1 Pro Preview outscores Llama 4 Maverick on 9 dimensions, with Llama 4 Maverick winning 1 and the two tying on 2.

Where Gemini 3.1 Pro Preview wins clearly:

• Agentic planning: 5 vs 3. Gemini ties for 1st among 54 models; Maverick ranks 42nd of 54. For multi-step autonomous tasks — goal decomposition, failure recovery — this is a significant gap that will surface in real agentic workflows.
• Strategic analysis: 5 vs 2. Gemini ties for 1st among 54 models; Maverick ranks 44th of 54. Nuanced tradeoff reasoning with real numbers is a Gemini strength and a Maverick weakness.
• Creative problem solving: 5 vs 3. Gemini ties for 1st among 54 models; Maverick ranks 30th of 54. Generating non-obvious, feasible ideas is firmly in Gemini's column.
• Long context: 5 vs 4. Both above median (p50 = 5), but Gemini ties for 1st among 55 models while Maverick ranks 38th of 55. At 30K+ token retrieval tasks, Gemini is more reliable.
• Faithfulness: 5 vs 4. Gemini ties for 1st among 55 models; Maverick ranks 34th. Sticking to source material without hallucinating matters for RAG and summarization pipelines.
• Multilingual: 5 vs 4. Both above the field median (p50 = 5), but Gemini ties for 1st while Maverick ranks 36th of 55.
• Structured output: 5 vs 4. Gemini ties for 1st among 54 models; Maverick ranks 26th. JSON schema compliance is solid on both, but Gemini is more consistent.
• Constrained rewriting: 4 vs 3. Gemini ranks 6th of 53; Maverick ranks 31st. Compressing text within hard character limits favors Gemini.
• Tool calling: 4 vs N/A (Maverick's tool calling test hit a 429 rate limit on our testing date — noted as likely transient). Gemini scored 4, ranking 18th of 54. Maverick's tool calling score is unavailable from our testing, so no direct comparison can be made here.

Where Llama 4 Maverick wins:

• Classification: 3 vs 2. Maverick ranks 31st of 53; Gemini ranks 51st of 53. For routing and categorization tasks, Maverick is the better choice — and Gemini's score here is notably weak, sitting near the bottom of the field.

Ties:

• Safety calibration: Both score 2, both rank 12th of 55 (20 models share this score). Neither model excels here relative to the field.
• Persona consistency: Both score 5, both tied for 1st among 53 models. Maintaining character under adversarial prompting is equally strong on both.

External benchmark (AIME 2025, Epoch AI): Gemini 3.1 Pro Preview scores 95.6% on AIME 2025, ranking 2nd of 23 models tracked by Epoch AI — placing it at the very top of math olympiad performance. No AIME 2025 score is available for Llama 4 Maverick in our data payload. This is a strong signal for Gemini's reasoning depth on quantitative tasks.

Gemini 3.1 Pro Preview costs $2.00/M input tokens and $12.00/M output tokens. Llama 4 Maverick costs $0.15/M input and $0.60/M output — a 13x gap on input and 20x gap on output. At 1M output tokens/month, you're paying $12 vs $0.60. At 10M output tokens, that's $120 vs $6. At 100M output tokens — a realistic volume for a production API consumer — the difference is $1,200 vs $60 per month, a $1,140 gap that compounds fast. Developers running high-volume classification, summarization, or chat pipelines where Llama 4 Maverick's scores are sufficient should think hard before paying the Gemini premium. The calculus flips for lower-volume, high-stakes tasks: agentic workflows, complex analysis, or long-document retrieval where Gemini 3.1 Pro Preview's benchmark advantages translate directly to fewer errors and retries.

Choose Gemini 3.1 Pro Preview if:

• You're building agentic systems that require reliable goal decomposition and failure recovery (scored 5 vs 3, ranks 1st vs 42nd of 54 in our tests)
• Your work involves strategic analysis, complex reasoning, or competition-level math (95.6% on AIME 2025 per Epoch AI — rank 2 of 23)
• You need strong faithfulness in RAG or summarization pipelines (5 vs 4, ranks 1st vs 34th of 55)
• You handle long documents and require accurate retrieval at 30K+ tokens (5 vs 4, ranks 1st vs 38th of 55)
• Cost is secondary to output quality and you're running low-to-moderate token volumes where the $1,140/month gap at 100M tokens is acceptable

Choose Llama 4 Maverick if:

• Classification and routing are your primary workload — it outscores Gemini here (3 vs 2, and Gemini ranks near the bottom of the field at 51st of 53)
• You're running high-volume inference where the 20x output cost difference ($0.60 vs $12 per million tokens) makes Gemini 3.1 Pro Preview economically untenable
• Your use case doesn't require deep agentic planning or strategic reasoning, where Maverick's scores lag significantly
• You accept a smaller max output window (16,384 tokens vs 65,536) and don't need audio/video input modalities that Gemini 3.1 Pro Preview supports
• You want access to advanced sampling parameters (min_p, top_k, repetition_penalty, logit_bias, frequency/presence penalty) that Maverick supports but Gemini does not