Which model is better: Gemini 3.1 Pro or Llama 4 Maverick?
Llama 4 Maverick is 84.0% cheaper for input tokens ($0.20 vs. $1.25 per 1M tokens) and $0.60 vs. $10.00 for output tokens (6.2x cost advantage). In terms of operational performance, Llama 4 Maverick delivers lower response latency at 135ms TTFT (285ms faster than Gemini 3.1 Pro) with 175 vs. 98 tok/s throughput. Gemini 3.1 Pro leads coding evaluations at 71.8% SWE-bench Verified vs. 62.4% for Llama 4 Maverick and 82.4% vs. 73.8% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
Gemini 3.1 Pro
Llama 4 Maverick
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$0.20
Llama 4 Maverick is 6.3x cheaper
Output Cost / 1M Tokens
$10.00
$0.60
Llama 4 Maverick (94.0% lower)
Prompt Cache Read / 1M
$0.310
$0.050
Llama 4 Maverick is 6.2x cheaper
Prompt Cache Write / 1M
$1.25
$0.20
Llama 4 Maverick is 6.3x cheaper
Context Window (Max Output)
1,049k (66k out)
1,049k (33k out)
Equal Context Window
Avg TTFT Response Latency
420ms
135ms
Llama 4 Maverick (285ms faster)
Generation Speed (Throughput)
98 tok/s
175 tok/s
Llama 4 Maverick (+77 tok/s faster)
SWE-bench Verified (Coding)
71.8%
62.4%
Gemini 3.1 Pro (+9.4% lead)
GPQA Diamond (PhD STEM)
82.4%
73.8%
Gemini 3.1 Pro (+8.6% lead)
MMLU-Pro (Multi-Discipline)
89.5%
84.5%
Gemini 3.1 Pro (+5.0% lead)
MATH-500 (Quantitative Logic)
96.8%
91%
Gemini 3.1 Pro (+5.8% lead)
Inference Value Score (IVR)
60 / 100
77 / 100
Llama 4 Maverick (+17 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M cached reads).
Which is cheaper: Gemini 3.1 Pro or Llama 4 Maverick?
Llama 4 Maverick is cheaper at $0.20/1M input tokens ($0.60/1M output) compared to $1.25/1M input ($10.00/1M output) for Gemini 3.1 Pro.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.1 Pro or Llama 4 Maverick?
Llama 4 Maverick delivers faster first-token latency at 135ms TTFT (175 tok/s throughput) compared to 420ms TTFT (98 tok/s) for Gemini 3.1 Pro.
How do Gemini 3.1 Pro and Llama 4 Maverick compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 62.4% for Llama 4 Maverick. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 73.8% for Llama 4 Maverick (and 96.8% vs 91% on MATH-500).
When should you choose Gemini 3.1 Pro?
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M cached reads).
When should you choose Llama 4 Maverick?
Choose Llama 4 Maverick (Meta) for high-throughput open-weight multimodal assistants, enterprise RAG, and low-cost cloud inference on Groq and Cerebras — offering a 1,048k context window, 135ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
