Llama 4 Maverick wins on price ($0.6 vs $2/1M input). Gemini 3.1 Pro wins on coding (80 vs 58) and writing quality and context window (2M vs 256K). For most workflows, Gemini 3.1 Pro is the stronger default — best for research and deep document analysis — 2m context at the best premium price.
The safest Llama 4 Maverick vs Gemini 3.1 Pro default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Gemini 3.1 Pro is the strongest answer here for Llama 4 Maverick vs Gemini 3.1 Pro — pick it when quality of output matters more than the $2.00/1M/1M input you pay for it.
Llama 4 Maverick handles the same job for about 84% less per token. Start here and only move up if the output is not good enough.
Llama 4 Maverick is the fastest of these for Llama 4 Maverick vs Gemini 3.1 Pro — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Gemini 3.1 Pro leads on coding with a score of 80 vs 58 for Llama 4 Maverick.
Gemini 3.1 Pro has the larger context window: 2M vs 256K for Llama 4 Maverick.
Llama 4 Maverick is cheaper at $0.6/1M input tokens vs $2/1M for Gemini 3.1 Pro.
Choose Gemini 3.1 Pro for research, deep document analysis, and long-context reasoning at competitive pricing. Its research and long context scores are what carry the recommendation here.
Switch to Llama 4 Maverick when your work is mostly flexible self-hosted deployments and mixed general workloads; on that narrower brief it is the better tool.
Llama 4 Maverick is the more cost-efficient option at $0.6/1M input — Gemini 3.1 Pro costs 3x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the Llama 4 Maverick vs Gemini 3.1 Pro answer changes when cost, speed, or long-document depth leads the decision.
Google / Premium / Sep 2, 2026
Best for research and deep document analysis — 2M context at the best premium price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your primary use case is writing quality or agentic coding — Claude wins both.
Where the Llama 4 Maverick vs Gemini 3.1 Pro recommendation shifts once you weigh price or latency differently.
Best flexible option for teams that need open-weight portability.
Best for research and deep document analysis — 2M context at the best premium price.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Gemini 3.1 ProGoogle | $2.00/1M | $12.00/1M | $44 | 2M tokens | Balanced | 80 | 82 | 99 |
| Llama 4 MaverickMeta | $0.60/1M | $1.60/1M | $9.20 | 256k tokens | Fast | 58 | 66 | 64 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for Llama 4 Maverick vs Gemini 3.1 Pro, what it is genuinely good at, and where we would steer you away from it.
Our pick for Llama 4 Maverick vs Gemini 3.1 Pro. It scores 99/100 on the research axis we weight this page by, and nothing else in this shortlist matches it on output quality.
Google's flagship with the largest context window of any frontier model at 2M tokens, Deep Think reasoning, and the best price-to-performance among premium models.
Your primary use case is writing quality or agentic coding — Claude wins both.
The best research and long-context model available. Handles entire codebases, legal documents, and large datasets in a single pass — at a lower price than GPT-5.4 or Claude Sonnet 4.6.
Full pricing, benchmark table and release notes on the Gemini 3.1 Pro page.
The value option for Llama 4 Maverick vs Gemini 3.1 Pro: about 84% less per token than Gemini 3.1 Pro, at 64/100 on research. Worth starting here and moving up only if the output disappoints.
Flexible open-weight model for teams that want control, portability, and solid general-purpose performance.
You want the strongest hosted answer quality — closed frontier models win on benchmarks.
Best when infrastructure control and open-weight flexibility matter more than absolute peak quality.
Full pricing, benchmark table and release notes on the Llama 4 Maverick page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Gemini 3.1 Pro wins on more of the categories we score — research, long context, reasoning — so it is the better default of the two. Llama 4 Maverick is the better pick when your work is mostly flexible self-hosted deployments and mixed general workloads. Neither is universally "better": Gemini 3.1 Pro is aimed at research and deep document analysis, Llama 4 Maverick at flexible self-hosted deployments and mixed general workloads.
Llama 4 Maverick is cheaper at $0.6/1M input and $1.6/1M output. Gemini 3.1 Pro costs $2/1M input and $12/1M output.
Gemini 3.1 Pro has the larger context window at 2M tokens vs Llama 4 Maverick's 256K. For large document analysis, Gemini 3.1 Pro is the stronger pick.
Gemini 3.1 Pro is better for coding with a score of 80 vs Llama 4 Maverick's 58 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Llama 4 Maverick is faster with a fast speed rating (score: 4) vs Gemini 3.1 Pro's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the Gemini 3.1 Pro latency penalty is usually invisible.
Slower than Flash for everyday lightweight tasks. Claude Sonnet 4.6 is better for writing quality. Avoid it if your primary use case is writing quality or agentic coding — Claude wins both. That is the main case for looking at Llama 4 Maverick instead.
Quality depends heavily on deployment setup and hardware. No significant lead over hosted models in any single benchmark category. Avoid it if you want the strongest hosted answer quality — closed frontier models win on benchmarks. Against Gemini 3.1 Pro specifically, the gap shows up most on coding (80 vs 58).
Take a moderate workload of 10M input and 2M output tokens a month. Llama 4 Maverick runs $9.20 (at $0.6/1M in and $1.6/1M out); Gemini 3.1 Pro runs $44.00 (at $2/1M in and $12/1M out). That is a $34.80/month difference — Llama 4 Maverick is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is Gemini 3.1 Pro for research and deep document analysis, with Llama 4 Maverick handling flexible self-hosted deployments and mixed general workloads. Routing high-volume, low-stakes calls to Llama 4 Maverick at $0.6/1M and reserving Gemini 3.1 Pro for the hard cases is usually the cheapest arrangement that does not cost you quality.