Llama 4 Maverick wins on price ($0.6 vs $2.5/1M input). GPT-5.4 wins on coding (90 vs 58) and writing quality. For most workflows, GPT-5.4 is the stronger default — best for agentic automation and desktop control workflows.
The safest Llama 4 Maverick vs GPT-5.4 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.4 is the strongest answer here for Llama 4 Maverick vs GPT-5.4 — pick it when quality of output matters more than the $2.50/1M/1M input you pay for it.
Llama 4 Maverick handles the same job for about 87% 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 GPT-5.4 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.4 leads on coding with a score of 90 vs 58 for Llama 4 Maverick.
GPT-5.4 has the larger context window: 272K vs 256K for Llama 4 Maverick.
Llama 4 Maverick is cheaper at $0.6/1M input tokens vs $2.5/1M for GPT-5.4.
Go with GPT-5.4 if you want one model to handle coding and research — it targets agentic workflows, desktop automation, and complex multi-step reasoning.
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 — GPT-5.4 costs 4x 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 GPT-5.4 answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Premium / Sep 3, 2026
Best for agentic automation and desktop control workflows.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need the highest current coding benchmark scores — Claude Opus 4.7 and GPT-5.5 are newer premium picks.
Where the Llama 4 Maverick vs GPT-5.4 recommendation shifts once you weigh price or latency differently.
Best flexible option for teams that need open-weight portability.
Best for agentic automation and desktop control workflows.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-5.4OpenAI | $2.50/1M | $15.00/1M | $55 | 272k tokens | Balanced | 90 | 88 | 88 |
| 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 GPT-5.4, what it is genuinely good at, and where we would steer you away from it.
The default answer for Llama 4 Maverick vs GPT-5.4 — 90/100 on the coding axis, and the model we would start with unless the price below rules it out.
OpenAI's latest flagship with unique desktop-control capabilities — it can see your screen, click, and navigate apps via the API.
You need the highest current coding benchmark scores — Claude Opus 4.7 and GPT-5.5 are newer premium picks.
Best choice when you need a model that can operate software autonomously at the older GPT-5.4 price tier. For current premium coding quality, Claude Opus 4.7 leads.
Full pricing, benchmark table and release notes on the GPT-5.4 page.
Where most budgets should land for Llama 4 Maverick vs GPT-5.4 — about 87% less per token than GPT-5.4, and still 58/100 on the coding axis.
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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GPT-5.4 wins on more of the categories we score — coding, research, 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": GPT-5.4 is aimed at agentic workflows and desktop automation, 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. GPT-5.4 costs $2.5/1M input and $15/1M output.
GPT-5.4 has the larger context window at 272K tokens vs Llama 4 Maverick's 256K. For large document analysis, GPT-5.4 is the stronger pick.
GPT-5.4 is better for coding with a score of 90 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 GPT-5.4's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-5.4 latency penalty is usually invisible.
Claude Opus 4.7 and GPT-5.5 now outperform it on current premium coding benchmarks. Smaller context window (272K) vs Gemini 3.1 Pro (2M) for research. Avoid it if you need the highest current coding benchmark scores — Claude Opus 4.7 and GPT-5.5 are newer premium picks. 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 GPT-5.4 specifically, the gap shows up most on coding (90 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); GPT-5.4 runs $55.00 (at $2.5/1M in and $15/1M out). That is a $45.80/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 GPT-5.4 for agentic workflows and desktop automation, 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 GPT-5.4 for the hard cases is usually the cheapest arrangement that does not cost you quality.