GPT-5.5 wins on coding (96 vs 58) and writing quality and context window (1M vs 256K). Llama 4 Maverick wins on price ($0.6 vs $5/1M input). For most workflows, GPT-5.5 is the stronger default — best openai flagship for agentic coding, research, and computer-use work.
The safest GPT-5.5 vs Llama 4 Maverick default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.5 is the strongest answer here for GPT-5.5 vs Llama 4 Maverick — pick it when quality of output matters more than the $5.00/1M/1M input you pay for it.
Llama 4 Maverick handles the same job for about 94% 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 GPT-5.5 vs Llama 4 Maverick — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.5 leads on coding with a score of 96 vs 58 for Llama 4 Maverick.
GPT-5.5 has the larger context window: 1M vs 256K for Llama 4 Maverick.
Llama 4 Maverick is cheaper at $0.6/1M input tokens vs $5/1M for GPT-5.5.
Go with GPT-5.5 if you want one model to handle coding and research — it targets agentic coding, computer-use workflows, and complex research tasks.
Llama 4 Maverick earns its place when your work is mostly flexible self-hosted deployments and mixed general workloads, even though it loses the overall count here.
Llama 4 Maverick is the more cost-efficient option at $0.6/1M input — GPT-5.5 costs 8x 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 GPT-5.5 vs Llama 4 Maverick answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Premium / Sep 3, 2026
Best OpenAI flagship for agentic coding, research, and computer-use work.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You only care about the highest public coding benchmark score or need a cheaper high-volume model.
Where the GPT-5.5 vs Llama 4 Maverick recommendation shifts once you weigh price or latency differently.
Best OpenAI flagship for agentic coding, research, and computer-use work.
Best flexible option for teams that need open-weight portability.
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-5.5OpenAI | $5.00/1M | $30.00/1M | $110 | 1M tokens | Balanced | 96 | 92 | 94 |
| 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 GPT-5.5 vs Llama 4 Maverick, what it is genuinely good at, and where we would steer you away from it.
Ranked first here for GPT-5.5 vs Llama 4 Maverick: 96/100 on coding, with the widest margin of anything in this line-up.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
You only care about the highest public coding benchmark score or need a cheaper high-volume model.
The strongest OpenAI pick for agentic coding and knowledge work. Claude Opus 4.7 still wins on the public SWE-Bench Pro coding number, but GPT-5.5 is the better OpenAI default when ecosystem, Codex, or computer-use workflows matter.
Full pricing, benchmark table and release notes on the GPT-5.5 page.
The cost-conscious pick for GPT-5.5 vs Llama 4 Maverick, about 94% less per token than GPT-5.5 than the top choice while holding 58/100 on coding.
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-5.5 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-5.5 is aimed at agentic coding and computer-use workflows, 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-5.5 costs $5/1M input and $30/1M output.
GPT-5.5 has the larger context window at 1M tokens vs Llama 4 Maverick's 256K. For large document analysis, GPT-5.5 is the stronger pick.
GPT-5.5 is better for coding with a score of 96 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-5.5's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-5.5 latency penalty is usually invisible.
Claude Opus 4.7 leads GPT-5.5 on SWE-Bench Pro for pure coding ceiling. Premium API pricing makes it less attractive for high-volume low-risk work. Avoid it if you only care about the highest public coding benchmark score or need a cheaper high-volume model. 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-5.5 specifically, the gap shows up most on coding (96 vs 58).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-5.5 runs $110.00 (at $5/1M in and $30/1M out); Llama 4 Maverick runs $9.20 (at $0.6/1M in and $1.6/1M out). That is a $100.80/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-5.5 for agentic coding and computer-use workflows, 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-5.5 for the hard cases is usually the cheapest arrangement that does not cost you quality.