GPT-5.2 wins on coding (85 vs 58) and writing quality. Llama 4 Maverick wins on price ($0.6 vs $1.75/1M input). For most workflows, GPT-5.2 is the stronger default — capable but outclassed — gpt-5.4 is now cheaper and better.
The safest GPT-5.2 vs Llama 4 Maverick default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.2 is the strongest answer here for GPT-5.2 vs Llama 4 Maverick — pick it when quality of output matters more than the $1.75/1M/1M input you pay for it.
Llama 4 Maverick handles the same job for about 86% 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-5.2 vs Llama 4 Maverick — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.2 leads on coding with a score of 85 vs 58 for Llama 4 Maverick.
Llama 4 Maverick has the larger context window: 256K vs 200K for GPT-5.2.
Llama 4 Maverick is cheaper at $0.6/1M input tokens vs $1.75/1M for GPT-5.2.
Go with GPT-5.2 if you want one model to handle coding and research — it targets serious coding and complex product work.
Choose Llama 4 Maverick when your work is mostly flexible self-hosted deployments and mixed general workloads — that is the workload it was tuned for.
Llama 4 Maverick is the more cost-efficient option at $0.6/1M input — GPT-5.2 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 GPT-5.2 vs Llama 4 Maverick answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Premium / Sep 3, 2026
Capable but outclassed — GPT-5.4 is now cheaper and better.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You're starting a new project — GPT-5.4 is cheaper and more capable.
Where the GPT-5.2 vs Llama 4 Maverick recommendation shifts once you weigh price or latency differently.
Capable but outclassed — GPT-5.4 is now cheaper and better.
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-5.2OpenAI | $1.75/1M | $14.00/1M | $46 | 200k tokens | Balanced | 85 | 82 | 84 |
| 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-5.2 vs Llama 4 Maverick, what it is genuinely good at, and where we would steer you away from it.
The default answer for GPT-5.2 vs Llama 4 Maverick — 85/100 on the coding axis, and the model we would start with unless the price below rules it out.
Reliable OpenAI flagship for serious coding and product work — a strong default before GPT-5.4 was released.
You're starting a new project — GPT-5.4 is cheaper and more capable.
Still capable but GPT-5.4 at $2.50/1M input is now cheaper and better. Hard to justify GPT-5.2 at $12/1M for new projects.
Full pricing, benchmark table and release notes on the GPT-5.2 page.
The value option for GPT-5.2 vs Llama 4 Maverick: about 86% less per token than GPT-5.2, at 58/100 on coding. 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.
Newsletter
We email when the GPT-5.2 vs Llama 4 Maverick pick changes, when one of these models moves on price, or when something new displaces the current leader.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
GPT-5.2 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-5.2 is aimed at serious coding and complex product work, 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-5.2 costs $1.75/1M input and $14/1M output.
Llama 4 Maverick has the larger context window at 256K tokens vs GPT-5.2's 200K. For large document analysis, Llama 4 Maverick is the stronger pick.
GPT-5.2 is better for coding with a score of 85 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-5.2's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-5.2 latency penalty is usually invisible.
Superseded by GPT-5.4 for most use cases. Claude Sonnet 4.6 leads on both coding and writing quality. Avoid it if you're starting a new project — GPT-5.4 is cheaper and more capable. 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-5.2 specifically, the gap shows up most on coding (85 vs 58).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-5.2 runs $45.50 (at $1.75/1M in and $14/1M out); Llama 4 Maverick runs $9.20 (at $0.6/1M in and $1.6/1M out). That is a $36.30/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-5.2 for serious coding and complex product work, 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-5.2 for the hard cases is usually the cheapest arrangement that does not cost you quality.