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.
Pick GPT-5.4 for coding and research. Pick Llama 4 Maverick when flexible self-hosted deployments and mixed general workloads.
Best for agentic automation and desktop control workflows.
Use GPT-5.4 if you want the strongest default. Switch only when cost, speed, or context length matters more than maximum reliability.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GPT-5.4 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Grok 4 is the lower-cost option to start with when you still need useful output at scale.
Llama 4 Maverick is the better pick when response speed matters more than maximum 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.
Choose GPT-5.4 for coding and research — agentic workflows.
Choose Llama 4 Maverick when flexible self-hosted deployments and mixed general workloads.
Llama 4 Maverick is the more cost-efficient option at $0.6/1M — worth considering if token volume is a concern.
This comparison focuses on the models most likely to answer this search intent well, not every model in the directory.
Best flexible option for teams that need open-weight portability.
Best for agentic automation and desktop control workflows.
Use these cards as the practical decision layer: what each leading option is good at, and when it becomes the wrong default.
Best flexible option for teams that need open-weight portability.
Flexible self-hosted deployments and mixed general workloads
You want the strongest hosted answer quality — closed frontier models win on benchmarks.
Best for agentic automation and desktop control workflows.
Agentic workflows, desktop automation, and complex multi-step reasoning
You need the highest coding benchmark scores — Claude Opus 4.6 and Sonnet 4.6 lead SWE-bench.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Benchmark scores from SWE-bench (coding), ARC-AGI-2 (reasoning), and MMLU (knowledge breadth) — cross-referenced against Chatbot Arena community votes to filter out cherry-picked provider claims.
Input and output costs verified directly against each provider's official API pricing page. Updated whenever a price change is detected. Value-per-dollar is weighted separately from raw benchmark rank.
Advertised context sizes are noted but scored against real-world usability — models that degrade significantly at large contexts are penalised even if the window is technically available.
Production signals matter more than lab scores. We weight Cursor and Windsurf defaults, HackerNews sentiment, developer surveys, and which models teams actually keep using after the honeymoon period.
One-off wins on cherry-picked benchmarks don't move our rankings. We favour models that stay dependable across repeated prompts, diverse task types, and long sessions without degrading.
Time-to-first-token and output throughput from Artificial Analysis speed benchmarks. Latency is categorised from Very fast to Deliberate — relevant when building interactive or high-throughput products.
Data sources
The fastest way to see where the recommendation shifts when your priority changes.
Best flexible option for teams that need open-weight portability.
Best for agentic automation and desktop control workflows.
Only frontier model that can control a desktop via API (click, type, navigate)
Strong at multi-step agentic tasks and autonomous workflows
Competitive coding performance with 74.9% SWE-bench score
Claude Opus 4.6 and Sonnet 4.6 outperform it on pure coding benchmarks
Smaller context window (272K) vs Gemini 3.1 Pro (2M) for research
Newsletter
Useful if you care about ranking shifts, pricing changes, or a better recommendation appearing in this decision path.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
GPT-5.4 wins on more categories — coding, research, reasoning. Llama 4 Maverick is the better pick when flexible self-hosted deployments and mixed general workloads. The right choice depends on your specific use case.
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. For the highest coding quality available, Claude Sonnet 4.6 (79.6% SWE-bench) or Opus 4.6 (80.8%) remain benchmarks.
Llama 4 Maverick is faster with a fast speed rating (score: 4) vs GPT-5.4's balanced rating (score: 3).
