OpenAI's closed-source flagship against Meta's open-weight contender. We break down where each model excels across eight key dimensions, then invite you to run your own test with LLMWise Compare mode.
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| Dimension | GPT-5.2 | Llama 4 Maverick | Edge |
|---|---|---|---|
| Coding | GPT-5.2 produces polished, well-documented code with excellent function calling and structured output support, making it a strong choice for agentic coding workflows. | Llama 4 Maverick writes solid code across mainstream languages and benefits from a vibrant community of fine-tuned coding variants, though it trails GPT-5.2 on complex multi-file tasks. | |
| Creative Writing | GPT-5.2 leads in creative writing with natural tonal variety, engaging storytelling, and a polished voice that adapts to any genre or register. | Llama 4 Maverick handles creative tasks competently but its output tends to be less distinctive and more formulaic, especially on long-form narrative. | |
| Math & Reasoning | GPT-5.2 is a capable reasoner on multi-step math and logic problems, though it occasionally struggles with competition-level challenges. | Llama 4 Maverick shows strong chain-of-thought reasoning and performs well on graduate-level math, closing much of the gap with closed-source models. | tie |
| Speed | GPT-5.2 delivers consistent low-latency responses backed by OpenAI's highly optimized serving infrastructure across global regions. | Llama 4 Maverick uses a mixture-of-experts architecture for efficient inference, and speed varies significantly depending on the hosting provider and quantization level. | |
| Cost | GPT-5.2 sits at premium pricing that scales quickly for high-volume workloads, with limited options for reducing per-token costs outside of batch APIs. | Llama 4 Maverick is dramatically cheaper via API providers and can be self-hosted at near-zero marginal cost, making it one of the most affordable frontier-adjacent models available. | |
| Context Window | GPT-5.2 supports a large context window with strong recall, though retrieval accuracy can dip in the middle of very long inputs. | Llama 4 Maverick supports a competitive context window and handles long inputs well, though recall accuracy varies more across hosting providers and quantization settings. | |
| Safety | GPT-5.2 has mature, well-tested safety filters and content policies refined over several years of public deployment and red-teaming. | Llama 4 Maverick ships with Meta's safety guardrails, but open-weight distribution means end users can remove or modify safety layers, a double-edged sword for compliance-sensitive deployments. | |
| Open Source Flexibility | GPT-5.2 is fully closed-source with no option to self-host, fine-tune on your own infrastructure, or inspect model weights. | Llama 4 Maverick is open-weight with a permissive license, enabling self-hosting, custom fine-tuning, quantization, and full control over the inference stack. |
GPT-5.2 wins on raw output quality, creative writing, coding polish, speed, and safety maturity. Llama 4 Maverick wins decisively on cost and open-source flexibility, giving teams full control over fine-tuning, deployment, and data privacy. For quality-critical production workloads, GPT-5.2 is the safer pick. For teams that need customization, on-premises deployment, or dramatic cost savings, Llama 4 Maverick is the smarter investment.
Use LLMWise Compare mode to test both models on your own prompts in one API call.
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Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.
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