GPT-5.1 vs Llama 4 Maverick

GPT-5.1 wins 9 of 12 benchmarks against Llama 4 Maverick in our 12-test suite, ties 3, and loses none.

Where GPT-5.1 dominates:

• Strategic analysis: GPT-5.1 scores 5/5 (tied for 1st among 54 models) vs Llama 4 Maverick's 2/5 (rank 44 of 54). This is the largest gap in the comparison — for nuanced tradeoff reasoning with real numbers, Maverick is near the bottom of the field while GPT-5.1 is at the top.
• Faithfulness: GPT-5.1 scores 5/5 (tied for 1st among 55 models) vs Maverick's 4/5 (rank 34 of 55). For retrieval-augmented generation and document-grounded tasks, GPT-5.1 hallucinates less in our testing.
• Agentic planning: GPT-5.1 scores 4/5 (rank 16 of 54) vs Maverick's 3/5 (rank 42 of 54). Goal decomposition and failure recovery are meaningfully worse in Maverick — relevant for any multi-step autonomous workflow.
• Multilingual: GPT-5.1 5/5 (tied for 1st among 55) vs Maverick 4/5 (rank 36 of 55). For non-English use cases, GPT-5.1 is a tier above.
• Creative problem solving: GPT-5.1 4/5 (rank 9 of 54) vs Maverick 3/5 (rank 30 of 54).
• Classification: GPT-5.1 4/5 (tied for 1st among 53) vs Maverick 3/5 (rank 31 of 53).
• Constrained rewriting: GPT-5.1 4/5 (rank 6 of 53) vs Maverick 3/5 (rank 31 of 53).
• Long context: GPT-5.1 5/5 (tied for 1st among 55) vs Maverick 4/5 (rank 38 of 55). At 30K+ token retrieval tasks, the gap is real.
• Tool calling: GPT-5.1 4/5 (rank 18 of 54) vs Maverick — note that Maverick's tool calling test hit a 429 rate limit on our testing run (flagged as likely transient), so we have no valid score to compare. GPT-5.1's 4/5 stands uncontested here.

Where models tie:

• Structured output (JSON schema compliance): Both score 4/5, both rank 26 of 54. Identical performance.
• Safety calibration: Both score 2/5, both rank 12 of 55. Neither model excels at balancing refusals — this is a shared weakness, and both sit below the field median of 2 only marginally.
• Persona consistency: Both score 5/5, tied for 1st among 53 models. No difference for chatbot or character-maintenance use cases.

External benchmarks (Epoch AI data): GPT-5.1 scores 68% on SWE-bench Verified (rank 7 of 12 models with this score in our dataset) and 88.6% on AIME 2025 (rank 7 of 23). These place it above the field medians of 70.8% and 83.9% respectively — a competent coding and math model, though not the top performer on these third-party measures. No external benchmark scores are available for Llama 4 Maverick in our dataset.

GPT-5.1 costs $1.25/M input and $10/M output tokens. Llama 4 Maverick costs $0.15/M input and $0.60/M output. At 1M output tokens/month, that's $10 vs $0.60 — a $9.40 difference that's negligible for most teams. At 10M output tokens/month, the gap widens to $94, still manageable for most businesses. At 100M output tokens/month — the scale of a production consumer app — GPT-5.1 costs $1,000 vs Llama 4 Maverick's $60, a $940/month delta that demands justification. Developers building high-volume pipelines (summarization, classification, routing) should run the numbers carefully: Llama 4 Maverick's 4/5 on structured output matches GPT-5.1 exactly, meaning for pure JSON extraction workloads you may be paying 16.7x for no measurable gain. GPT-5.1's premium is most defensible for agentic, analytical, or long-context tasks where it scores meaningfully higher.

Choose GPT-5.1 if: You're building agentic systems, RAG pipelines, or analytical tools where faithfulness (5 vs 4), strategic analysis (5 vs 2), and agentic planning (4 vs 3) matter. It's also the clear choice for multilingual applications, long-context retrieval, and any task requiring reliable tool calling. At $10/M output tokens, the cost is real — but for quality-critical or complex tasks, GPT-5.1's across-the-board advantage is substantial.

Choose Llama 4 Maverick if: You're running high-volume, cost-sensitive workloads where structured output or persona consistency are your primary requirements — both models tie on these. At $0.60/M output tokens (16.7x cheaper), Maverick's matching performance on JSON extraction and character consistency makes it the rational choice for classification pipelines, chatbot scaffolding, or any scenario where strategic depth and faithfulness are not critical. Its 1M-token context window (vs GPT-5.1's 400K) is also a technical advantage worth noting for very long document ingestion — though in our long-context benchmark testing, GPT-5.1 outscored it 5 vs 4.