Claude Opus 4.6 vs Llama 4 Maverick

Across our 12-test suite Claude Opus 4.6 wins 8 tasks, Llama 4 Maverick wins none, and four are ties. Head-to-head highlights from our testing: - Strategic analysis: Opus 4.6 scores 5/5 vs Llama 4 Maverick 2/5 — Opus ranks tied for 1st (tied with 25 others of 54) while Maverick is rank 44 of 54. This matters for nuanced tradeoff reasoning and numeric decision-making. - Creative problem solving: 5/5 (Opus) vs 3/5 (Maverick); Opus is tied for 1st and will produce more non-obvious, executable ideas. - Agentic planning: 5/5 vs 3/5; Opus is tied for 1st (better at goal decomposition/failure recovery). - Tool calling: Opus 5/5 (tied for 1st); Maverick’s tool_calling hit a transient 429 on OpenRouter and has no successful score recorded here — in our testing Opus reliably selected functions and arguments. - Long context: Opus 5/5 (tied for 1st) vs Maverick 4/5 (rank 38 of 55) — Opus performs better on tasks requiring retrieval at 30k+ tokens. - Faithfulness: Opus 5/5 (tied for 1st) vs Maverick 4/5 — fewer hallucinations in our tests. - Safety calibration: Opus 5/5 (tied for 1st) vs Maverick 2/5 (rank 12 of 55) — Opus refused harmful prompts more consistently while allowing legitimate ones. Ties: structured_output 4/5 each, constrained_rewriting 3/5 each, classification 3/5 each, persona_consistency 5/5 each. External benchmarks: beyond our internal scores, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) and ranks 1 of 12 on that external test; it also scores 94.4% on AIME 2025 in our reported results (rank 4 of 23). Llama 4 Maverick has no external SWE-bench or AIME scores in the payload. In practice, Opus’s 5/5 wins indicate stronger performance for coding, multi-step agents, long documents, and safety-critical flows; Maverick delivers comparable persona and structured-output behavior at much lower cost but with weaker planning, strategy, and long-context performance.

Raw price points from the payload: Claude Opus 4.6 input $5 / mTok and output $25 / mTok; Llama 4 Maverick input $0.15 / mTok and output $0.60 / mTok. Treating mTok as 1,000 tokens (standard convention), combined input+output cost per 1k tokens is $30.00 for Opus 4.6 and $0.75 for Llama 4 Maverick (priceRatio ≈ 41.67). At 1M tokens/month (1,000 * 1k): Opus 4.6 ≈ $30,000; Llama 4 Maverick ≈ $750. At 10M tokens/month: Opus ≈ $300,000; Llama ≈ $7,500. At 100M tokens/month: Opus ≈ $3,000,000; Llama ≈ $75,000. Who should care: any high-volume deployment, product with tight margins, or prototyping team — the Opus→Maverick gap is economically decisive. If your application needs Opus-level wins (see benchmarks) but you expect millions of tokens, plan for substantially higher infrastructure costs or reserved/enterprise pricing conversations; if cost per token dominates, Llama 4 Maverick is the clear practical choice.

Choose Claude Opus 4.6 if you need best-in-class performance for coding, agentic workflows, long-context retrieval, safety-calibrated responses, or you require top results on SWE-bench Verified (78.7% per Epoch AI) and can absorb substantially higher token costs. Choose Llama 4 Maverick if budget or token volume is the dominant constraint and you need solid persona consistency and structured-output parity at vastly lower cost (Opus ≈ $30,000 vs Maverick ≈ $750 at 1M tokens/month). If you require a middle path, prototype on Llama 4 Maverick and move critical, high-value tasks to Claude Opus 4.6 where performance justifies the cost.