Claude Sonnet 4.6 vs Llama 4 Maverick

Across our 12-test suite, Claude Sonnet 4.6 wins 9 categories, ties 3, and Llama 4 Maverick wins 0 (payload win/tie data). Key per-test comparisons (score — ranking):

• Strategic analysis: Sonnet 5 (ranked tied for 1st of 54), Maverick 2 (rank 44 of 54). This means Sonnet handles nuanced trade-off reasoning with real numbers far better in our tests.
• Creative problem solving: Sonnet 5 (tied for 1st of 54) vs Maverick 3 (rank 30); Sonnet generates more non-obvious, feasible ideas in our prompts.
• Tool calling: Sonnet 5 (tied for 1st of 54); Maverick’s tool_calling test was rate-limited on OpenRouter (quirk flagged), so Maverick’s result is not comparable here — Sonnet demonstrated reliable function selection/argument accuracy in our runs.
• Faithfulness: Sonnet 5 (tied for 1st of 55) vs Maverick 4 (rank 34); Sonnet sticks to source material better in our tests.
• Classification: Sonnet 4 (tied for 1st of 53) vs Maverick 3 (rank 31); Sonnet is more accurate for routing/categorization tasks.
• Long context: Sonnet 5 (tied for 1st of 55) vs Maverick 4 (rank 38); Sonnet preserves retrieval accuracy at 30K+ tokens in our benchmarks. Sonnet also reports a 1,000,000 token context window vs Maverick’s 1,048,576 — Sonnet’s config in the payload indicates massive long-context support and a max_output_tokens of 128,000 (Maverick 16,384).
• Safety calibration: Sonnet 5 (tied for 1st of 55) vs Maverick 2 (rank 12); Sonnet more reliably refuses harmful prompts while allowing legitimate requests in our tests.
• Agentic planning: Sonnet 5 (tied for 1st of 54) vs Maverick 3 (rank 42); Sonnet decomposes goals and plans recovery better in our scenarios.
• Multilingual: Sonnet 5 (tied for 1st of 55) vs Maverick 4 (rank 36); Sonnet produced higher-quality non-English outputs in our runs. Ties: structured_output both 4 (rank 26 of 54), constrained_rewriting both 3 (rank 31 of 53), persona_consistency both 5 (tied for 1st of 53). External benchmarks (Epoch AI): Sonnet scores 75.2% on SWE-bench Verified (rank 4 of 12) and 85.8% on AIME 2025 (rank 10 of 23); Maverick has no SWE-bench / AIME external scores in this payload. These external numbers supplement our internal results and help explain Sonnet’s edge on code and math-related tasks.

Prices in the payload are per mTok: Claude Sonnet 4.6 input $3 and output $15 per mTok; Llama 4 Maverick input $0.15 and output $0.6 per mTok. Assuming a 1:1 split of input and output tokens, monthly costs are: for 1M tokens (1,000 mTok) — Sonnet $18,000 vs Maverick $750 (gap $17,250); for 10M tokens (10,000 mTok) — Sonnet $180,000 vs Maverick $7,500 (gap $172,500); for 100M tokens (100,000 mTok) — Sonnet $1,800,000 vs Maverick $75,000 (gap $1,725,000). The 25× output cost ratio dominates operating expense: teams with heavy volume (≥10M tokens/month) or thin margins should prefer Llama 4 Maverick; teams that require fewer tokens but need the highest capability (complex code orchestration, long-context work, stricter safety) may justify Sonnet's premium.

Choose Claude Sonnet 4.6 if you need highest capability for complex code orchestration, reliable tool-calling, long-context reasoning, strict safety calibration, or multilingual and agentic workflows — and you can absorb higher runtime cost. Choose Llama 4 Maverick if your priority is cost efficiency at scale (Sonnet costs 25× more on output: $15 vs $0.6 per mTok), you have constrained token budgets (tens of millions/month), or you only need solid persona consistency and structured output at a much lower price.