DeepSeek V3.2 vs Llama 4 Maverick

Overview: In our 12-test suite DeepSeek V3.2 wins 9 tests, Llama 4 Maverick wins 0, and the two tie on 3 tests (classification, safety_calibration, persona_consistency). Detailed walk-through (score shown as DeepSeek → Llama):

• structured_output: 5 → 4 — DeepSeek ties for 1st on structured output (tied for 1st with 24 others of 54 tested). This means DeepSeek is more reliable at producing exact JSON/schema-adherent outputs in our tests.
• strategic_analysis: 5 → 2 — DeepSeek is tied for 1st (tied for 1st with 25 others of 54) while Llama ranks 44 of 54; this gap shows DeepSeek handles nuanced tradeoff reasoning and numeric tradeoffs much better in our benchmarks.
• constrained_rewriting: 4 → 3 — DeepSeek ranks 6 of 53 in constrained rewriting; better for tight-character edits and adherence to strict limits.
• creative_problem_solving: 4 → 3 — DeepSeek ranks 9 of 54 vs Llama at 30 of 54; in our tests DeepSeek produced more feasible, specific creative solutions.
• tool_calling: 3 → (Llama lower / rate-limited) — DeepSeek wins this test in our comparison, but note DeepSeek’s tool_calling rank is 47 of 54 (so while it beat Llama here, neither model is a top performer for tool orchestration in the broader field). Llama encountered a transient rate limit on OpenRouter during our tool_calling run (tool_calling_rate_limited: true).
• faithfulness: 5 → 4 — DeepSeek ties for 1st (tied for 1st with 32 others of 55); it more reliably sticks to source material in our tests.
• long_context: 5 → 4 — DeepSeek tied for 1st (tied for 1st with 36 others of 55) despite Llama having a far larger raw context_window (1,048,576 tokens vs DeepSeek’s 163,840). In practice this means DeepSeek produced more accurate retrievals at 30K+ token probes in our suite, though Llama’s huge window may be useful for specific streaming/multi-file workloads.
• agentic_planning: 5 → 3 — DeepSeek tied for 1st (tied for 1st with 14 others of 54); it beats Llama on goal decomposition and failure-recovery tasks in our tests.
• multilingual: 5 → 4 — DeepSeek ties for 1st (tied for 1st with 34 others of 55); expect stronger non‑English parity in our tests.
• classification, safety_calibration, persona_consistency: ties — both models scored the same on these tests in our runs (classification 3/3; safety_calibration 2/2; persona_consistency 5/5). Implication for real tasks: DeepSeek consistently outperformed Llama on reasoning, structured outputs, long-context retrieval, multilingual output, and agentic planning in our benchmarks. Llama’s payload advantages: multimodal input (text+image→text), a massive context_window (1,048,576) and a cheaper input mTok price — these matter for image understanding, extremely large-window streaming, or input-heavy workflows. Note: Llama hit a tool_calling rate limit in our OpenRouter runs, which affected that test's behavior.

Per-mTok prices from the payload: DeepSeek V3.2 charges $0.26 input / $0.38 output; Llama 4 Maverick charges $0.15 input / $0.60 output. If your usage is a 50/50 split of input vs output tokens, cost per million total tokens (1M tokens = 1,000 mTok; 50% input / 50% output): DeepSeek = $320 per 1M tokens (0.5M in → $130; 0.5M out → $190). Llama 4 Maverick = $375 per 1M tokens (0.5M in → $75; 0.5M out → $300). Scaling to volume: at 10M tokens/month (50/50) DeepSeek ≈ $3,200 vs Llama ≈ $3,750; at 100M tokens/month DeepSeek ≈ $32,000 vs Llama ≈ $37,500. If your workload is input-heavy (far more input than output), Llama’s $0.15 input mTok is materially cheaper; if output-heavy (or balanced), DeepSeek’s lower output price ($0.38 vs $0.60) and lower combined cost favor DeepSeek. Enterprises at 10M+ tokens/month should care about the ~$550 per million-token gap in the balanced scenario; at 100M tokens the difference becomes >$5,000/month.

Choose DeepSeek V3.2 if: you need top-tier reasoning, faithful outputs, reliable structured/JSON output, long-context accuracy at 30K+ tokens, agentic planning, or multilingual parity — and you want lower combined token cost for balanced or output-heavy workloads. Choose Llama 4 Maverick if: you require multimodal image→text capabilities, an enormous context window (1,048,576 tokens) or you run input-heavy pipelines where Llama’s $0.15 input mTok reduces cost. Also factor in the transient tool_calling rate-limit we observed for Llama in our OpenRouter test.