DeepSeek V3.1 vs Llama 4 Maverick

We ran both models across our 12-test suite and report these head-to-head results from our testing. DeepSeek V3.1 wins 7 tests: structured_output (5 vs 4), strategic_analysis (4 vs 2), creative_problem_solving (5 vs 3), tool_calling (3 vs Llama’s rate-limited run), faithfulness (5 vs 4), long_context (5 vs 4), and agentic_planning (4 vs 3). Context: structured_output evaluates JSON/schema compliance — DeepSeek’s 5/5 (tied for 1st with 24 others) means it reliably follows strict formats; Llama’s 4/5 (rank 26/54) is solid but less exact. Long_context measures retrieval at 30K+ tokens — DeepSeek’s 5/5 is tied for 1st (36 others), while Llama’s 4/5 places it much lower (rank 38/55), so for very long documents DeepSeek provides noticeably better accuracy. Faithfulness (sticking to source) is 5/5 for DeepSeek (tied for 1st) vs 4/5 for Llama (rank 34/55) — expect fewer hallucinations from DeepSeek in our tests. Tool_calling saw DeepSeek at 3/5 (rank 47/54) and Llama’s tested run hit a transient 429 rate limit on OpenRouter; the payload flags Llama’s tool_calling as rate-limited, so DeepSeek performed better in our measured tool selection and argument accuracy. DeepSeek also scored 5/5 on creative_problem_solving (tied for 1st) versus 3/5 for Llama, indicating stronger non-obvious idea generation in our tasks. Llama 4 Maverick wins safety_calibration (2/5 vs DeepSeek 1/5) — in our safety benchmark Llama refused harmful prompts more appropriately while allowing legitimate ones more often (its safety rank is 12/55 vs DeepSeek rank 32/55). Four tests tie: constrained_rewriting (3/3), classification (3/3), persona_consistency (5/5 tied for 1st each), and multilingual (4/4 tied). Rankings shown are out of 52–55 models depending on the test; where DeepSeek’s scores are tied for 1st, that indicates it matches the top performers in that dimension in our suite.

DeepSeek V3.1 charges $0.75 per mtoken output (input $0.15/mtok). Llama 4 Maverick charges $0.60 per mtoken output (input $0.15/mtok). At 1M output tokens/month (1,000 mtokens): DeepSeek = $750, Llama = $600 (DeepSeek +$150). At 10M tokens: DeepSeek = $7,500, Llama = $6,000 (difference $1,500). At 100M tokens: DeepSeek = $75,000, Llama = $60,000 (difference $15,000). If your workload is output-heavy (large responses, frequent generation) the 25% output premium on DeepSeek materially increases spend at scale; enterprises and high-volume SaaS should budget accordingly. For low-volume or research use the quality upside may justify the higher per-token spend.

Choose DeepSeek V3.1 if you need: - Reliable long-context retrieval (5/5 long_context), strict schema/JSON outputs (5/5 structured_output), high faithfulness (5/5), or stronger creative problem solving (5/5). It’s the better developer/API choice when accuracy and format adherence justify a 25% higher output cost. Choose Llama 4 Maverick if you need: - Better safety calibration (2/5 vs DeepSeek 1/5), multimodal inputs (text+image->text), or a massive context_window (1,048,576 tokens) and want to reduce output spend (0.60 vs 0.75 $/mtok). Prefer Llama for applications where safety tuning and multimodality matter and cost sensitivity is high.