DeepSeek V3.1 Terminus vs Llama 4 Maverick

Overview: across our 12-test suite DeepSeek V3.1 Terminus wins 7 tests, Llama 4 Maverick wins 3, and 2 tie. Below we compare each test with scores and ranking context from our data.

Structured output — DeepSeek 5 vs Maverick 4. DeepSeek scored 5/5 for JSON/schema compliance; it is tied for 1st in structured_output: "tied for 1st with 24 other models out of 54 tested." Expect more reliable schema adherence and fewer format fixes in production.

Strategic analysis — DeepSeek 5 vs Maverick 2. DeepSeek is 5/5 and "tied for 1st with 25 other models out of 54 tested," while Maverick scores 2/5 (rank 44 of 54). DeepSeek handles nuanced tradeoffs and numeric reasoning much better in our tests.

Creative problem solving — DeepSeek 4 vs Maverick 3. DeepSeek’s 4/5 places it at rank 9 of 54 ("rank 9 of 54 (21 models share this score)"), so it produces more feasible, specific ideas for difficult prompts.

Tool calling — DeepSeek 3 vs Maverick (rate-limited in our test). DeepSeek scored 3/5 but its tool_calling ranking is low ("rank 47 of 54"), whereas Llama 4 Maverick’s tool_calling run hit a 429 rate limit on OpenRouter (payload notes tool_calling_rate_limited). In practice DeepSeek is usable but not best-in-class for complex function-selection; Maverick’s tool calling performance was not reliably measurable in our run due to the rate limit.

Long context — DeepSeek 5 vs Maverick 4. DeepSeek scored 5/5 and is "tied for 1st with 36 other models out of 55 tested," indicating strong retrieval and coherence over 30K+ token contexts. Maverick’s 4/5 sits at rank 38 of 55.

Agentic planning — DeepSeek 4 vs Maverick 3. DeepSeek’s 4/5 yields rank 16 of 54; it decomposes goals and recovery paths better in our tests. Maverick’s 3/5 ranks 42 of 54.

Multilingual — DeepSeek 5 vs Maverick 4. DeepSeek is 5/5 and "tied for 1st with 34 other models out of 55 tested," offering stronger parity across languages in our suite.

Faithfulness — Maverick 4 vs DeepSeek 3. Maverick wins here (4/5, rank 34 of 55) while DeepSeek scores 3/5 and ranks very low ("rank 52 of 55"). For applications that must avoid hallucinations, Maverick is superior in our testing.

Safety calibration — Maverick 2 vs DeepSeek 1. Maverick’s 2/5 ranks "12 of 55" while DeepSeek’s 1/5 ranks 32 of 55. Maverick refuses harmful requests and permits legitimate ones more reliably in our tests.

Persona consistency — Maverick 5 vs DeepSeek 4. Maverick is "tied for 1st with 36 other models out of 53 tested," so it better maintains character and resists injection attacks in chat-like scenarios.

Constrained rewriting — tie, both 3/5. Classification — tie, both 3/5. These tasks showed parity in our suite.

Practical interpretation: DeepSeek is the stronger generalist for long-context workflows, structured outputs, strategic analysis and agentic tasks. Llama 4 Maverick is the safer, more faithful, and more persona-consistent option and also comes at a lower per-token cost; however, Maverick’s tool-calling test was rate-limited in our run and should be re-tested for function-heavy deployments.

Pricing per mtoken from the payload: DeepSeek V3.1 Terminus input $0.21, output $0.79; Llama 4 Maverick input $0.15, output $0.60. Assuming a 50/50 split between input and output tokens (stated assumption), effective cost per mtoken = $0.50 for DeepSeek and $0.375 for Llama. At scale (1000 mtok = 1,000,000 tokens): 1M tokens/month costs ~$500 (DeepSeek) vs ~$375 (Maverick) — a $125/month difference. At 10M tokens: ~$5,000 vs ~$3,750 — $1,250/month gap. At 100M tokens: ~$50,000 vs ~$37,500 — $12,500/month gap. High-volume deployments, startups with tight margins, and products with predictable token consumption should care most about the cost gap; projects where the model’s stronger long-context and structured-output scores materially reduce engineering overhead may justify DeepSeek’s higher spend.

Choose DeepSeek V3.1 Terminus if: you need best-in-class long-context handling (5/5, tied for 1st), reliable structured output (5/5, tied for 1st), stronger strategic analysis (5/5) or better agentic planning; its higher per-token cost may be justified by fewer downstream engineering fixes. Choose Llama 4 Maverick if: cost matters (input $0.15 / output $0.60 vs DeepSeek’s $0.21 / $0.79), and you prioritize faithfulness (4/5), safety calibration (2/5) and persona consistency (5/5). If your workload is tool-heavy, re-test Maverick’s tool calling (our run hit a rate limit) before committing.