DeepSeek V3.1 Terminus vs Gemma 4 31B

Test-by-test outcomes (our 11-task comparison): • Wins for DeepSeek V3.1 Terminus: long_context — DeepSeek 5 vs Gemma 4. DeepSeek ranks tied for 1st of 55 on long_context, while Gemma ranks 38 of 55; this means DeepSeek is the safer choice for retrieval/summarization over 30K+ tokens. • Wins for Gemma 4 31B: constrained_rewriting 4 vs 3 (Gemma rank 6 of 53), tool_calling 5 vs 3 (Gemma tied for 1st of 54; DeepSeek rank 47 of 54), faithfulness 5 vs 3 (Gemma tied for 1st of 55; DeepSeek rank 52 of 55), classification 4 vs 3 (Gemma tied for 1st of 53), safety_calibration 2 vs 1 (Gemma rank 12 of 55), persona_consistency 5 vs 4 (Gemma tied for 1st), and agentic_planning 5 vs 4 (Gemma tied for 1st). These wins show Gemma is stronger for function selection/argument accuracy, resisting hallucination, routing/classification, agentic workflows and safer responses. • Ties: structured_output (both 5/5, tied for 1st), strategic_analysis (both 5/5), creative_problem_solving (both 4/5), multilingual (both 5/5). Structured output parity means both models handle schema-compliant JSON equally well in our tests. • Practical meaning: pick Gemma for tool-enabled agents, classification pipelines, production chatbots requiring faithfulness and safety; pick DeepSeek for extremely long-context document tasks and any place you need its 163,840-token window and top-ranked long-context retrieval.

Using the per-mtok prices in the payload (input+output): DeepSeek V3.1 Terminus totals $0.21 + $0.79 = $1.00 per mtok; Gemma 4 31B totals $0.13 + $0.38 = $0.51 per mtok. That price ratio (DeepSeek ≈ 2.08× Gemma) scales linearly: assuming 1 mtoken = 1,000 tokens, a 50/50 input-output token mix costs: • 1M tokens (1,000 mtoks): DeepSeek ≈ $1,000 vs Gemma ≈ $510. • 10M tokens: DeepSeek ≈ $10,000 vs Gemma ≈ $5,100. • 100M tokens: DeepSeek ≈ $100,000 vs Gemma ≈ $51,000. Who should care: high-volume deployments (millions of tokens/month), especially generation-heavy apps where output cost dominates, will see large absolute savings with Gemma. Teams that need DeepSeek’s maximum context (163,840 tokens) or its specific behavior may accept the premium; otherwise Gemma gives better benchmark coverage per dollar.

Choose Gemma 4 31B if you need: production-grade tool calling, high faithfulness (5/5), classification, persona consistency, agentic planning, or a lower cost per token — Gemma wins 7 of 11 benchmarks and costs $0.51/mtok vs $1.00/mtok. Choose DeepSeek V3.1 Terminus if you need: maximal long-context retrieval and strict structured-output handling (long_context 5/5, structured_output 5/5) and you can justify paying ~2.08× more for that capability.