DeepSeek V3.2 vs Gemma 4 31B

We ran both models across our 12-test suite and report scores (1–5) plus ranking displays from our pool. Summary from win/loss/tie: Gemma wins tool_calling and classification; DeepSeek wins long_context; the other nine tests tie. Detailed walk-through: - Tool calling: Gemma 5 (rank: "tied for 1st with 16 other models out of 54 tested") vs DeepSeek 3 (rank: "rank 47 of 54 (6 models share this score)"). In practice this means Gemma is measurably better at function selection, argument accuracy, and sequencing for agentic workflows. - Classification: Gemma 4 ("tied for 1st with 29 other models out of 53 tested") vs DeepSeek 3 ("rank 31 of 53 (20 models share this score)") — Gemma is more reliable for routing and categorization tasks. - Long context: DeepSeek 5 ("tied for 1st with 36 other models out of 55 tested") vs Gemma 4 ("rank 38 of 55 (17 models share this score)") — DeepSeek is stronger for retrieval and accuracy across 30K+ token contexts. - Structured output: tie 5/5 (both "tied for 1st with 24 other models out of 54 tested") — both models reliably follow JSON/schema constraints. - Strategic analysis: tie 5/5 (both "tied for 1st with 25 other models out of 54 tested") — both handle nuanced tradeoff reasoning. - Constrained rewriting: tie 4/4 (both "rank 6 of 53 (25 models share this score)") — both compress well within strict limits. - Creative problem solving: tie 4/4 (both "rank 9 of 54 (21 models share this score)") — comparable at generating feasible, non-obvious ideas. - Faithfulness: tie 5/5 (both "tied for 1st with 32 other models out of 55 tested") — both stick to source material. - Safety calibration: tie 2/2 (both "rank 12 of 55 (20 models share this score)") — similar refusal/permit behavior on risky prompts. - Persona consistency, agentic planning, multilingual: all ties at 5 where rankings show both models among the top performers. Practical meaning: choose Gemma when you need best-in-class tool calling and classification for agents and pipelines; choose DeepSeek when you prioritize maximum long-context retrieval fidelity. Most other capabilities are effectively equal in our tests.

Payload prices are input/output costs per million tokens (payload units). With a balanced 50/50 input/output split: DeepSeek V3.2 costs ~$0.32 per 1M tokens (0.50.26 + 0.50.38 = $0.13 + $0.19). Gemma 4 31B costs ~$0.255 per 1M tokens (0.50.13 + 0.50.38 = $0.065 + $0.19). At scale, that gap multiplies: for 1M tokens/month DeepSeek ≈ $0.32 vs Gemma ≈ $0.255 (save $0.065); for 10M: DeepSeek ≈ $3.20 vs Gemma ≈ $2.55 (save $0.65); for 100M: DeepSeek ≈ $32.00 vs Gemma ≈ $25.50 (save $6.50). High-volume consumers (10M+ tokens/month) will notice the difference; small-scale hobby projects will see negligible dollar impact but may value Gemma's input-cost efficiency. If your workload is output-heavy, the two models cost the same on output ($0.38 per M), so savings fall with lower input fractions.

Choose DeepSeek V3.2 if: you need top-tier long-context retrieval (DeepSeek scores 5 vs Gemma 4 and is "tied for 1st" on long_context) for document search, large transcripts, or chain-of-thought that spans 30K+ tokens. Choose Gemma 4 31B if: you build agentic systems, need reliable function/tool invocation, or require stronger classification (Gemma tool_calling 5 vs DeepSeek 3; classification 4 vs 3) and want lower input-token costs ($0.13 vs $0.26 per M). If you care mainly about schema adherence, safety calibration, faithfulness, or creative problem solving, both models perform similarly on our 12-test suite.