DeepSeek V3.1 Terminus vs DeepSeek V3.2
Winner for most use cases: DeepSeek V3.2 — it wins 5 decisive benchmarks (faithfulness, agentic planning, safety, persona consistency, constrained rewriting) while matching V3.1 on core strengths like long-context, structured output, and strategic analysis. V3.1 Terminus may still make sense for input-heavy workloads because its input cost is slightly lower ($0.21 vs $0.26 per mTOK), but it charges much more for outputs ($0.79 vs $0.38 per mTOK).
deepseek
DeepSeek V3.1 Terminus
Benchmark Scores
External Benchmarks
Pricing
Input
$0.210/MTok
Output
$0.790/MTok
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deepseek
DeepSeek V3.2
Benchmark Scores
External Benchmarks
Pricing
Input
$0.260/MTok
Output
$0.380/MTok
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Benchmark Analysis
Overview: Across our 12-test suite V3.2 wins 5 benchmarks, V3.1 wins 0, and 7 tests tie. Detailed walk-through: - Faithfulness: V3.2 5 vs V3.1 3. V3.2 ranks tied for 1st of 55 on faithfulness (tied with 32 others); V3.1 ranks 52 of 55. For tasks needing strict fidelity to sources (summaries, citation-heavy answers), V3.2 is substantially safer. - Agentic planning: V3.2 5 vs V3.1 4. V3.2 ties for 1st of 54; V3.1 ranks 16 of 54. This means V3.2 better decomposes goals and recovers from failures in agentic flows. - Safety calibration: V3.2 2 vs V3.1 1. V3.2 ranks 12 of 55 vs V3.1 at 32 of 55; V3.2 refuses harmful prompts more reliably in our tests. - Persona consistency: V3.2 5 vs V3.1 4. V3.2 ties for 1st; V3.1 is mid-ranked (38 of 53). Better for role-play or assistant persona preservation. - Constrained rewriting: V3.2 4 vs V3.1 3. V3.2 ranks 6 of 53 vs V3.1 at 31 — V3.2 is noticeably better at tight-length rewrites. Ties (no clear winner): structured_output (5/5 both; both tied for 1st), strategic_analysis (5/5 ties for 1st), creative_problem_solving (4/4 both rank 9), tool_calling (3/3 both rank 47 of 54), classification (3/3 both rank 31), long_context (5/5 both tied for 1st), multilingual (5/5 both tied for 1st). Interpretation for tasks: - If you need schema-compliant JSON, long-context retrieval, or complex reasoned tradeoffs, both match at top-tier performance. - If you need reliability to source material, multi-step agentic behavior, safer refusals, or tight-constrained rewriting, V3.2 demonstrably wins in our benchmarks. - Tool-calling is mediocre (3/5) for both in our tests; neither is a standout for complex function orchestration based on this suite.
Pricing Analysis
DeepSeek V3.1 Terminus charges $0.21 per 1k input tokens and $0.79 per 1k output tokens; DeepSeek V3.2 charges $0.26 per 1k input and $0.38 per 1k output. Price ratio (V3.1 output / V3.2 output) = 0.79 / 0.38 ≈ 2.08x. Practical examples assuming a 50/50 input/output split: 1M tokens/month costs V3.1 ≈ $500 vs V3.2 ≈ $320; 10M tokens = $5,000 vs $3,200; 100M tokens = $50,000 vs $32,000. If your workload is output-heavy (e.g., mostly model responses), V3.1 can cost $790 per 1M output tokens vs $380 for V3.2. High-volume deployers, SaaS vendors, and teams optimizing per-response cost should care — V3.2 materially reduces output spending while also winning several quality benchmarks.
Real-World Cost Comparison
Bottom Line
Choose DeepSeek V3.2 if you need higher faithfulness, stronger agentic planning, better safety calibration, and lower output costs — ideal for production assistants, retrieval-augmented generation, and agent pipelines. Choose DeepSeek V3.1 Terminus only if you have input-heavy workloads where slightly cheaper input tokens ($0.21 vs $0.26 per 1k) matter, or if an existing contract ties you to V3.1 and you specifically value the identical top-tier long-context and structured-output behavior both models share.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.