Claude Sonnet 4.6 vs DeepSeek V3.2

Overview (our 12-test suite): Claude Sonnet 4.6 wins 4 tests, DeepSeek V3.2 wins 2, and 6 tests tie. Details: - Creative problem solving: Sonnet 5 vs DeepSeek 4. Sonnet is tied for 1st on creative_problem_solving ("tied for 1st with 7 other models out of 54 tested"), so expect more non-obvious, actionable ideas in our tests. - Tool calling: Sonnet 5 vs DeepSeek 3. Sonnet is tied for 1st on tool_calling ("tied for 1st with 16 other models out of 54 tested"), while DeepSeek ranks 47/54; Sonnet is meaningfully better at function selection, argument accuracy and sequencing. - Classification: Sonnet 4 vs DeepSeek 3. Sonnet ranks tied for 1st on classification ("tied for 1st with 29 other models out of 53 tested"), so routing and labeling tasks were more accurate in our runs. - Safety calibration: Sonnet 5 vs DeepSeek 2. Sonnet is tied for 1st on safety_calibration ("tied for 1st with 4 other models out of 55 tested"), indicating clearer refusal/allow behavior on risky prompts in our tests. - Structured output: Sonnet 4 vs DeepSeek 5. DeepSeek is tied for 1st on structured_output ("tied for 1st with 24 other models out of 54 tested"), so DeepSeek produced stricter JSON/schema-compliant outputs in our schema-adherence tests. - Constrained rewriting: Sonnet 3 vs DeepSeek 4. DeepSeek ranks 6/53 on constrained_rewriting (good compression within tight character limits), while Sonnet ranks 31/53. Ties (both models scored equally): strategic_analysis (5/5, both tied for 1st), faithfulness (5/5, both tied for 1st), long_context (5/5, both tied for 1st), persona_consistency (5/5, both tied for 1st), agentic_planning (5/5, both tied for 1st), and multilingual (5/5, both tied for 1st). External benchmarks: Claude Sonnet 4.6 scores 75.2% on SWE-bench Verified and 85.8% on AIME 2025 (Epoch AI); DeepSeek V3.2 has no external SWE-bench or AIME scores in the payload. Practical meaning: Sonnet is the safer, more reliable choice for tool-driven agents, complex code navigation, and refusal-sensitive tasks; DeepSeek is stronger for strict schema outputs and tight character-budget rewriting and is far more cost-efficient for bulk inference.

Costs shown are per mTok (1 mTok = 1,000 tokens). Claude Sonnet 4.6: input $3/mTok, output $15/mTok. DeepSeek V3.2: input $0.26/mTok, output $0.38/mTok. Assuming a 50/50 split of input/output tokens, monthly cost examples: 1M tokens — Sonnet ≈ $9,000 vs DeepSeek ≈ $320; 10M tokens — Sonnet ≈ $90,000 vs DeepSeek ≈ $3,200; 100M tokens — Sonnet ≈ $900,000 vs DeepSeek ≈ $32,000. The payload’s overall priceRatio is 39.47, reflecting Sonnet’s substantially higher unit cost. Teams doing frequent high-volume inference (10M+ tokens/month) or cost-sensitive consumer deployments should care most about DeepSeek’s lower per-token price; teams needing the highest tool-calling reliability, safety calibration, and agentic workflows may justify Sonnet’s premium.

Choose Claude Sonnet 4.6 if you need best-in-class tool calling, safety calibration, creative problem solving, and higher external-code/math performance (Sonnet wins 4 of 12 benchmarks and scores 75.2% on SWE-bench Verified and 85.8% on AIME 2025). Choose DeepSeek V3.2 if you prioritize structured JSON/schema compliance and constrained rewriting (DeepSeek wins structured_output and constrained_rewriting), need long-context or persona consistency at much lower cost, or are running high-volume, cost-sensitive production.