DeepSeek V3.1 Terminus vs GPT-5.4

Summary of head-to-heads in our 12-test suite (scores are our 1–5 proxies unless noted):

• Wins for GPT-5.4 (in our testing): constrained_rewriting 4 vs 3 (GPT-5.4 ranks 6 of 53), tool_calling 4 vs 3 (GPT-5.4 rank 18 of 54), faithfulness 5 vs 3 (GPT-5.4 tied for 1st of 55; DeepSeek rank 52 of 55), safety_calibration 5 vs 1 (GPT-5.4 tied for 1st of 55; DeepSeek rank 32 of 55), persona_consistency 5 vs 4 (GPT-5.4 tied for 1st of 53; DeepSeek rank 38 of 53), agentic_planning 5 vs 4 (GPT-5.4 tied for 1st of 54; DeepSeek rank 16 of 54). These wins indicate GPT-5.4 is measurably stronger where refusal/safety behavior, source fidelity, function selection, and multi-step planning matter.
• Ties (neither side wins in our testing): structured_output 5/5 (both tied for 1st of 54), strategic_analysis 5/5 (tied for 1st of 54), creative_problem_solving 4/4 (both rank ~9 of 54), classification 3/3 (both mid-ranked), long_context 5/5 (both tied for 1st of 55 despite very different context windows), multilingual 5/5 (both tied for 1st of 55). For these tasks, you can expect similar outputs in our tests: both models handle long-context retrieval, structured JSON output and multilingual output at top-tier levels.
• Areas where DeepSeek does not win any benchmark in our testing: there are no aWins in the payload; DeepSeek’s relative weakness shows most in safety_calibration (1 vs GPT-5.4’s 5) and faithfulness (3 vs GPT-5.4’s 5).
• External benchmarks (supplementary): GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025, according to Epoch AI — cited here as a third-party signal that complements our internal results. DeepSeek has no external scores in the payload. Practical meaning: if your app needs strong refusal behavior and factual fidelity (e.g., medical triage, compliance workflows, or automation that calls tools), GPT-5.4’s higher safety and faithfulness scores translate to fewer hallucinations and safer agentic behavior in our tests. If you need to run very large-context transformations or produce exact JSON schemas at scale and cost matters, DeepSeek matches GPT-5.4 on structured output and long-context retrieval in our suite while being dramatically cheaper.

Costs per thousand tokens (mTok): DeepSeek V3.1 Terminus = $0.21 input + $0.79 output = $1.00 per mTok total (assuming equal input/output). GPT-5.4 = $2.50 input + $15.00 output = $17.50 per mTok total. At realistic volumes (equal in/out assumption): 1M tokens (1,000 mTok) → DeepSeek $1,000 vs GPT-5.4 $17,500. 10M tokens (10,000 mTok) → DeepSeek $10,000 vs GPT-5.4 $175,000. 100M tokens (100,000 mTok) → DeepSeek $100,000 vs GPT-5.4 $1,750,000. The price ratio in the payload is ~0.0527 (DeepSeek cost ≈ 5.27% of GPT-5.4). Teams with narrow margins or high throughput (chat apps, large-scale processing pipelines, startups with heavy token usage) should care deeply about the gap; organizations that must minimize hallucinations, meet safety requirements, or need agentic planning may justify GPT-5.4’s cost.

Choose DeepSeek V3.1 Terminus if: you must minimize API spend at scale (DeepSeek ≈ $1.00 per mTok vs GPT-5.4 $17.50 per mTok), you need top-tier long-context handling or strict structured-output (both models scored 5 in our tests), and you can accept weaker safety/fidelity. Choose GPT-5.4 if: your priority is safety calibration, faithfulness, tool calling and agentic planning (GPT-5.4 wins these in our testing), you need multimodal file/image inputs (GPT-5.4 modality includes text+image+file→text), and your budget allows the significantly higher token costs. If you need both cost efficiency and safety-critical guarantees, prototype on DeepSeek for scale and validate high-risk flows against GPT-5.4.