GPT-5 vs DeepSeek V3 API Pricing:
Real Cost Comparison (Apr 2026)
DeepSeek V3.2 costs up to 95% less than GPT-5 per token. But raw per-token pricing doesn't tell you what you'll actually pay each month. This guide breaks down real API costs, cache pricing, and monthly scenarios for agencies and developers choosing between the two models.
For identical token volumes, DeepSeek V3.2 costs $0.28/$0.42 per 1M input/output vs GPT-5's $1.25/$10.00. At 10,000 runs/month (2,000 input + 500 output tokens each), that's $7.70 vs $75.00 — a 10x monthly difference.
The Numbers Side by Side
These are verified list API rates as of February 2026. All prices are per 1 million tokens — the standard unit used by every major AI provider. Input and output tokens are billed separately at different rates.
⭐ DeepSeek V3.2's cache-hit rate ($0.028) activates automatically on repeated context prefixes — 10x cheaper than its standard input rate.
Real-World Monthly Cost Scenarios
These scenarios use a common agency workload: 2,000 input tokens + 500 output tokens per API call, at 10,000 runs per month. To model your own numbers, use the Burn Rate Calculator.
DeepSeek's Context Cache Advantage
DeepSeek V3.2 applies automatic context caching at $0.028/M tokens — 10x cheaper than its standard input rate — whenever it detects a repeated prefix. For RAG pipelines, document analysis, or applications reusing the same system prompt, this makes the cost gap even larger.
Benchmark Performance
DeepSeek V3.2 achieves 671B total parameters in a Mixture-of-Experts design, trained on 14.8 trillion tokens. On most text tasks, it delivers results within a few percentage points of GPT-5 at a fraction of the cost. GPT-5's clear advantages are its 400K context window and more mature multimodal and tool-use capabilities.
When to Use GPT-5 vs DeepSeek V3.2
Choose DeepSeek V3.2 when:
- Volume is high — the cost gap compounds quickly above 1M tokens/month
- Context caching applies — RAG, document analysis, and repeated system prompts benefit from the 10x cache discount
- Tasks are well-defined — summarisation, classification, data extraction, structured code generation
- You're cost-sensitive — agencies billing per-task, startups with thin margins, or projects in MVP stage
- Open weights matter — DeepSeek V3 is MIT-licensed and fully self-hostable
Choose GPT-5 when:
- 400K context is required — analysing entire codebases, legal documents, or long transcripts in one call
- Complex agentic workflows — OpenAI's function-calling and assistant API ecosystem is more mature
- Multimodal input — GPT-5 handles image, audio, and video natively
- US data residency required — enterprise SOC 2, HIPAA, or contract-mandated OpenAI SLAs
Most agencies use DeepSeek V3.2 for 80–90% of tasks and reserve GPT-5 for 10–20% that require long context, agentic orchestration, or enterprise compliance. The blended cost stays close to DeepSeek rates.
GPT-5 Batch Pricing
OpenAI's Batch API cuts GPT-5 pricing in half for asynchronous workloads: $0.625 input / $5.00 output per 1M tokens, with results returned within 24 hours. At batch rates, GPT-5's output cost drops to about 12x above DeepSeek — not 24x — which meaningfully closes the gap for overnight processing, bulk summaries, and data enrichment pipelines.