Gemini 3.1 Pro 2.5M Token vs DeepSeek V4 GA Open-Source: Cheapest LLM API for APAC Enterprise AI Inference Cost 2026
Two major LLM events landed within the same news cycle: Google's Gemini 3.1 Pro broke the 2.5 million-token context window record and claimed the title of best cost-performance reasoning model on Google Cloud, while DeepSeek V4 reached General Availability (GA) with fully open-source weights and inference pricing that is setting a new floor for the market. For APAC enterprises running AI workloads on AWS, GCP, Alibaba Cloud, or self-hosted GPU clusters, the decision between these two models now directly affects your monthly cloud bill.
This article gives you a structured, data-grounded comparison of both models—context window, pricing, open-source flexibility, and APAC deployment fit—so you can make a defensible budget decision today.
Model Snapshot: What Just Changed
Gemini 3.1 Pro — Context Record + Cost-Performance Crown
- Context window: 2.5 million tokens (largest commercially available window as of this writing)
- Positioning: Google's stated "best cost-performance inference model" in the Gemini family
- Availability: Google AI Studio, Vertex AI (us-central1, asia-southeast1, asia-northeast1)
- Use case fit: Long-document RAG, code review across massive repos, multi-turn enterprise agents, compliance audit summarisation
DeepSeek V4 GA — Open-Source + Record-Low Pricing
- Status: General Availability; open-source weights publicly released
- Pricing: GA launch pricing confirmed at a new market low (see table below)
- Availability: DeepSeek API, self-hosted on H100/A100, available via third-party APAC inference providers
- Use case fit: Cost-sensitive batch inference, coding automation, enterprises wanting full model ownership and data residency control
Head-to-Head Cost & Capability Comparison
| Dimension | Gemini 3.1 Pro | DeepSeek V4 GA |
|---|---|---|
| Context Window | 2.5M tokens | ~128K tokens (standard API) |
| Input Price (API) | ~$1.25–$2.50 / 1M tokens (Vertex AI, varies by region) | ~$0.14 / 1M tokens (cache hit); ~$0.27 / 1M tokens (cache miss) — GA launch pricing |
| Output Price (API) | ~$5.00–$10.00 / 1M tokens | ~$1.10 / 1M tokens |
| Open-Source Weights | No (proprietary) | Yes (fully open) |
| Self-Hosting Option | No | Yes (H100 / A100 / H200) |
| APAC Region Inference | Singapore, Tokyo, Mumbai (Vertex AI) | Self-host anywhere; API routed via China-origin PoPs |
| Data Residency Control | Vertex AI Data Regions (contractual) | Full control if self-hosted |
| Latency (TTFT, typical) | 800ms–1.5s (long-context) | 400ms–900ms (standard context, self-hosted H100) |
| Best For | Long-doc RAG, enterprise agents, compliance | High-volume batch inference, coding, cost-first workloads |
Note: Gemini 3.1 Pro pricing sourced from Vertex AI public pricing page. DeepSeek V4 GA pricing sourced from DeepSeek official API pricing at GA launch. Both subject to change; always verify at time of purchase.
Cost Scenario: 10 Billion Tokens/Month Enterprise Workload
For an APAC enterprise processing 10 billion tokens per month (a realistic scale for a mid-size AI product or internal RAG platform), the cost difference is significant:
- Gemini 3.1 Pro (Vertex AI, 70/30 input-output split): ~$175,000–$350,000/month depending on region and caching
- DeepSeek V4 GA (API, cache-optimised): ~$10,000–$12,000/month
- DeepSeek V4 GA (self-hosted, 8× H100 cluster at $1.03/hr market rate): ~$6,000–$8,000/month all-in GPU cost, zero per-token API fee
The raw cost gap is 15×–40× in favour of DeepSeek V4 for pure volume workloads. However, this calculation ignores the value of Gemini 3.1 Pro's 2.5M token window—for workloads that genuinely require ultra-long context (whole-codebase analysis, legal document review, multi-session enterprise agents), Gemini 3.1 Pro may be the only viable option at any price.
APAC-Specific Deployment Considerations
Latency & Regional Availability
Gemini 3.1 Pro is available on Vertex AI in Singapore (asia-southeast1) and Tokyo (asia-northeast1), giving APAC users sub-200ms round-trip to model inference for most Southeast and Northeast Asian traffic. DeepSeek's managed API routes from mainland China, adding 50–150ms for Southeast Asian users. Self-hosting DeepSeek V4 on a Singapore or Tokyo GPU cloud eliminates this latency gap entirely.
Data Residency & Compliance
For iGaming operators under Philippine PAGCOR or Maltese MGA requirements, or fintech firms under MAS TRM guidelines, data residency is non-negotiable. Gemini 3.1 Pro on Vertex AI offers contractual data region commitments. DeepSeek V4 self-hosted on APAC cloud infrastructure offers the same—or stronger—guarantees because you control the stack. Using DeepSeek's managed API introduces China-jurisdiction data routing, which may conflict with some regulatory frameworks.
AWS CapEx Signal: $43.2B/Quarter
AWS announced $43.2 billion in quarterly CapEx, prioritising AI compute supply. This signals that AWS Bedrock is likely to expand its third-party model catalogue—potentially including open-source DeepSeek-