OpenAI: GPT-6.1 Terra vs Microsoft: Phi 4
Compare real-world API costs for OpenAI: GPT-6.1 Terra ($1/$5) vs Microsoft: Phi 4 ($0.07/$0.14). Calculate Prompt Caching ROI & Batch API savings.
OpenAI: GPT-6.1 Terra
- Base Input Rate
- $1.00 / 1M
- Output Rate
- $5.00 / 1M
- Prompt Cache Read
- $0.050 / 1M (-95%)
- Cache Write Surcharge
- $0.00 (Free write)
- Batch API Discount
- -50% Off
- Context Window
- 1,050k tokens
- Quality Index (MMLU)
- 87.8
Microsoft: Phi 4
- Base Input Rate
- $0.07 / 1M
- Output Rate
- $0.14 / 1M
- Prompt Cache Read
- $0.035 / 1M (-50%)
- Cache Write Surcharge
- $0.00 (Free write)
- Batch API Discount
- -50% Off
- Context Window
- 16.384k tokens
- Quality Index (MMLU)
- 78
Prompt Caching ROI & Write Surcharge Break-Even Analysis
Prompt caching drastically reduces input token costs for static prefixes like system prompts, tool schemas, and document chunks. Compare caching mechanisms between OpenAI: GPT-6.1 Terra and Microsoft: Phi 4:
OpenAI: GPT-6.1 Terra Caching Profile-95% read discount
- Cached Read Price: $0.050 per 1M tokens.
- Write Surcharge: $0.00 (automatic caching with zero write penalty).
- Minimum Cache Prefix: 1,024 tokens.
- Amortization Rule: Immediate net savings from first cached read call.
Microsoft: Phi 4 Caching Profile-50% read discount
- Cached Read Price: $0.035 per 1M tokens.
- Write Surcharge: $0.00 (automatic caching with zero write penalty).
- Minimum Cache Prefix: 1,024 tokens.
- Amortization Rule: Immediate net savings from first cached read call.
Batch API 50% Discount Offload vs Real-Time SLA
Asynchronous Batch APIs cut total token expenditures in half (50% off input and output) in exchange for non-realtime execution (up to 24-hour turnaround SLA).
| Model / Architecture | Standard Input / Output | Batch 50% Off Input / Output | Turnaround SLA | Optimal Use Cases |
|---|---|---|---|---|
| OpenAI: GPT-6.1 Terra | $1.00 / $5.00 | $0.50 / $2.50 (-50%) | 24 Hours max (typically 30m - 2h) | Bulk evaluation, dataset labeling, background summarization |
| Microsoft: Phi 4 | $0.07 / $0.14 | $0.04 / $0.07 (-50%) | 24 Hours max (typically 30m - 2h) | Offline document translation, embedding backfills, test suites |
Context Window Escalation & Tiered Pricing Architecture
Massive context windows allow ingesting entire repositories or books, but pricing models differ drastically between flat-rate vendors and tiered step-up pricing.
Provides a 1,050k token context window with a maximum output limit of 128k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.
Provides a 16.384k token context window with a maximum output limit of 14.745k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.
Real-World Production Scenarios: Monthly Spend Projections
Pre-computed monthly engineering bill projections across 3 representative enterprise application workloads:
RAG Knowledge Base & Enterprise Search
100,000 queries/month, 3,500 input tokens (system + 5 retrieved chunks), 400 output tokens, 75% prompt cache hit rate.
Multi-Turn Coding Agent & ReAct Loops
25,000 tasks/month, 8,000 input tokens (accumulating conversation context & tool schemas), 1,200 output tokens, 85% cache hit rate.
High-Throughput Offline Data Extraction
500,000 records/month, 1,500 input tokens, 250 output tokens, 100% batch offload (24-hour turnaround SLA).
OpenAI: GPT-6.1 Terra 對比 Microsoft: Phi 4
針對企業典型三大工程落地形態（RAG 知識庫、Coding Agent 多輪、離線批次清洗）進行精確成本演算：
預計每月節省
Frequently Asked Questions: OpenAI: GPT-6.1 Terra vs Microsoft: Phi 4
Authoritative answers on API rate limits, pricing math, break-even thresholds, and architectural migration.
1Which model is cheaper overall: OpenAI: GPT-6.1 Terra or Microsoft: Phi 4?
At base pricing, OpenAI: GPT-6.1 Terra costs $1/1M input and $5/1M output, while Microsoft: Phi 4 costs $0.07/1M input and $0.14/1M output. The cheaper option depends on your input-to-output token ratio and whether prompt caching or batch discounts are utilized.
2How does Prompt Caching impact the cost of OpenAI: GPT-6.1 Terra vs Microsoft: Phi 4?
OpenAI: GPT-6.1 Terra offers a 95% read discount ($0.05/1M), whereas Microsoft: Phi 4 provides a 50% read discount ($0.035/1M). High cache hit ratios (>70%) significantly compress the cost delta in agentic and RAG architectures.
3Can I use Batch API with OpenAI: GPT-6.1 Terra or Microsoft: Phi 4 to save 50%?
OpenAI: GPT-6.1 Terra supports 50% batch offload discounts for asynchronous 24-hour turnaround tasks. Microsoft: Phi 4 supports 50% batch discounts.
4What are the context window limits and tiered pricing rules for OpenAI: GPT-6.1 Terra vs Microsoft: Phi 4?
OpenAI: GPT-6.1 Terra features a 1,050k context window with flat per-token pricing. In comparison, Microsoft: Phi 4 supports 16.384k tokens with flat per-token pricing.