tokevenGPT · Mid-tier · 1,050K context window
GPT 5.6 Terra pricing for product descriptions
$0.0190 per call at 500 input / 1,500 output tokens.
Per call
$0.0190
10K calls/mo
$190
Cache savings
90% off input
Batch savings
50% off all
Calculate your GPT 5.6 Terra costs
Adjust token counts, volume, and discount toggles to model your product descriptions spend. Prices verified daily against provider pricing pages.
Configure your usage
Estimated cost
Quick start
Call GPT 5.6 Terra with the OpenAI Python SDK:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5.6-terra",
max_tokens=1500,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0190 per call at standard rates
# Input: $2.00/MTok
# Output: $12.00/MTokHow GPT 5.6 Terra compares on cost
Per-call cost for all models scoring 50+ on product descriptions benchmarks. Hover for details.
Cheapest viable option: Claude Sonnet 5 at $0.0160/call (16% less, 87/100 quality).
What drives product descriptions cost
Catalogue-scale generation: modest output per item, multiplied across thousands of SKUs. Per-call price is small and total spend is set by catalogue size, which makes this a batch API workload almost by definition: descriptions are rarely needed within seconds. Shared brand and formatting context belongs in the cached prefix.
For GPT 5.6 Terra specifically, that means output tokens cost 6.0x more than input, at $12.00 vs $2.00 per MTok. At this use case’s 500/1,500 token split, output accounts for 95% of the $0.0190 per-call cost.
Complete GPT 5.6 Terra pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $2.00 | 500 |
| Output | $12.00 | 1,500 |
Prompt caching: 90% off input tokens
Caching drops input cost from $2.00 to $0.20/MTok. For repeated system prompts, that saves $0.000900 per call.
Batch API: 50% off all tokens
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
Monthly cost projections
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.0190 | $190 | $181 | $9 |
| 50,000 calls | $0.0190 | $950 | $905 | $45 |
| 100,000 calls | $0.0190 | $1,900 | $1,810 | $90 |
| 500,000 calls | $0.0190 | $9,500 | $9,050 | $450 |
| 1,000,000 calls | $0.0190 | $19,000 | $18,100 | $900 |
GPT 5.6 Terra quality benchmarks for product descriptions
GPT 5.6 Terra is a solid pick for product descriptions
Scores 80/100 (rank #5 of 20). Good balance of quality and cost for product descriptions.
Top-ranked: Claude Fable 5 (95/100) at $0.0800/call
Performance across all task types
GPT 5.6 Terra scores by task type. Product Descriptions highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Summarization | 92/100 | #1 |
| Q&A | 92/100 | #1 |
| Extraction | 90/100 | #1 |
| Classification | 89/100 | #1 |
| Code Generation | 86/100 | #4 |
| Code Review | 84/100 | #4 |
| Reasoning | 82/100 | #4 |
| Creative Writing(current page) | 80/100 | #5 |
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Alternative models for product descriptions
7 models score 80+ on product descriptions benchmarks, sorted by input rate. Cheapest: Claude Sonnet 5 (16% less, 87/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 87/100 | $0.0160 | 16% cheaper |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 84/100 | $0.0190 | same cost |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 85/100 | $0.0240 | 26% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 92/100 | $0.0320 | 68% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 93/100 | $0.0400 | 111% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 91/100 | $0.0475 | 150% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 95/100 | $0.0800 | 321% more |
About GPT 5.6 Terra
Provider
GPT
Tier
Mid-tier
Context
1,050K tokens
Released
Jul 2026
Other models from GPT
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| GPT 5.5 | Flagship | $5.00 | $30.00 | 1,050K |
| GPT 5.4 | Mid-tier | $2.50 | $15.00 | 1,050K |
| GPT 5.6 Sol | Flagship | $4.00 | $20.00 | 1,050K |
| GPT 5.6 Luna | Fast / Budget | $0.20 | $1.20 | 1,050K |
You can compare GPT 5.6 Terra head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.
GPT 5.6 Terra pricing for other use cases
See how GPT 5.6 Terra costs change across different workload types.
Code Generation
$0.0520/call
Unit Test Generation
$0.0660/call
SQL Generation
$0.0150/call
API Generation
$0.0780/call
Code Refactoring
$0.0560/call
Code Review
$0.0340/call
PR Review
$0.0520/call
Security Review
$0.0420/call
Bug Detection
$0.0340/call
Document Summarization
$0.0320/call
PDF Summarization
$0.0480/call
Meeting Notes
$0.0400/call
Email Summarization
$0.0120/call
Chatbot
$0.0160/call
RAG Pipeline
$0.0300/call
Customer Support Bot
$0.0180/call
FAQ Bot
$0.0126/call
Data Extraction
$0.0200/call
JSON Extraction
$0.0156/call
Invoice Parsing
$0.0112/call
Resume Parsing
$0.0146/call
Chain of Thought
$0.0660/call
Legal Analysis
$0.0680/call
Math Reasoning
$0.0390/call
Financial Analysis
$0.0640/call
Content Moderation
$0.002200/call
Sentiment Analysis
$0.002200/call
Categorization
$0.003400/call
Intent Detection
$0.001800/call
Copywriting
$0.0250/call
Blog Writing
$0.0980/call
Marketing Copy
$0.0376/call
Related pages
Frequently asked questions
How much does GPT 5.6 Terra cost for product descriptions per API call?
A typical product descriptions call using GPT 5.6 Terra costs about $0.0190 per call. This estimate uses 500 input tokens and 1,500 output tokens at standard rates. Standard rates are $2.00 per million input tokens and $12.00 per million output tokens.
Is GPT 5.6 Terra the best model for product descriptions?
GPT 5.6 Terra scores 80 out of 100 on product descriptions benchmarks overall. It ranks #5 across all 20 models in our cross-provider registry. The highest-ranked model for this task is Claude Fable 5 from Claude. That model scores 95/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
What is the cheapest model for product descriptions with good quality?
The most affordable model for product descriptions above 80/100 quality is Claude Sonnet 5. It comes from Claude at $2.00 per million input tokens. Output costs $10.00 per million tokens with a quality score of 87/100. Switching from GPT 5.6 Terra to Claude Sonnet 5 saves about 16% per API call.
How can I reduce my GPT 5.6 Terra costs for product descriptions?
Three strategies lower GPT 5.6 Terra costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.0190 to about $0.000950 per call.
How does GPT 5.6 Terra pricing compare to other GPT models?
GPT has 4 other models alongside GPT 5.6 Terra today: GPT 5.5 at $5.00/$30.00 per million tokens (flagship), GPT 5.4 at $2.50/$15.00 per million tokens (mid-tier), GPT 5.6 Sol at $4.00/$20.00 per million tokens (flagship), GPT 5.6 Luna at $0.20/$1.20 per million tokens (fast / budget). The right choice depends on whether your product descriptions workload prioritizes quality, cost, or speed.
What context window does GPT 5.6 Terra support?
GPT 5.6 Terra supports a context window of 1,050K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For product descriptions, typical calls use 500 input and 1,500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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