Cost and performance comparison
GPT-5.6 Terra vs o3-mini
Model AOpenAI
GPT-5.6 Terra
- Cost / 1M Tokens
- $5.00
- 70% in · 30% out mix
- Value Index (score÷cost)
- 18.8
- Higher = better value
- Speed
- 86/100
- Context
- 1.1M
- Tier
- Smart Tier
List price from the OpenAI pricing page, verified October 12, 2026.
Check the sourceModel BOpenAI
o3-mini
- Cost / 1M Tokens
- $2.09
- 70% in · 30% out mix
- Value Index (score÷cost)
- 44.5
- Higher = better value
- Speed
- 78/100
- Context
- 200K
- Tier
- Smart Tier
List price from the OpenAI pricing page, verified September 30, 2026.
Check the sourceIn-depth analysis
GPT-5.6 Terra vs o3-mini: Detailed Comparison
GPT-5.6 Terra is OpenAI's mid-range-tier language model with a 1.1M-token context window, excelling at reasoning. o3-mini from OpenAI is a mid-range-tier model supporting 200K tokens in context, with standout performance in reasoning.
GPT-5.6 Terra costs 2.4x what o3-mini does for the same text. That is a steep premium, and it buys a 6-point lead on combined coding and reasoning. Whether that trade is worth it depends entirely on how much of your traffic actually needs the harder model — for most workloads the honest answer is a small fraction of it, which is an argument for routing rather than for picking one. GPT-5.6 Terra is priced at $2.00/M input tokens and $12.00/M output tokens. o3-mini costs $1.10/M input and $4.40/M output.
On iOPTERA's editorial scores, GPT-5.6 Terra leads with 93/100 for coding and 94/100 for reasoning, against o3-mini's 88/100 and 93/100.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Terra and o3-mini stack up head to head:
Best model by task
- coding: GPT-5.6 Terra wins with 93/100
- reasoning: GPT-5.6 Terra wins with 94/100
- data extraction: GPT-5.6 Terra wins with 93/100
- creative tasks: GPT-5.6 Terra wins with 92/100
- vision/multimodal: GPT-5.6 Terra wins with 93/100
Estimated monthly cost at scale
For 10M + 2M tokens of text a month (counted the way OpenAI's tokenizer counts), GPT-5.6 Terra runs about $44.00 while o3-mini runs about $19.80 — o3-mini saves roughly $24.20 (55%) every month.
What actually decides it
Both models come from OpenAI, so they share a tokenizer, an API surface, and a billing account. Switching between them is a model-string change and nothing else — no new SDK, no re-tokenizing your prompts to re-estimate cost, no second vendor to onboard. That makes routing between them far cheaper to implement than a cross-vendor split.
These are the same lab's model at the same tier, 18 months apart — so this is an upgrade question, not a choice between alternatives. GPT-5.6 Terra is the current entry; o3-mini is here because plenty of production traffic still runs on it. If you are starting something new there is little reason to pick the older one, and if you are already on it the question is whether the migration cost is worth the gain.
The context gap is the largest single difference on this pair: GPT-5.6 Terra takes 1.1M tokens against 200K for o3-mini, roughly 5.3x. That is the difference between feeding in a whole repository or a full contract set and having to chunk it. If your work involves documents you cannot split cleanly, this decides it on its own.
Throughput is close enough to ignore — 86/100 versus 78/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
GPT-5.6 Terra is 2.4x the price of o3-mini. Reserve it for the requests that actually need it and route the rest to o3-mini — that hybrid beats either model used alone on cost per useful answer.
What you actually pay
The list price is only the start. Prompt caching, batch processing and long prompts change the bill, and each provider sets them differently. Prices per 1M tokens.
| Pricing term | GPT-5.6 Terra | o3-mini |
|---|---|---|
| List price (input / output) | $2.00 / $12.00 | $1.10 / $4.40 |
| Cached input | $0.2010% of the input price | — |
| Batch API | 50% off input and output | — |
| Long prompts | Over 272K tokens: $4.00 / $18.00 | — |
| Input with 80% cache hits | $0.56 | — |
The last row assumes 80% of input tokens are read from the cache, as in a chat app that re-sends a long system prompt or RAG over the same documents. Cache-write and storage charges are not included.
Checked on October 8, 2026 against the pricing pages of OpenAI.
Capability scores
iOPTERA editorial scores across 5 categories — informed by public benchmarks, not measured by us
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
What Is a Token?
Models don't read words — they process tokens.
A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.
Short phrase
"Hello, world!"
- GPT-5.6 Terra
- $0.80
- o3-mini
- $0.44
Business email
One typical email (~200 words)
- GPT-5.6 Terra
- $54.00
- o3-mini
- $29.70
Code file
50-line Python script
- GPT-5.6 Terra
- $80.00
- o3-mini
- $44.00
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Terra and o3-mini, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.
How to check your token usage
response.usage.total_tokensEvery API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.
Your Cost Calculator
Enter your actual monthly token usage to see real savings
Quick Presets
GPT-5.6 Terra
$150.00/mo
$1,800.00/yr
o3-mini
$62.70/mo
$752.40/yr
Annual Savings
$1,047.60 saved per year
o3-mini cheaper · $87.30/mo
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