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OPTERA LABS

GPT-5.6 Luna VS GPT-5.6 Terra

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-5.6 Luna

gpt-5-6-luna

Intelligence Score85%
Cost / 1M Tokens$0.50

70% in · 30% out mix

Value Index(score÷cost)
170.0

Higher = better value

Speed

98/100

Context

1.1M

Tier

fast

Model B · OpenAI

GPT-5.6 Terra

gpt-5-6-terra

Intelligence Score94%
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

IN-DEPTH ANALYSIS

GPT-5.6 Luna vs GPT-5.6 Terra: Detailed Comparison

GPT-5.6 Luna is OpenAI's lightweight-tier language model with a 1.1M-token context window, excelling at data extraction. GPT-5.6 Terra from OpenAI is a mid-range-tier model supporting 1.1M tokens in context, with standout performance in reasoning.

GPT-5.6 Terra costs 10.0x what GPT-5.6 Luna does per blended million tokens. That is a steep premium, and it buys a 18-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 Luna is priced at $0.20/M input tokens and $1.20/M output tokens. GPT-5.6 Terra costs $2.00/M input and $12.00/M output.

In independent benchmark evaluations, GPT-5.6 Terra leads with coding scores of 93/100 and reasoning scores of 94/100, compared to GPT-5.6 Luna's 84/100 in coding and 85/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-5.6 Luna and GPT-5.6 Terra stack up head to head:

coding
84
93
reasoning
85
94
data extraction
92
93
creative tasks
84
92
vision/multimodal
85
93

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

At 10M + 2M per month, GPT-5.6 Luna runs about $4.40 while GPT-5.6 Terra runs about $44.00 — GPT-5.6 Luna saves roughly $39.60 (90%) 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.

GPT-5.6 Luna and GPT-5.6 Terra were released within 0 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.

Context is effectively a tie: 1.1M against 1.1M. Neither model unlocks a document size the other cannot handle, so this axis should not enter the decision. If you were hoping context would break the tie for you, it will not.

Throughput is close enough to ignore — 98/100 versus 86/100. 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 10.0x the price of GPT-5.6 Luna. Reserve it for the requests that actually need it and route the rest to GPT-5.6 Luna — that hybrid beats either model used alone on cost per useful answer.

Benchmark Comparison

Head-to-head scores across 5 categories — sourced from official evals

CategoryGPT-5.6 LunaGPT-5.6 TerraWinner

Coding

84
93
B

Reasoning

85
94
B

Extraction

92
93
B

Creative

84
92
B

Vision

85
93
B
GPT-5.6 Luna: 0 wins
GPT-5.6 Terra: 5 wins
GPT-5.6 Terra leads overall

Speed Score

98/100vs86/100
GPT-5.6GPT-5.6

Context Window

1050Kvs1050K
GPT-5.6GPT-5.6

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!"

4 tokens
GPT-5.6 Luna
$0.08
GPT-5.6 Terra
$0.80

Business email

One typical email (~200 words)

~270 tokens
GPT-5.6 Luna
$5.40
GPT-5.6 Terra
$54.00

Code file

50-line Python script

~400 tokens
GPT-5.6 Luna
$8.00
GPT-5.6 Terra
$80.00

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Luna and GPT-5.6 Terra, 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_tokens

Every 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

30.0M TOKENS
Prompt 70%Completion 30%
CHEAPER

GPT-5.6 Luna

$15.00/mo

$180.00/yr

$0.2/M in$1.2/M out

GPT-5.6 Terra

$150.00/mo

$1,800.00/yr

$2/M in$12/M out

Annual Savings

$1,620.00 saved per year

GPT-5.6 Luna cheaper · $135.00/mo

Deep-Dive Audit — GPT-5.6 Luna & GPT-5.6 Terra

SURGICAL AUDIT LAB7D332818

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

-$38.196

Without optimization protocols, current model choices will result in -$12.732 capital loss per year.

EFFICIENCY SCORE

85%

Deep Logic

This model achieves a 85 benchmark score in this category.

CATEGORY GAP

15 pts

Distance from Leader

Competitive Landscape Analysis

Source: MMLU-Pro + GPQA Diamond (Apr 2026)

Category Champion: Claude Fable 5

According to MMLU-Pro + GPQA Diamond (Apr 2026) data, Claude Fable 5 provides the optimum balance for Deep Logic tasks.

Market Score

%100

Savings Rate

%-76

Operational Prescription

  • Implement model cascading to optimize token spend.
  • Analyze complex_reasoning data to leverage local semantic caching.

COST AUDIT PROTOCOL

Overkill Detected

"GPT-5.6 Luna is overpriced for this task type. Claude Fable 5 scores 100 in this category at a fraction of the cost."

Categorical Alternative Opportunity

"Claude Fable 5 leads this category with 100 points according to MMLU-Pro + GPQA Diamond (Apr 2026) data."

Inertia Tax Detected

"85% of traffic can be routed to cheaper models. Fast tier (GPT-5 Nano) and Smart tier (o3-mini) can save $-1.06/month."

3-Tier Intelligent Routing Architecture

-76% SAVINGS VIA ROUTING
Fast Tier
50%

GPT-5 Nano

IQ Score: 72/100

$18.00/yr

Smart Tier
35%

o3-mini

IQ Score: 97/100

$277.20/yr

Power Tier
15%

DeepSeek R1

IQ Score: 97/100

$59.184/yr

Fast Tier 50%Smart Tier 35%Power Tier 15%

Without tiered routing, you pay the 'Inertia Tax' — routing all traffic to the most expensive model regardless of task complexity. Tiered cascade eliminates $0.00/year in avoidable overhead.

Deep Logic — Model Cost / Quality Matrix

Source: MMLU-Pro + GPQA Diamond (Apr 2026)
ModelBenchmarkInput (per M)Output (per M)Annual Cost*Value Index
o3-mini
97/100
$1.10$4.40$66.00
6/100
GPT-5.2 Chat
96/100
$1.75$14.00$189.00
2/100
Claude 3.7 Sonnet
95/100
$3.00$15.00$216.00
2/100
GPT-5.6 Terra
94/100
$2.00$12.00$168.00
2/100
Claude 3.5 Sonnet
93/100
$3.00$15.00$216.00
2/100
GPT-4.1
93/100
$2.00$8.00$120.00
3/100
Claude Sonnet 5
92/100
$3.00$15.00$216.00
2/100
DeepSeek V3
91/100
$0.28$0.42$8.40
45/100
Grok 4.5
90/100
$2.00$6.00$96.00
4/100
GPT-4o
90/100
$2.50$10.00$150.00
3/100
Gemini 3.1 Pro
89/100
$2.00$12.00$168.00
2/100
Gemini 2.0 Pro
88/100
$1.25$5.00$75.00
5/100
Gemini 1.5 Pro
87/100
$1.25$5.00$75.00
5/100
Mistral Large 2
86/100
$2.00$6.00$96.00
4/100
GPT-5.6 LunaSELECTED
85/100
$0.20$1.20$16.80
21/100
DeepSeek V3.2
83/100
$0.26$0.38$7.68
45/100
Claude Haiku 4.5
82/100
$1.00$5.00$72.00
5/100
Gemini 2.0 Flash
81/100
$0.10$0.40$6.00
56/100
Claude 3.5 Haiku
80/100
$0.80$4.00$57.60
6/100
Llama 3 70B
79/100
$0.65$2.75$40.80
8/100
GPT-4o Mini
78/100
$0.15$0.60$9.00
36/100
Gemini 1.5 Flash
76/100
$0.07$0.30$4.50
70/100
GPT-5 NanoBEST VALUE
72/100
$0.10$0.15$3.00
100/100
Claude 3 Haiku
70/100
$0.25$1.25$18.00
16/100

* Annual cost for given volumes. Value Index = Score / Cost (Higher = Best Value).

Tactical Code Gen
// iOPTERA Surgical Routing Wrapper
const auditModel = async (prompt: string) => {
  const complexity = measureComplexity(prompt);
  
  // Tactical Cascade Logic
  if (complexity < 0.45) {
    // Redirect simple tasks to efficient model
    return await llm.call("iOPTERA Optimization", prompt); 
  }
  
  // High-latency routing for complex reasoning
  return await llm.call("Claude Fable 5", prompt);
};
READY TO DEPLOY IN Vercel Edge OR AWS Lambda

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