GPT-4o Mini
gpt-4o-mini
70% in · 30% out mix
Higher = better value
Speed
97/100
Context
128K
Tier
fast
GPT-5.6 Luna
gpt-5-6-luna
70% in · 30% out mix
Higher = better value
Speed
98/100
Context
1.1M
Tier
fast
IN-DEPTH ANALYSIS
GPT-4o Mini vs GPT-5.6 Luna: Detailed Comparison
GPT-4o Mini is OpenAI's lightweight-tier language model with a 128K-token context window, excelling at data extraction. GPT-5.6 Luna from OpenAI is a lightweight-tier model supporting 1.1M tokens in context, with standout performance in data extraction.
This is a genuine tradeoff rather than a clear win. GPT-5.6 Luna leads by 17 points on combined coding and reasoning, and charges 43% more per blended million tokens to do it. The margin is narrow enough that the answer depends on your workload: on tasks where the extra capability shows up, the premium pays for itself; on routine work it does not. GPT-4o Mini is priced at $0.15/M input tokens and $0.60/M output tokens. GPT-5.6 Luna costs $0.20/M input and $1.20/M output.
In independent benchmark evaluations, GPT-5.6 Luna leads with coding scores of 84/100 and reasoning scores of 85/100, compared to GPT-4o Mini's 74/100 in coding and 78/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-4o Mini and GPT-5.6 Luna stack up head to head:
Best model by task
- coding: GPT-5.6 Luna wins with 84/100
- reasoning: GPT-5.6 Luna wins with 85/100
- data extraction: GPT-4o Mini wins with 95/100
- creative tasks: GPT-5.6 Luna wins with 84/100
- vision/multimodal: GPT-5.6 Luna wins with 85/100
Estimated monthly cost at scale
At 10M + 2M per month, GPT-4o Mini runs about $2.70 while GPT-5.6 Luna runs about $4.40 — GPT-4o Mini saves roughly $1.70 (39%) 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, 24 months apart — so this is an upgrade question, not a choice between alternatives. GPT-5.6 Luna is the current entry; GPT-4o 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 Luna takes 1.1M tokens against 128K for GPT-4o Mini, roughly 8.2x. 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 — 98/100 versus 97/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
Neither model is the obvious answer. GPT-5.6 Luna leads on benchmarks, GPT-4o Mini on cost, and the gap is small on both. Run the calculator above with your real token mix — for most workloads that decides it faster than any benchmark table will.
Benchmark Comparison
Head-to-head scores across 5 categories — sourced from official evals
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-4o Mini
- $0.06
- GPT-5.6 Luna
- $0.08
Business email
One typical email (~200 words)
- GPT-4o Mini
- $4.05
- GPT-5.6 Luna
- $5.40
Code file
50-line Python script
- GPT-4o Mini
- $6.00
- GPT-5.6 Luna
- $8.00
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-4o Mini and GPT-5.6 Luna, 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-4o Mini
$8.55/mo
$102.60/yr
GPT-5.6 Luna
$15.00/mo
$180.00/yr
Annual Savings
$77.40 saved per year
GPT-4o Mini cheaper · $6.45/mo
Deep-Dive Audit — GPT-4o Mini & GPT-5.6 Luna
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
-$61.596
Without optimization protocols, current model choices will result in -$20.532 capital loss per year.
EFFICIENCY SCORE
78%
This model achieves a 78 benchmark score in this category.
CATEGORY GAP
22 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
%-228
Operational Prescription
- Implement model cascading to optimize token spend.
- Analyze complex_reasoning data to leverage local semantic caching.
COST AUDIT PROTOCOL
Categorical Fit
"GPT-4o Mini scores 78 in this category — a well-matched choice."
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 $-1.71/month."
3-Tier Intelligent Routing Architecture
-228% SAVINGS VIA ROUTINGGPT-5 Nano
IQ Score: 72/100
$18.00/yr
o3-mini
IQ Score: 97/100
$277.20/yr
DeepSeek R1
IQ Score: 97/100
$59.184/yr
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)| Model | Benchmark | Input (per M) | Output (per M) | Annual Cost* | Value Index |
|---|---|---|---|---|---|
DeepSeek V3 | 91/100 | $0.28 | $0.42 | $8.40 | 45/100 |
GPT-5.6 Luna | 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 MiniSELECTED | 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).
// 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);
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