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OPTERA LABS

GPT-4o Mini VS GPT-5 Nano

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-4o Mini

gpt-4o-mini

Intelligence Score78%
Cost / 1M Tokens$0.29

70% in · 30% out mix

Value Index(score÷cost)
273.7

Higher = better value

Speed

97/100

Context

128K

Tier

fast

Model B · OpenAI

GPT-5 Nano

gpt-5-nano

Intelligence Score72%
Cost / 1M Tokens$0.11

70% in · 30% out mix

Value Index(score÷cost)
626.1

Higher = better value

Speed

99/100

Context

128K

Tier

fast

IN-DEPTH ANALYSIS

GPT-4o Mini vs GPT-5 Nano: Detailed Comparison

GPT-4o Mini is OpenAI's lightweight-tier language model with a 128K-token context window, excelling at data extraction. GPT-5 Nano from OpenAI is a lightweight-tier model supporting 128K tokens in context, with standout performance in data extraction.

GPT-4o Mini costs 2.5x what GPT-5 Nano does per blended million tokens. That is a steep premium, and it buys a 8-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-4o Mini is priced at $0.15/M input tokens and $0.60/M output tokens. GPT-5 Nano costs $0.10/M input and $0.15/M output.

In independent benchmark evaluations, GPT-4o Mini leads with coding scores of 74/100 and reasoning scores of 78/100, compared to GPT-5 Nano's 72/100 in coding and 72/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-4o Mini and GPT-5 Nano stack up head to head:

coding
74
72
reasoning
78
72
data extraction
95
88
creative tasks
83
75
vision/multimodal
80
70

Best model by task

  • coding: GPT-4o Mini wins with 74/100
  • reasoning: GPT-4o Mini wins with 78/100
  • data extraction: GPT-4o Mini wins with 95/100
  • creative tasks: GPT-4o Mini wins with 83/100
  • vision/multimodal: GPT-4o Mini wins with 80/100

Estimated monthly cost at scale

At 10M + 2M per month, GPT-4o Mini runs about $2.70 while GPT-5 Nano runs about $1.30 — GPT-5 Nano saves roughly $1.40 (52%) 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 Nano 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.

Context is effectively a tie: 128K against 128K. 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 — 99/100 versus 97/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

One practical asymmetry: GPT-4o Mini offers a batch API at 50% off standard rates, and GPT-5 Nano does not. For anything that does not need an answer immediately — nightly enrichment, backfills, evaluation runs — that discount can be worth more than the difference in list price, and it is easy to overlook when comparing headline rates.

GPT-4o Mini is 2.5x the price of GPT-5 Nano. Reserve it for the requests that actually need it and route the rest to GPT-5 Nano — 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-4o MiniGPT-5 NanoWinner

Coding

74
72
A

Reasoning

78
72
A

Extraction

95
88
A

Creative

83
75
A

Vision

80
70
A
GPT-4o Mini: 5 wins
GPT-5 Nano: 0 wins
GPT-4o Mini leads overall

Speed Score

97/100vs99/100
GPT-4oGPT-5

Context Window

128Kvs128K
GPT-4oGPT-5

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-4o Mini
$0.06
GPT-5 Nano
$0.04

Business email

One typical email (~200 words)

~270 tokens
GPT-4o Mini
$4.05
GPT-5 Nano
$2.70

Code file

50-line Python script

~400 tokens
GPT-4o Mini
$6.00
GPT-5 Nano
$4.00

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 Nano, 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%

GPT-4o Mini

$8.55/mo

$102.60/yr

$0.15/M in$0.6/M out
CHEAPER

GPT-5 Nano

$3.45/mo

$41.40/yr

$0.1/M in$0.15/M out

Annual Savings

$61.20 saved per year

GPT-5 Nano cheaper · $5.10/mo

Deep-Dive Audit — GPT-4o Mini & GPT-5 Nano

SURGICAL AUDIT LAB1D4C1631

Surgically Auditing: Deep Logic

Leakage Detected

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%

Deep Logic

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 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
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).

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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