GPT-5 Nano
gpt-5-nano
70% in · 30% out mix
Higher = better value
Speed
99/100
Context
128K
Tier
fast
GPT-4o Mini
gpt-4o-mini
70% in · 30% out mix
Higher = better value
Speed
97/100
Context
128K
Tier
fast
IN-DEPTH ANALYSIS
GPT-5 Nano vs GPT-4o Mini: Detailed Comparison
GPT-5 Nano is OpenAI's lightweight-tier language model with a 128K-token context window, excelling at data extraction. GPT-4o Mini 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-5 Nano is priced at $0.10/M input tokens and $0.15/M output tokens. GPT-4o Mini costs $0.15/M input and $0.60/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-5 Nano and GPT-4o Mini stack up head to head:
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-5 Nano runs about $1.30 while GPT-4o Mini runs about $2.70 — 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
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 Nano
- $0.04
- GPT-4o Mini
- $0.06
Business email
One typical email (~200 words)
- GPT-5 Nano
- $2.70
- GPT-4o Mini
- $4.05
Code file
50-line Python script
- GPT-5 Nano
- $4.00
- GPT-4o Mini
- $6.00
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5 Nano and GPT-4o 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 Nano
$3.45/mo
$41.40/yr
GPT-4o Mini
$8.55/mo
$102.60/yr
Annual Savings
$61.20 saved per year
GPT-5 Nano cheaper · $5.10/mo
Deep-Dive Audit — GPT-5 Nano & GPT-4o Mini
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
-$79.596
Without optimization protocols, current model choices will result in -$26.532 capital loss per year.
EFFICIENCY SCORE
72%
This model achieves a 72 benchmark score in this category.
CATEGORY GAP
28 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
%-884
Operational Prescription
- Implement model cascading to optimize token spend.
- Analyze complex_reasoning data to leverage local semantic caching.
COST AUDIT PROTOCOL
Categorical Fit
"GPT-5 Nano scores 72 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 $-2.21/month."
3-Tier Intelligent Routing Architecture
-884% 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 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 NanoSELECTEDBEST 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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