GPT-4o
gpt-4o
70% in · 30% out mix
Higher = better value
Speed
90/100
Context
128K
Tier
smart
GPT-5.6 Sol
gpt-5-6-sol
70% in · 30% out mix
Higher = better value
Speed
70/100
Context
1.1M
Tier
power
IN-DEPTH ANALYSIS
GPT-4o vs GPT-5.6 Sol: Detailed Comparison
GPT-4o is OpenAI's mid-range-tier language model with a 128K-token context window, excelling at vision/multimodal. GPT-5.6 Sol from OpenAI is a flagship-tier model supporting 1.1M tokens in context, with standout performance in reasoning.
GPT-5.6 Sol costs 2.6x what GPT-4o does per blended million tokens. That is a steep premium, and it buys a 19-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 is priced at $2.50/M input tokens and $10.00/M output tokens. GPT-5.6 Sol costs $5.00/M input and $30.00/M output.
In independent benchmark evaluations, GPT-5.6 Sol leads with coding scores of 97/100 and reasoning scores of 99/100, compared to GPT-4o's 87/100 in coding and 90/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-4o and GPT-5.6 Sol stack up head to head:
Best model by task
- coding: GPT-5.6 Sol wins with 97/100
- reasoning: GPT-5.6 Sol wins with 99/100
- data extraction: GPT-5.6 Sol wins with 95/100
- creative tasks: GPT-5.6 Sol wins with 96/100
- vision/multimodal: GPT-5.6 Sol wins with 96/100
Estimated monthly cost at scale
At 10M + 2M per month, GPT-4o runs about $45.00 while GPT-5.6 Sol runs about $110.00 — GPT-4o saves roughly $65.00 (59%) 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 Sol and GPT-4o are 26 months apart, which is more than one generation in this market. Benchmark comparisons across that gap flatter the older model: it was measured against the evaluations that existed at the time. Treat GPT-4o's scores as a floor for what it does well and be sceptical of a close-looking result.
The context gap is the largest single difference on this pair: GPT-5.6 Sol takes 1.1M tokens against 128K for GPT-4o, 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.
Latency separates them: GPT-4o scores 90/100 against 70/100 for GPT-5.6 Sol, a 20-point gap. That matters for anything a person waits on — chat, autocomplete, interactive tools. For batch and background work it does not, and trading latency for capability there is usually the right call.
GPT-5.6 Sol is 2.6x the price of GPT-4o. Reserve it for the requests that actually need it and route the rest to GPT-4o — 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-4o
- $1.00
- GPT-5.6 Sol
- $2.00
Business email
One typical email (~200 words)
- GPT-4o
- $67.50
- GPT-5.6 Sol
- $135.00
Code file
50-line Python script
- GPT-4o
- $100.00
- GPT-5.6 Sol
- $200.00
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-4o and GPT-5.6 Sol, 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
$142.50/mo
$1,710.00/yr
GPT-5.6 Sol
$375.00/mo
$4,500.00/yr
Annual Savings
$2,790.00 saved per year
GPT-4o cheaper · $232.50/mo
Deep-Dive Audit — GPT-4o & GPT-5.6 Sol
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
$361.404
Without optimization protocols, current model choices will result in $120.468 capital loss per year.
EFFICIENCY SCORE
90%
This model achieves a 90 benchmark score in this category.
CATEGORY GAP
10 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
%80
Operational Prescription
- Implement model cascading to optimize token spend.
- Analyze complex_reasoning data to leverage local semantic caching.
COST AUDIT PROTOCOL
Overkill Detected
"GPT-4o 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 $10.04/month."
3-Tier Intelligent Routing Architecture
80% 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 $1,445.616/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 |
|---|---|---|---|---|---|
Claude Fable 5LEADER | 100/100 | $10.00 | $50.00 | $720.00 | 5/100 |
GPT-5.6 Sol | 99/100 | $5.00 | $30.00 | $420.00 | 8/100 |
Claude Opus 5 | 98/100 | $5.00 | $25.00 | $360.00 | 9/100 |
Claude Opus 4.6 | 98/100 | $5.00 | $25.00 | $360.00 | 9/100 |
o3-mini | 97/100 | $1.10 | $4.40 | $66.00 | 50/100 |
DeepSeek R1BEST VALUE | 97/100 | $0.55 | $2.19 | $32.88 | 100/100 |
Claude Opus 4.8 | 96/100 | $5.00 | $25.00 | $360.00 | 9/100 |
GPT-5.2 Chat | 96/100 | $1.75 | $14.00 | $189.00 | 17/100 |
Claude 3.7 Sonnet | 95/100 | $3.00 | $15.00 | $216.00 | 15/100 |
GPT-5.6 Terra | 94/100 | $2.00 | $12.00 | $168.00 | 19/100 |
Claude 3.5 Sonnet | 93/100 | $3.00 | $15.00 | $216.00 | 15/100 |
GPT-4.1 | 93/100 | $2.00 | $8.00 | $120.00 | 26/100 |
Claude Sonnet 5 | 92/100 | $3.00 | $15.00 | $216.00 | 14/100 |
Claude 3 Opus | 90/100 | $15.00 | $75.00 | $1,080.00 | 3/100 |
Grok 4.5 | 90/100 | $2.00 | $6.00 | $96.00 | 32/100 |
GPT-4oSELECTED | 90/100 | $2.50 | $10.00 | $150.00 | 20/100 |
Gemini 3.1 Pro | 89/100 | $2.00 | $12.00 | $168.00 | 18/100 |
Gemini 2.0 Pro | 88/100 | $1.25 | $5.00 | $75.00 | 40/100 |
Llama 3.1 405B | 88/100 | $2.70 | $2.70 | $64.80 | 46/100 |
Gemini 1.5 Pro | 87/100 | $1.25 | $5.00 | $75.00 | 39/100 |
Mistral Large 2 | 86/100 | $2.00 | $6.00 | $96.00 | 30/100 |
* 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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