GPT-5.6 Sol
gpt-5-6-sol
70% in · 30% out mix
Higher = better value
Speed
70/100
Context
1.1M
Tier
power
Gemini 3.1 Pro
gemini-3-1-pro
70% in · 30% out mix
Higher = better value
Speed
82/100
Context
2.0M
Tier
smart
IN-DEPTH ANALYSIS
GPT-5.6 Sol vs Gemini 3.1 Pro: Detailed Comparison
GPT-5.6 Sol is OpenAI's flagship-tier language model with a 1.1M-token context window, excelling at reasoning. Gemini 3.1 Pro from Google is a mid-range-tier model supporting 2.0M tokens in context, with standout performance in vision/multimodal.
GPT-5.6 Sol costs 2.5x what Gemini 3.1 Pro 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-5.6 Sol is priced at $5.00/M input tokens and $30.00/M output tokens. Gemini 3.1 Pro costs $2.00/M input and $12.00/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 Gemini 3.1 Pro's 88/100 in coding and 89/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Sol and Gemini 3.1 Pro 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-5.6 Sol runs about $110.00 while Gemini 3.1 Pro runs about $44.00 — Gemini 3.1 Pro saves roughly $66.00 (60%) every month.
What actually decides it
GPT-5.6 Sol and Gemini 3.1 Pro come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.
GPT-5.6 Sol and Gemini 3.1 Pro were released within 3 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.
Gemini 3.1 Pro carries the larger context window at 2.0M tokens versus 1.1M for GPT-5.6 Sol. The gap is real but not decisive — it matters if your prompts routinely run long, and is irrelevant if they sit where most production prompts sit, well under 100K. Bear in mind that filling a large window is also what makes a request expensive.
Throughput is close enough to ignore — 82/100 versus 70/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
One practical asymmetry: GPT-5.6 Sol offers a batch API at 50% off standard rates, and Gemini 3.1 Pro 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-5.6 Sol is 2.5x the price of Gemini 3.1 Pro. Reserve it for the requests that actually need it and route the rest to Gemini 3.1 Pro — 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-5.6 Sol
- $2.00
- Gemini 3.1 Pro
- $0.78
Business email
One typical email (~200 words)
- GPT-5.6 Sol
- $135.00
- Gemini 3.1 Pro
- $52.38
Code file
50-line Python script
- GPT-5.6 Sol
- $200.00
- Gemini 3.1 Pro
- $77.60
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Sol and Gemini 3.1 Pro, 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-5.6 Sol
$375.00/mo
$4,500.00/yr
Gemini 3.1 Pro
$150.00/mo
$1,800.00/yr
Annual Savings
$2,700.00 saved per year
Gemini 3.1 Pro cheaper · $225.00/mo
Deep-Dive Audit — GPT-5.6 Sol & Gemini 3.1 Pro
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
$1,171.404
Without optimization protocols, current model choices will result in $390.468 capital loss per year.
EFFICIENCY SCORE
99%
This model achieves a 99 benchmark score in this category.
CATEGORY GAP
1 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
%93
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 Sol 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 $32.54/month."
3-Tier Intelligent Routing Architecture
93% 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 $4,685.616/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 SolSELECTED | 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-4o | 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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