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OPTERA LABS

Gemini 3.1 Pro VS o3-mini

2026 Cost & Performance Comparison
Model A · Google

Gemini 3.1 Pro

gemini-3-1-pro

Intelligence Score89%
Cost / 1M Tokens$5.00

70% in · 30% out mix

Value Index(score÷cost)
17.8

Higher = better value

Speed

82/100

Context

2.0M

Tier

smart

Model B · OpenAI

o3-mini

o3-mini

Intelligence Score97%
Cost / 1M Tokens$2.09

70% in · 30% out mix

Value Index(score÷cost)
46.4

Higher = better value

Speed

78/100

Context

200K

Tier

smart

IN-DEPTH ANALYSIS

Gemini 3.1 Pro vs o3-mini: Detailed Comparison

Gemini 3.1 Pro is Google's mid-range-tier language model with a 2.0M-token context window, excelling at vision/multimodal. o3-mini from OpenAI is a mid-range-tier model supporting 200K tokens in context, with standout performance in reasoning.

o3-mini is both the cheaper and the stronger model here — it costs 58% less than Gemini 3.1 Pro on a typical prompt/completion mix and still leads on combined coding and reasoning by 10 points. There is no tradeoff to weigh on this pair: unless you need something specific from Gemini 3.1 Pro, the cheaper model is simply the better one. Gemini 3.1 Pro is priced at $2.00/M input tokens and $12.00/M output tokens. o3-mini costs $1.10/M input and $4.40/M output.

In independent benchmark evaluations, o3-mini leads with coding scores of 90/100 and reasoning scores of 97/100, 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 Gemini 3.1 Pro and o3-mini stack up head to head:

coding
88
90
reasoning
89
97
data extraction
90
85
creative tasks
88
80
vision/multimodal
95
72

Best model by task

  • coding: o3-mini wins with 90/100
  • reasoning: o3-mini wins with 97/100
  • data extraction: Gemini 3.1 Pro wins with 90/100
  • creative tasks: Gemini 3.1 Pro wins with 88/100
  • vision/multimodal: Gemini 3.1 Pro wins with 95/100

Estimated monthly cost at scale

At 10M + 2M per month, Gemini 3.1 Pro runs about $44.00 while o3-mini runs about $19.80 — o3-mini saves roughly $24.20 (55%) every month.

What actually decides it

Gemini 3.1 Pro and o3-mini 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.

Gemini 3.1 Pro and o3-mini are 15 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 o3-mini'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: Gemini 3.1 Pro takes 2.0M tokens against 200K for o3-mini, roughly 10.0x. 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 — 82/100 versus 78/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

One practical asymmetry: o3-mini 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.

o3-mini wins this comparison outright — cheaper and stronger. Choose Gemini 3.1 Pro only if it has a specific capability you need; on price and benchmarks it is behind on both.

Benchmark Comparison

Head-to-head scores across 5 categories — sourced from official evals

CategoryGemini 3.1o3-miniWinner

Coding

88
90
B

Reasoning

89
97
B

Extraction

90
85
A

Creative

88
80
A

Vision

95
72
A
Gemini 3.1 Pro: 3 wins
o3-mini: 2 wins
Gemini 3.1 Pro leads overall

Speed Score

82/100vs78/100
Geminio3-mini

Context Window

2000Kvs200K
Geminio3-mini

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
Gemini 3.1 Pro
$0.78
o3-mini
$0.44

Business email

One typical email (~200 words)

~270 tokens
Gemini 3.1 Pro
$52.38
o3-mini
$29.70

Code file

50-line Python script

~400 tokens
Gemini 3.1 Pro
$77.60
o3-mini
$44.00

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both Gemini 3.1 Pro and o3-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_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%

Gemini 3.1 Pro

$150.00/mo

$1,800.00/yr

$2/M in$12/M out
CHEAPER

o3-mini

$62.70/mo

$752.40/yr

$1.1/M in$4.4/M out

Annual Savings

$1,047.60 saved per year

o3-mini cheaper · $87.30/mo

Deep-Dive Audit — Gemini 3.1 Pro & o3-mini

SURGICAL AUDIT LAB85BEC0F9

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

$415.404

Without optimization protocols, current model choices will result in $138.468 capital loss per year.

EFFICIENCY SCORE

89%

Deep Logic

This model achieves a 89 benchmark score in this category.

CATEGORY GAP

11 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

%82

Operational Prescription

  • Implement model cascading to optimize token spend.
  • Analyze complex_reasoning data to leverage local semantic caching.

COST AUDIT PROTOCOL

Overkill Detected

"Gemini 3.1 Pro 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 $11.54/month."

3-Tier Intelligent Routing Architecture

82% 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 $1,661.616/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
o3-miniBEST VALUE
97/100
$1.10$4.40$66.00
100/100
GPT-5.2 Chat
96/100
$1.75$14.00$189.00
35/100
Claude 3.7 Sonnet
95/100
$3.00$15.00$216.00
30/100
GPT-5.6 Terra
94/100
$2.00$12.00$168.00
38/100
Claude 3.5 Sonnet
93/100
$3.00$15.00$216.00
29/100
GPT-4.1
93/100
$2.00$8.00$120.00
53/100
Claude Sonnet 5
92/100
$3.00$15.00$216.00
29/100
Grok 4.5
90/100
$2.00$6.00$96.00
64/100
GPT-4o
90/100
$2.50$10.00$150.00
41/100
Gemini 3.1 ProSELECTED
89/100
$2.00$12.00$168.00
36/100
Gemini 2.0 Pro
88/100
$1.25$5.00$75.00
80/100
Gemini 1.5 Pro
87/100
$1.25$5.00$75.00
79/100
Mistral Large 2
86/100
$2.00$6.00$96.00
61/100

* 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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