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OPTERA LABS

Grok 4.5 VS Gemini 3.1 Pro

2026 Cost & Performance Comparison
Model A · xAI

Grok 4.5

grok-4-5

Intelligence Score90%
Cost / 1M Tokens$3.20

70% in · 30% out mix

Value Index(score÷cost)
28.1

Higher = better value

Speed

89/100

Context

500K

Tier

smart

Model B · 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

IN-DEPTH ANALYSIS

Grok 4.5 vs Gemini 3.1 Pro: Detailed Comparison

Grok 4.5 is xAI's mid-range-tier language model with a 500K-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.

Grok 4.5 is both the cheaper and the stronger model here — it costs 36% less than Gemini 3.1 Pro on a typical prompt/completion mix and still leads on combined coding and reasoning by 2 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. Grok 4.5 is priced at $2.00/M input tokens and $6.00/M output tokens. Gemini 3.1 Pro costs $2.00/M input and $12.00/M output.

In independent benchmark evaluations, Grok 4.5 leads with coding scores of 89/100 and reasoning scores of 90/100, 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 Grok 4.5 and Gemini 3.1 Pro stack up head to head:

coding
89
88
reasoning
90
89
data extraction
88
90
creative tasks
89
88
vision/multimodal
86
95

Best model by task

  • coding: Grok 4.5 wins with 89/100
  • reasoning: Grok 4.5 wins with 90/100
  • data extraction: Gemini 3.1 Pro wins with 90/100
  • creative tasks: Grok 4.5 wins with 89/100
  • vision/multimodal: Gemini 3.1 Pro wins with 95/100

Estimated monthly cost at scale

At 10M + 2M per month, Grok 4.5 runs about $32.00 while Gemini 3.1 Pro runs about $44.00 — Grok 4.5 saves roughly $12.00 (27%) every month.

What actually decides it

Grok 4.5 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.

Grok 4.5 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.

The context gap is the largest single difference on this pair: Gemini 3.1 Pro takes 2.0M tokens against 500K for Grok 4.5, roughly 4.0x. 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 — 89/100 versus 82/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

Grok 4.5 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

CategoryGrok 4.5Gemini 3.1Winner

Coding

89
88
A

Reasoning

90
89
A

Extraction

88
90
B

Creative

89
88
A

Vision

86
95
B
Grok 4.5: 3 wins
Gemini 3.1 Pro: 2 wins
Grok 4.5 leads overall

Speed Score

89/100vs82/100
GrokGemini

Context Window

500Kvs2000K
GrokGemini

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
Grok 4.5
$0.80
Gemini 3.1 Pro
$0.78

Business email

One typical email (~200 words)

~270 tokens
Grok 4.5
$54.00
Gemini 3.1 Pro
$52.38

Code file

50-line Python script

~400 tokens
Grok 4.5
$80.00
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 Grok 4.5 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_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%
CHEAPER

Grok 4.5

$96.00/mo

$1,152.00/yr

$2/M in$6/M out

Gemini 3.1 Pro

$150.00/mo

$1,800.00/yr

$2/M in$12/M out

Annual Savings

$648.00 saved per year

Grok 4.5 cheaper · $54.00/mo

Deep-Dive Audit — Grok 4.5 & Gemini 3.1 Pro

SURGICAL AUDIT LAB9AAA1CEF

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

$199.404

Without optimization protocols, current model choices will result in $66.468 capital loss per year.

EFFICIENCY SCORE

90%

Deep Logic

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

%69

Operational Prescription

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

COST AUDIT PROTOCOL

Overkill Detected

"Grok 4.5 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 $5.54/month."

3-Tier Intelligent Routing Architecture

69% 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 $797.616/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 4.5SELECTED
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 Pro
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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