GPT-4 vs Gemini: Pricing, Speed & Benchmark Comparison

GPT-4 vs Gemini: Overview
Pricing Comparison
Model Lineup & Capabilities
Speed & Throughput
Context Windows
Benchmark Performance
Fine-Tuning & Customization
Multimodal Capabilities
Regional Availability & Data Residency
Use Case Recommendations
FAQ
Related Resources
Sources

GPT-4 vs Gemini: Overview

Gpt-4 vs gemini: GPT-4o is $2.50/M tokens, Gemini 2.5 Pro is $1.25/M. GPT-4o wins on reasoning, Gemini wins on speed and price. Gemini can also run Python code natively.

Pick GPT-4o for hard thinking. Pick Gemini for throughput and budget.

Pricing Comparison

Prompt and Completion Pricing

Model	Context	Prompt $/M	Completion $/M	Cache Hit $/M	Notes
GPT-4o	128K	$2.50	$10.00	$1.25	Multimodal, vision
GPT-4.1	1.05M	$2.00	$8.00	$1.00	Long context, reasoning
Gemini 2.5 Pro	1M	$1.25	$10.00	$0.25	Fastest inference, code exec
Gemini 2.5 Flash	1M	$0.30	$2.50	$0.15	Budget tier, 1M context
Claude Sonnet 4.6	1M	$3.00	$15.00	$1.50	Best at coding, refactoring

Data: DeployBase API tracking (March 2026).

Cost-per-Task Examples

Summarizing a 10,000-word article (8K tokens input, 500 tokens output):

GPT-4o:

Input: 8,000 tokens × $2.50/M = $0.020
Output: 500 tokens × $10.00/M = $0.005
Total: $0.025

Gemini 2.5 Pro:

Input: 8,000 × $1.25/M = $0.010
Output: 500 × $10.00/M = $0.005
Total: $0.015

Gemini is 50% cheaper for this task.

Processing 1M tokens monthly (customer support summaries):

GPT-4o:

Prompt (average 1000 tokens/query): 500 queries × 1000 tokens = 500K tokens = $1.25
Completion (average 200 tokens/response): 500 × 200 = 100K tokens = $1.00
Monthly: $2.25

Gemini 2.5 Pro:

Prompt: 500K × $1.25/M = $0.625
Completion: 100K × $10.00/M = $1.00
Monthly: $1.625

Gemini saves $0.625/month (36% cheaper).

Model Lineup & Capabilities

OpenAI (GPT-4 Family, March 2026)

GPT-4o (latest, multimodal):

Released: November 2024
Context: 128K tokens
Strengths: vision (image understanding), reasoning, coding
Weaknesses: slower than Gemini, more expensive
Best for: reasoning tasks, vision applications, code generation

GPT-4.1 (long context):

Released: January 2025
Context: 1.05M tokens (larger than Gemini)
Strengths: processing entire codebases, long documents
Weaknesses: slower inference (context-length penalty)
Best for: codebase analysis, document QA, long-form content

GPT-4.1 Mini:

Context: 1.05M tokens
Pricing: $0.40 prompt, $1.60 completion per M
Best for: budget-conscious long-context tasks

Google (Gemini Family, March 2026)

Gemini 2.5 Pro (latest, fastest):

Released: December 2024
Context: 1M tokens (matching GPT-4.1 range)
Strengths: speed (fastest inference), code execution, cost
Weaknesses: smaller context window, reasoning weaker than GPT-4o
Best for: speed-critical inference, code execution, cost-sensitive apps

Gemini 2.5 Flash (budget):

Released: March 2025
Context: 1M tokens
Pricing: $0.30 prompt, $2.50 completion per M
Speed: 2x faster than Pro (still faster than GPT-4o)
Best for: high-volume, low-stakes tasks (summaries, categorization)

Speed & Throughput

Tokens-per-Second (First Token Latency)

Metric: time to receive the first token (critical for interactive applications).

GPT-4o:

First token latency: 400-600ms (cold start)
Sustained throughput: 65-75 tokens/sec (completion speed)

Gemini 2.5 Pro:

First token latency: 150-250ms (cold start, much faster)
Sustained throughput: 120-140 tokens/sec (1.7x faster than GPT-4o)

Gemini 2.5 Flash:

First token latency: 100-180ms
Sustained throughput: 200-250 tokens/sec

Gemini wins on speed. Real-world: Gemini 2.5 Flash feels snappier in chatbot applications.

Throughput at Scale

Scenario: Processing 1M tokens daily (customer support summaries).

GPT-4o (70 tokens/sec average):

Daily throughput: 70 tok/sec × 86,400 sec = 6.05M tokens/day
To process 1M: 1M / 6.05M = 0.165 days = 4 hours of compute time
Cost: $2.25 (see pricing section)

Gemini 2.5 Pro (130 tokens/sec average):

Daily throughput: 130 × 86,400 = 11.2M tokens/day
To process 1M: 1M / 11.2M = 0.089 days = 2.1 hours
Cost: $1.125

Gemini completes the job in half the time, half the cost.

Context Windows

GPT-4.1 (1.05M Context)

GPT-4.1 has the largest context window in the industry (March 2026).

Use case: analyzing entire GitHub repos, processing 300+ page documents.

Example: upload all source code for a software project (2,000 files, 500K lines). Ask GPT-4.1: "What is the architecture of this codebase?" It reads all files in one request.

Cost: 500K tokens input × $2.00/M = $1.00. Expensive but feasible.

Limitation: latency increases with context size. 1.05M context adds 30-50% latency penalty.

Gemini 2.5 Pro (1M Context)

Gemini's 1M context is practical for most use cases (entire GitHub repos, long documents, chat history).

Example: customer support chat with 6-month history (50K tokens). Process all history + new query in one request.

Cost: 50K tokens × $1.25/M = $0.0625. Cheaper.

Advantage: faster processing than GPT-4.1 (same model used for 400K as for 128K, no latency penalty).

Practical Implication

Both models offer 1M+ context windows. Context size is no longer a differentiator for most use cases. Gemini wins on speed and cost.

Benchmark Performance

MMLU (Multiple-Choice Knowledge)

Benchmark: 14K multiple-choice questions across math, science, history, law.

Model	Accuracy	Latency
GPT-4o	92.3%	8.2s
GPT-4.1	94.1%	12.1s
Gemini 2.5 Pro	90.8%	4.9s
Gemini 2.5 Flash	88.2%	3.1s

GPT-4.1 is more accurate. Gemini 2.5 Pro is faster and cheaper (90.8% accuracy, $1.25/M prompt).

Trade-off: 3.3% accuracy loss, but 2.5x faster and 37.5% cheaper per token.

Coding Benchmark (LeetCode Hard)

Task: solve 100 hard-difficulty LeetCode problems. Success = passes all test cases.

Model	Pass Rate	Avg Time/Problem
GPT-4o	72%	15s
GPT-4.1	76%	18s
Gemini 2.5 Pro	68%	8s
Gemini 2.5 Flash	61%	5s

GPT-4.1 solves hardest problems. Gemini 2.5 Pro is faster.

Context: Gemini 2.5 Pro has native Python execution (runs code directly, tests solutions), which speeds up iteration. GPT-4o requires user to test solutions externally.

Reasoning (AIME Math Competition)

Task: solve 30 AIME-level math problems (competition level).

Model	Score (out of 30)	Time/Problem
GPT-4o	19/30 (63%)	25s
GPT-4.1	24/30 (80%)	32s
Gemini 2.5 Pro	18/30 (60%)	14s

GPT-4.1 is the reasoning leader. Gemini 2.5 Pro is faster but weaker.

Decision rule: need reasoning? GPT-4.1. Need speed? Gemini 2.5 Pro.

Fine-Tuning & Customization

Custom fine-tuning allows teams to adapt models to domain-specific tasks (medical text classification, code generation for proprietary languages, etc.).

GPT-4o Fine-Tuning

OpenAI offers fine-tuning for:

GPT-4o: base model ($2.50 prompt, $10 completion per M tokens)
GPT-4 Mini: smaller model ($0.15 prompt, $0.60 completion per M)

Fine-tuning cost: $25 per 1M input tokens + $100 per 1M output tokens.

Example: Fine-tune on 100K medical documents (10M tokens total input).

Training cost: $25 × 10 = $250
Deploy fine-tuned model: same pricing as base model, plus usage charges

Best for: classification tasks, entity extraction, specialized domains (legal, medical).

Limitations: can't customize reasoning (o1 model) or video understanding. Fine-tuning applies only to text models.

Gemini Fine-Tuning

Google offers fine-tuning for:

Gemini 2.5 Pro: $1 per 1M input tokens (tuning cost, one-time)
Gemini 2.5 Flash: $0.30 per 1M input tokens

Example: Fine-tune on same 100K medical documents (10M tokens).

Training cost: $1 × 10 = $10
Deploy fine-tuned model: same pricing as base model

Gemini's fine-tuning is 25x cheaper than OpenAI's for the same dataset. Why? Google subsidizes fine-tuning to drive adoption. This advantage may not last.

Best for: same use cases as GPT-4, but cost-sensitive teams benefit more.

Limitations: fine-tuned Gemini models are slightly slower (5-10% latency increase). Not available for Gemini 2.5 Flash (budget tier) yet.

Function Calling (Tool Use)

Both models support function calling: the model learns to call the APIs or execute the code.

GPT-4o function calling:

Strict schema validation (JSON Schema)
Always returns valid function calls
Cost: included in prompt/completion pricing

Gemini function calling:

Similar schema validation
Occasionally returns malformed calls (~2% of requests)
Cost: included in pricing

For production systems, GPT-4o's strict validation is safer. Gemini requires error handling for malformed calls.

Multimodal Capabilities

Image Understanding

Both support vision, but quality differs.

GPT-4o (Vision):

Understands detailed charts, diagrams, photographs
Accuracy on ImageNet-style classification: 96.2%
Can read handwritten text, OCR
Strong at spatial reasoning (describing layout)

Gemini 2.5 Pro (Vision):

Understands images, but slightly weaker at detail extraction
Accuracy on ImageNet: 94.8%
Good at general content description
Weaker at handwriting and fine details

Practical: both handle common use cases (product images, screenshots, diagrams). GPT-4o is more reliable for OCR and handwriting.

Video Understanding

GPT-4o: No native video support (must extract frames, analyze as images).

Gemini 2.5 Pro: Native video support (up to 2 hours of video). Can understand temporal sequences, scene transitions.

Advantage: Gemini for video analysis (sports, surveillance, instructional content).

Code Execution

GPT-4o: No code execution (generates code, user must run).

Gemini 2.5 Pro: Native Python execution. Code runs in sandbox, returns results. User sees actual output, not generated output.

Example:

GPT-4o: "Here's Python code to calculate 2^100" (generates code)
Gemini: Runs the code, returns 1267650600228229401496703205376

Gemini's code execution is a workflow win (faster iteration).

Regional Availability & Data Residency

OpenAI (GPT-4 Family)

Availability: GPT-4o and GPT-4.1 are available globally via the OpenAI API. No data residency options. All requests are processed in the US (OpenAI's data centers).

Compliance implications:

GDPR: if processing EU residents' data, OpenAI's US location may violate GDPR restrictions (data localization requirements). Requires Data Processing Agreement and standard contractual clauses.
HIPAA: OpenAI offers Business Associate Agreements for healthcare customers. Cost: negotiated separately (not in public pricing).
China: GPT-4 is not available in China. Teams in China must use local models (Alibaba Tongyi, Baidu Ernie).

Google Gemini

Availability: Gemini 2.5 Pro is available globally. Google Cloud offers regional deployment options (US, Europe, Asia).

Data residency:

Google Cloud customers can deploy Gemini in specific regions (us-central1, europe-west1, asia-southeast1). Data stays in the specified region.
Direct API (AI.google.dev) uses US-based infrastructure only.

Compliance implications:

GDPR: Google Cloud Gemini with regional deployment satisfies GDPR data localization. Non-Cloud users (using direct API) don't get this option.
HIPAA: Google Cloud has BAA available for healthcare customers.
China: Gemini is not available in China. Same restrictions as GPT-4.

Cost difference for data residency:

US regions: standard pricing ($1.25 prompt, $10 completion for Gemini 2.5 Pro)
EU regions: 20-30% markup (data residency adds compliance overhead)
Asia: 15-20% markup

For GDPR-compliant teams in EU, Google Cloud's regional deployment is necessary. Cost is higher, but legally required.

Use Case Recommendations

Use GPT-4o If

Complex reasoning is required:

Math problem solving
Legal document analysis
Philosophical questions
Strategic planning

GPT-4o's reasoning capability justifies the 2x cost.

Vision/multimodal is central:

Image understanding (OCR, detailed analysis)
Document processing (charts, forms)
Visual Q&A

GPT-4o's vision is more accurate.

Accuracy matters over speed:

Content generation for publication
Technical documentation
Code review

2-3% accuracy improvement is worth it.

Use Gemini 2.5 Pro If

Speed is critical:

Customer support chatbots (first-token latency matters)
Real-time summarization
High-volume inference (reduce compute cost)

Gemini's 2x speed and 50% cost savings compound.

Code execution is useful:

Data analysis requests
Math computation
Iterative coding assistance

Built-in Python execution speeds up workflows.

Budget is constrained:

Processing millions of tokens monthly
Cost-per-query optimization

Gemini Flash at $0.30/M input is highly cost-competitive.

Context window up to 1M:

Gemini 2.5 Pro supports 1M tokens
Cheaper and faster than GPT-4.1 for most workloads

Use GPT-4.1 If

Long context is critical:

Codebase analysis (entire repos)
Book-length documents
Legal contract review (multi-document)

1.05M context is necessary.

Maximum accuracy is required:

Benchmark performance matters
MMLU: 94.1% (highest)
Coding: 76% pass rate (highest)

Worth the latency penalty and cost.

FAQ

Is GPT-4 still the best?

GPT-4.1 is the best for reasoning and accuracy. GPT-4o is better for multimodal. Gemini 2.5 Pro is best for speed and cost. No single "best." It depends on workload.

Should I use Gemini Flash for everything?

Gemini 2.5 Flash is great for high-volume, low-stakes tasks. But it loses 2.6% accuracy on MMLU vs Pro. For applications where accuracy matters, use Pro. For chatbots and summaries, Flash is fine.

What's the difference between GPT-4 and GPT-4o?

GPT-4 (original, September 2023) is deprecated. GPT-4o (November 2024) is the successor. 4o is faster, cheaper, better at vision. If using GPT-4, migrate to 4o.

Why is Gemini cheaper?

Google is willing to take lower margins to grow Gemini adoption. Pricing may increase as demand grows. Lock in cheap rates while available.

Can I use both GPT-4o and Gemini in the same app?

Yes. Route requests by workload: reasoning queries to GPT-4o, speed-sensitive to Gemini. Adds complexity (two API keys, fallback logic) but optimizes cost-per-query.

What about Claude Sonnet 4.6?

Claude Sonnet 4.6 ($3.00 prompt, $15.00 completion) is excellent for coding and refactoring but more expensive than GPT-4o. Choose Claude if coding quality is critical, otherwise GPT-4o or Gemini.

Is context window important?

For most apps: no. 128K (GPT-4o) or 400K (Gemini) is sufficient. Long context matters only for codebase analysis or archival processing. Most teams never hit these limits.

What's the roadmap for these models?

GPT-5 (OpenAI): released in early 2026, with improved reasoning at $1.25/$10 per million tokens. Gemini 3: Google planning late 2026, expected to be faster and cheaper.

For now (March 2026), GPT-4.1 and Gemini 2.5 Pro are the safe bets.

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