AI Model Pricing
Comparison 2026

Q: What's the cheapest AI model in 2026?

By per-token rate: GPT-5 Nano ($0.05/$0.40), Gemini 2.5 Flash-Lite ($0.10/$0.40), and Mistral Small 3.2 ($0.10/$0.30) are among the cheapest. By cost-per-task: it depends entirely on your workload. DeepSeek Chat often wins on cost-efficiency because it produces concise outputs at $0.28/$0.42.

Q: Is Claude more expensive than GPT?

At similar tiers, Claude and GPT are comparably priced. Claude Sonnet 4.5 ($3/$15) vs GPT-5 ($1.25/$10) are close. But Claude often produces more concise outputs, so the cost-per-task can be lower despite higher per-token rates.

Q: How can I reduce AI API costs?

1) Benchmark to find the cheapest model that meets your quality bar. 2) Use prompt caching for repetitive workloads. 3) Optimize prompts to reduce token count. 4) Consider batch APIs for non-real-time tasks. 5) Route different task types to different models.

Per-token rates are misleading. The real cost depends on YOUR task. Compare actual API pricing across GPT, Claude, Gemini, DeepSeek, and 100+ models.

Key insight: A "cheap" model that uses 3x more tokens costs the same as an "expensive" one. The only way to know the real cost is to benchmark on your actual task. OpenMark shows you cost-per-task, not just cost-per-token.

AI Pricing at a Glance

AI model pricing falls into three broad tiers. Which tier is right for you depends on accuracy requirements, volume, and budget:

Budget Tier

< $1/M

DeepSeek Chat, GPT-5 Nano, Gemini 2.5 Flash-Lite, Mistral Small, MiniMax M2.5 — great for high-volume, simple tasks

Standard Tier

$1–$15/M

GPT-5 series, Claude Sonnet 4.5, Gemini 2.5 Pro, Grok 4 — best balance of quality and cost

Premium Tier

$20+/M

Claude Opus 4.5, GPT-5 Pro, o3-pro — maximum capability, research-grade tasks

Full Pricing Table (March 2026)

Prices shown per 1 million tokens. Input = what you send (prompts, context). Output = what the model generates (responses).

Model	Provider	Input $/1M	Output $/1M	Context
GPT-5 Nano	OpenAI	$0.05	$0.40	400K
Gemini 2.5 Flash-Lite	Google	$0.10	$0.40	1M
Gemini 3.1 Flash-Lite	Google	$0.25	$1.50	1M
GPT-4.1 Nano	OpenAI	$0.10	$0.40	1M
Mistral Small 3.2	Mistral	$0.10	$0.30	128K
DeepSeek Chat	DeepSeek	$0.28	$0.42	128K
Grok 4 Fast	xAI	$0.20	$0.50	2M
MiniMax M2.5	MiniMax	$0.30	$1.20	192K
GPT-5	OpenAI	$1.25	$10.00	400K
GPT-5.3 Chat	OpenAI	$1.75	$14.00	400K
GPT-5.4	OpenAI	$2.50	$15.00	400K
GPT-4.1	OpenAI	$2.00	$8.00	1M
Claude Sonnet 4.5	Anthropic	$3.00	$15.00	200K
Gemini 2.5 Pro	Google	$1.25	$10.00	1M
Gemini 2.5 Flash	Google	$0.30	$2.50	1M
Grok 4	xAI	$3.00	$15.00	256K
Mistral Large 3	Mistral	$0.50	$1.50	256K
Claude Opus 4.5	Anthropic	$5.00	$25.00	200K
GPT-5 Pro	OpenAI	$15.00	$120.00	400K
GPT-5.4 Pro	OpenAI	$30.00	$180.00	400K
o3-pro	OpenAI	$20.00	$80.00	200K

Prices as of March 2026. OpenMark's model registry includes 100+ models with live pricing. See all models →

Why Per-Token Pricing Is Misleading

The Real Cost Formula

What matters isn't cost per token — it's cost per task:

Cost per task = (input_tokens × input_rate) + (output_tokens × output_rate)

Different models tokenize differently and generate different amounts of output. A model that costs $0.50/M tokens but produces 3x more output than a $1.50/M model actually costs MORE per task.

Hidden Cost Factors

💡 Token verbosity: Some models use 2-3x more tokens for the same result. "Cheaper per token" doesn't mean cheaper per task.

💡 Retry costs: A model with 90% success rate costs you 10% in wasted API calls. Track stability, not just accuracy.

💡 Context stuffing: Long system prompts mean high input costs every call. Models with larger context windows may encourage bloated prompts.

💡 Caching savings: Prompt caching can reduce costs 50-90% for repetitive workloads. Some providers offer this, some don't.

💡 Rate limit costs: Hitting rate limits means queuing, retries, or provisioning multiple accounts — all hidden costs.

"We switched from GPT-4o to DeepSeek Chat for our classification pipeline. Same accuracy, 12x cheaper per task. We only discovered this because we benchmarked on our actual data — the per-token prices didn't tell this story."

Best Value Models by Use Case

Budget picks (< $1/M output tokens)

🏆 Best overall value: DeepSeek Chat — capable at rock-bottom prices ($0.28/$0.42 per M)

🏆 Fastest budget model: Gemini 2.5 Flash-Lite — sub-100ms latency, 1M context, great for real-time apps

🏆 Cheapest reasoning: GPT-5 Nano — reasoning-capable at just $0.05/$0.40 per M with 400K context

Performance picks ($1–$15/M output tokens)

🏆 Best for coding: Claude Sonnet 4.5 — top coding benchmark scores with extended thinking

🏆 Best generalist: GPT-5 series — strong reasoning, 400K context, broad capabilities

🏆 Best context window: Gemini 2.5 Pro — 1M tokens for massive document processing

These are general patterns — your mileage will vary. A model that's "best value" for customer support might be terrible value for your data extraction pipeline. The only way to know is to test.

For multi-step AI pipelines, benchmark each step to find the most cost-efficient model per task — routing simple steps to budget models like Gemini 3.1 Flash Lite ($0.25/M input) while reserving premium models for complex reasoning.

How to Find the Cheapest Model for YOUR Task

Instead of comparing pricing tables, benchmark models on your actual workload:

1️⃣ Define your task — write the actual prompt you'll use in production, with example inputs and expected outputs.

2️⃣ Select models across tiers — test budget, standard, AND premium models. Use OpenMark's Smart Pick to auto-select a representative set.

3️⃣ Run the benchmark — OpenMark tracks real API costs per task (not estimates), including actual token usage per model.

4️⃣ Sort by cost-per-task — filter out models below your accuracy threshold, then sort by cost. You might find a budget model that matches premium quality for your specific use case.

Many OpenMark users discover that a model 10x cheaper delivers the same accuracy for their specific task. You won't find that in a pricing table.

Pricing FAQ

What's the cheapest AI model in 2026?