Cheapest LLM APIs & Cost Optimization Guide

Find the most cost-effective AI models for your workload. Compare pricing, calculate real costs, and learn strategies to reduce your AI spend by up to 90%.

Cost analysisPricing comparisonSavings tips

Lowest Priced Models

$/M tokens
GLM-5
$0.5/$0.5
DeepSeek V4 Pro
$0.66/$1.98
MiniMax M2.5
$0.5/$2
Kimi K3
$3/$15
Gemini 3.8 Flash
$0.75/$3.75

LLM Pricing Comparison

Input/output pricing per million tokens, performance scores, and estimated cost per 1M tasks.

ModelInput PriceOutput PriceEditorial Fit ScoreContextEst. Cost/1M TasksKey Strengths
GLM-5
Zhipu AI
$0.5$0.58.5/10128K~$0.80Ultra-low pricing • Enterprise ready • Good Chinese support
DeepSeek V4 Pro
DeepSeek
$0.66$1.989.1/101M$0.66 / $1.98 off-peakPeak/off-peak pricing • Hybrid thinking • 1M context
MiniMax M2.5
MiniMax
$0.5$28.3/10245K~$1.50Low cost • Multimodal support • Fast inference
Kimi K3
Moonshot
$3$158.9/101M~$7.50Configurable reasoning • Native visual understanding • 1M context
Gemini 3.8 Flash
Google
$0.75$3.759.1/101.05M~$2.25Prompt caching • 1,048,576-token context • Introductory pricing through Dec. 31, 2026
Claude Opus 4.8
Anthropic
$5$259.4/101M~$15.00Best quality • Prompt caching • Complex tasks

Real Cost Scenarios

Estimated cost per task at different complexity levels.

ScenarioTokens/TaskGLM-5DeepSeekMiniMaxClaude
Simple classification500$0.0003Use current input/output mix$0.0004$0.02
Short code generation2,000$0.001Use current input/output mix$0.002$0.09
Long document analysis10,000$0.005Use current input/output mix$0.01$0.45
Complex multi-turn chat50,000$0.03Use current input/output mix$0.05$2.25

Cost Saving Strategies

Proven tactics to reduce your AI API spend.

Use prompt caching

Claude and Gemini offer caching that reduces costs 50-90% on repeated system prompts and context.

Up to 90% savings

Right-size your model

Use cheaper models for simple tasks. Reserve Claude/GPT-4 for complex reasoning and coding.

60-80% savings

Optimize token usage

Shorter prompts, structured output, and removing unnecessary context reduces costs significantly.

30-50% savings

Batch similar requests

Combine similar tasks into single API calls to reduce overhead and improve efficiency.

20-40% savings

Monitor and retry smarter

Track failure rates by model. Sometimes a more expensive model that works first try is cheaper.

10-30% savings

When to Use Which Model

Decision guide for balancing cost vs quality.

High-volume production API

DeepSeek V4 Pro

Strong balance of cost, 1M context, and agent features. Peak pricing is $1.32/$3.96 per MTok; off-peak pricing is half.

Enterprise on a budget

GLM-5

Lowest pricing at $0.50/$0.50 with solid 8.5/10 performance. Enterprise features included.

Need 1M context with configurable reasoning effort

Kimi K3

Kimi lists a 1M-token context window and $3/$15 per million input/output pricing.

Long context + caching

Gemini 3.8 Flash

It supports caching and a 1,048,576-token input limit; standard pricing is $0.75/$3.75 per MTok through December 31, 2026.

Quality matters more than cost

Claude Opus 4.8

Best quality at 9.4/10. Use for critical tasks where accuracy justifies the premium.

Frequently Asked Questions

What is the cheapest LLM API in 2026?

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Pricing varies by provider and workload. DeepSeek V4 Pro lists peak pricing of $1.32/$3.96 per million input/output tokens and off-peak pricing of $0.66/$1.98. Compare current official price pages before committing production traffic.

How do I calculate my AI API costs?

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Cost = (input tokens × input price + output tokens × output price) / 1,000,000. Track your average tokens per task, multiply by your volume, and compare across models. Our cost scenarios table shows real examples.

Is it worth paying more for a better model?

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Sometimes yes. If a cheaper model requires 3 retries or produces unusable output, a more expensive model that works first try can be cheaper overall. Track success rates and retries, not just token costs.

What is prompt caching and how much does it save?

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Prompt caching stores processed prompt prefixes so repeated context doesn't need reprocessing. Claude and Gemini offer this. Savings range from 50-90% when your requests share common system prompts or documentation.

Should I use different models for different tasks?

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Yes. Route simple classification to GLM-5 or MiniMax, standard coding to DeepSeek, and complex architecture decisions to Claude. This "model routing" approach typically saves 40-60% vs using one premium model for everything.

See Full Model Scorecards

Review source-linked model data, editorial fit notes, and cost analysis before routing production traffic.

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