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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 analysis Pricing comparison Savings tips
Compare pricing → Cost saving tips

Lowest Priced Models

$/M tokens
GLM-5
$0.5/$0.5
DeepSeek V3
$0.27/$1.1
MiniMax M2.5
$0.5/$2
Kimi K2.5
$2/$10
Gemini 3.1 Pro Preview
$2/$12

LLM Pricing Comparison

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

Model Input Price Output Price Overall Score Context Est. Cost/1M Tasks Key Strengths
GLM-5
Zhipu AI
 $0.5 $0.5 8.5/10 128K ~$0.80 Ultra-low pricing • Enterprise ready • Good Chinese support
DeepSeek V3
DeepSeek
 $0.27 $1.1 8.7/10 64K ~$0.95 Best value performance • Strong coding • Fast responses
MiniMax M2.5
MiniMax
 $0.5 $2 8.3/10 245K ~$1.50 Low cost • Multimodal support • Fast inference
Kimi K2.5
Moonshot
 $2 $10 8.9/10 200K ~$5.00 Great value • Strong reasoning • Large context
Gemini 3.1 Pro Preview
Google
 $2 $12 8.4/10 1M ~$7.00 Prompt caching • Massive context • Good value
Claude Opus 4.7
Anthropic
 $5 $25 9.4/10 1M ~$15.00 Best quality • Prompt caching • Complex tasks

Real Cost Scenarios

Estimated cost per task at different complexity levels.

Scenario Tokens/Task GLM-5 DeepSeek MiniMax Claude
Simple classification 500 $0.0003 $0.0003 $0.0004 $0.02
Short code generation 2,000 $0.001 $0.001 $0.002 $0.09
Long document analysis 10,000 $0.005 $0.006 $0.01 $0.45
Complex multi-turn chat 50,000 $0.03 $0.03 $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 V3

Best balance of low cost and strong performance. $0.27/$1.10 per million tokens with 8.7/10 quality.

Enterprise on a budget

GLM-5

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

Need quality + reasonable cost

Kimi K2.5

8.9/10 overall score at $2/$10 per million. Best quality-per-dollar for most workloads.

Long context + caching

Gemini 2.5 Pro

Prompt caching can reduce costs 50-90% on repeated contexts. 1M context for large documents.

Quality matters more than cost

Claude Opus 4.7

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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GLM-5 offers low pricing at $0.50 per million tokens for both input and output. DeepSeek V3 is close at $0.27/$1.10 with strong performance. Both are dramatically cheaper than Claude Opus 4.7 ($5/$25) or GPT-5.5 ($5/$30).

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.

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