LLM API pricing in 2026: the complete comparison
LLM pricing changes every few weeks. A model that cost $60/M output tokens last year costs $10 today. New providers undercut each other constantly. This page is our attempt to keep a single, updated reference.
Last updated: April 2026.
Frontier models
Section titled “Frontier models”The most capable models from each provider:
| Model | Input $/M | Output $/M | Context |
|---|---|---|---|
| Claude Opus 4.6 | $15.00 | $75.00 | 200K |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 200K |
| GPT-5.4 | $2.50 | $10.00 | 128K |
| Gemini 2.5 Pro | $1.25 | $10.00 | 1M |
| DeepSeek V3.2 | $0.27 | $1.10 | 128K |
| Qwen 3.5 397B | $0.40 | $1.20 | 128K |
| Mistral Large 3 | $2.00 | $6.00 | 128K |
The price spread is 55x between the cheapest (DeepSeek V3.2) and most expensive (Claude Opus 4.6) frontier model. The quality spread on MMLU-Pro is 6.5 points. That’s the opportunity.
Cost-efficient models
Section titled “Cost-efficient models”The sweet spot — models that handle 80% of tasks at a fraction of frontier prices:
| Model | Input $/M | Output $/M | Context |
|---|---|---|---|
| Claude Haiku 4.5 | $0.80 | $4.00 | 200K |
| GPT-4.1 mini | $0.40 | $1.60 | 1M |
| Gemini 2.5 Flash | $0.15 | $0.60 | 1M |
| Qwen 3.5 35B | $0.06 | $0.12 | 128K |
| Llama 3.1 8B | $0.02 | $0.05 | 128K |
Llama 3.1 8B at $0.02/M input is 750x cheaper than Claude Opus. It won’t write your authentication system, but it’ll classify intents, extract entities, and route requests just fine.
Real workload cost comparison
Section titled “Real workload cost comparison”Pricing per million tokens is hard to reason about. Here’s what actual workloads cost monthly:
Chatbot (50 conversations/day, ~2K tokens each)
Section titled “Chatbot (50 conversations/day, ~2K tokens each)”Agent workload (20 tasks/day, ~500K tokens each)
Section titled “Agent workload (20 tasks/day, ~500K tokens each)”The gap widens dramatically with agent workloads because context accumulation multiplies the per-token cost. Flat-rate pricing eliminates this entirely.
Per-token vs. flat-rate: when each makes sense
Section titled “Per-token vs. flat-rate: when each makes sense”Per-token is better when:
- Your usage is low and predictable (< $20/month)
- You’re prototyping and don’t know your volume yet
- You need a specific model not available on flat-rate platforms
Flat-rate is better when:
- You run agents with unpredictable token consumption
- Your monthly token bill exceeds the flat-rate plan cost
- You want cost certainty for budgeting
- You run multiple agents that need independent rate limits
The breakeven for flat-rate vs. per-token on DeepSeek V3.2 is roughly 40M tokens/month. An active agent does that in a week.
How to switch without rewriting code
Section titled “How to switch without rewriting code”Every provider listed in this article supports the OpenAI API format. Switching is a config change:
from openai import OpenAI
client = OpenAI( base_url="https://api.cheapestinference.com/v1", # or any provider api_key="sk-your-key")
response = client.chat.completions.create( model="deepseek/deepseek-chat-v3-0324", messages=[{"role": "user", "content": "Hello"}])Same SDK. Same methods. Same response format. Different price.
CheapestInference serves many models through a single OpenAI-compatible API. Flat-rate plans start at $10/month with per-key budget caps. Compare plans or see all models.