GPT-4o vs GPT-4o-mini vs Open Source: When to Use Each (2026 Pricing Guide)
Complete 2026 pricing comparison of GPT-4o, GPT-4o-mini, Claude, Gemini, and Llama 3. Per-token costs, when to use each model, and how to cut your LLM bill by routing intelligently.
NR
NeuralRouting Team
April 10, 2026
GPT-4o costs $2.50 per million input tokens. GPT-4o-mini costs $0.15. That''s a 16x price difference — and for most of your API calls, the output quality is identical.
This guide breaks down every major LLM''s pricing in 2026, shows you which model fits which task, and explains why routing by complexity is the single highest-leverage cost optimization you can make.
The 2026 LLM Pricing Table
All prices are per million tokens as of April 2026.
All requests on Llama 3.1 8B via Groq:
(10M × $0.05 + 5M × $0.08) / 1M = $0.50 + $0.40 = $0.90/day → $27/month
That''s an 83x cost difference. Obviously you can''t send everything to Llama 3.1 8B — some tasks need GPT-4o''s reasoning. But that''s exactly the point: most tasks don''t, and the savings from routing those to cheaper models are massive.
When to use each model
Use GPT-4o ($2.50/$10.00) when:
The task requires multi-step reasoning, nuanced understanding of context, complex code generation, or creative writing where subtle quality differences matter. Examples: legal document analysis, multi-turn debugging sessions, research synthesis, generating marketing copy that needs to match a specific tone precisely.
Roughly 20-30% of production LLM requests fall into this category.
Use GPT-4o-mini ($0.15/$0.60) when:
The task is moderate complexity: summarization, structured Q&A, template-based generation, sentiment analysis, basic code completion. The output quality for these tasks is functionally identical to GPT-4o at 1/16th the cost.
About 30% of requests fit here.
Use Llama 3.1 8B on Groq ($0.05/$0.08) when:
The task is simple and well-defined: text classification, entity extraction, date parsing, reformatting JSON, yes/no decisions, language detection. These are tasks where any competent model produces the same output.
This covers roughly 40% of production requests — and it costs 50x less than GPT-4o.
The routing sweet spot
A well-routed traffic mix looks like this:
Tier
Model
% of Requests
Monthly Cost (at 10M tokens/day)
Economy
Llama 3.1 8B
40%
~$11/month
Mid-tier
GPT-4o-mini
30%
~$135/month
Premium
GPT-4o
30%
~$675/month
Total
~$821/month
Compared to $2,250/month if everything hits GPT-4o, that''s a 64% reduction.
The question nobody asks
Here''s what most pricing guides miss: knowing the prices isn''t the hard part. The hard part is knowing which of your requests can safely use a cheaper model.
You need to analyze prompt complexity in real time — task type, reasoning depth, acceptable error tolerance — and route each request to the cheapest model that can handle it at equivalent quality. Then you need to validate that the cheaper model actually delivered.
This is what Model Cascading does. Simple requests cascade to economy models first. If the economy model''s confidence is low, the request escalates to the next tier. And the Shadow Engine runs background validation against premium models to make sure quality never silently degrades.
The result: your traffic automatically distributes across the optimal price-quality curve without you manually classifying every prompt type.
Batch API and caching: additional savings
Beyond model routing, two more optimizations stack on top:
Batch APIs. Both OpenAI and Anthropic offer 50% discounts for non-real-time workloads. If your pipeline can tolerate async processing (document analysis, nightly report generation, bulk classification), batch pricing cuts costs in half on top of any routing savings.
Semantic Caching. If you''re seeing repeated or near-identical prompts (common in customer support, FAQ, and templated workflows), caching responses at the semantic level eliminates the API call entirely. NeuralRouting''s 2-level Semantic Cache (exact match + vector similarity) saves 30-40% of calls.
Stack all three — routing + batching + caching — and the compounding effect is significant.
Calculate your savings
Stop guessing. Plug your numbers into the calculator and see exactly what your LLM bill would look like with intelligent routing.