LLM Pricing Map 2026: Visualizing AI API Costs Across 34 Models

May 29, 2026 · 6 min read · View Interactive Map

We plotted all 34 LLM API models on a single interactive chart. The result is a clear picture of where the value is, where the outliers are, and how to think about cost vs. capability in 2026.

Open the Interactive LLM Pricing Map to explore the data yourself. Below are the key insights.

The Pricing Landscape: Four Tiers

When you plot input cost against blended cost (average of input + output), four distinct clusters emerge:

Budget Tier: $0.075 – $1.00 per 1M tokens

This is where the volume lives. 16 models compete in this space:

Mid Tier: $1.00 – $5.00 per 1M input tokens

The workhorses. 12 models sit here, offering strong capability at reasonable prices:

Premium Tier: $5.00+ per 1M input tokens

The cutting edge. 5 models command premium pricing:

Outlier: xAI Grok 3

At $30/$150 per 1M tokens, Grok 3 is in a category of its own. On output tokens, it costs 6x more than GPT-5.5 Pro ($180) and 500x more than Gemini Flash Lite ($0.30). This is premium positioning for real-time X/Twitter data access.

Key Findings from the Pricing Map

1. The 100x Gap Between Cheapest and Most Expensive

The range is staggering. Gemini Flash Lite at $0.075/1M input vs. Grok 3 at $30/1M input is a 400x difference on input tokens alone. On output, the gap widens to 600x ($0.30 vs. $180).

What does this mean in practice? A developer sending 10M input tokens per month would pay:

2. Context Window Is the Hidden Variable

Bubble size on the pricing map represents context window. The differences are dramatic:

Model Context Input Cost Cost per 1M Context
Llama 4 Scout 10M $0.11/1M $0.011
Gemini 2.0 Flash 1M $0.10/1M $0.10
DeepSeek V4 Pro 1M $0.44/1M $0.44
Claude Sonnet 4.6 1M $3.00/1M $3.00
GPT-5.5 1M $5.00/1M $5.00
Claude 4 Opus 200K $15.00/1M $75.00

Llama 4 Scout offers 50x more context per dollar than Claude 4 Opus. For long-document processing, the savings are massive.

3. Provider Clustering Reveals Strategy

Each provider occupies a distinct position on the map:

4. The Output Token Tax

Output tokens consistently cost 2-6x more than input tokens. The ratio varies by provider:

If your workload is output-heavy (content generation, chatbots), the output ratio matters more than input pricing. Use the APIpulse Calculator to model both.

What This Means for Your Budget

The right model isn't the cheapest or the most expensive — it's the one that handles your task at the lowest cost per quality unit.

Three practical takeaways:

  1. Default to budget models. For 70-80% of tasks (summarization, extraction, simple Q&A), models under $1/1M tokens perform well enough. Start cheap, upgrade only when quality drops.
  2. Use model routing. Route simple requests to GPT-4o mini or Gemini Flash. Reserve GPT-5.5 or Claude Opus for complex reasoning. Our Routing Builder can model this.
  3. Watch the output ratio. If you're generating lots of text, pick models with low output-to-input ratios. DeepSeek and Llama are best here.
Explore the Interactive LLM Pricing Map

See all 34 models on one chart. Filter by provider, toggle log/linear scale, and click any model to learn more.

Open Pricing Map

Methodology

Data sourced from official provider pricing pages, verified May 29, 2026. Prices are per 1M tokens. "Blended cost" is the average of input and output pricing. Bubble size represents context window (logarithmic scale). Tier classification (Budget/Mid/Premium) is based on input pricing thresholds: Budget under $1, Mid $1-5, Premium over $5.

For the most up-to-date pricing, use the APIpulse Pricing Index or our free API.