2026 LLM Pricing Master Table
All prices are per million tokens (1M tokens ≈ 750,000 words ≈ 3,000 pages of text). Input and output tokens are priced separately because output generation is computationally more expensive.
Frontier Models (Highest Capability)
Efficient Models (Best Value)
Use our LLM Cost Calculator to estimate your monthly costs based on your specific token usage patterns, including prompt caching and batch discounts.
Frontier Models Deep Dive: GPT-4o vs Claude Sonnet vs Gemini Pro
The three major frontier model families have distinct pricing philosophies and cost structures in 2026:
OpenAI GPT-4o: The Benchmark Standard
GPT-4o at $2.50/1M input + $10.00/1M output tokens remains the industry benchmark. Its pricing is straightforward, with a 50% discount for cached prompts ($1.25/1M). For applications with high cache hit rates, effective GPT-4o costs can approach $1.50–$2.00/1M effective tokens.
GPT-4o also offers a batch API that provides a 50% discount for non-real-time workloads — making it attractive for background processing, data extraction, and batch analysis tasks where latency is not critical.
Anthropic Claude Sonnet: Highest Output Cost, Best Cache Economics
Claude Sonnet 4 has the highest output token price ($15/1M vs GPT-4o's $10/1M) — a significant disadvantage for output-heavy applications. However, Claude's prompt caching is dramatically cheaper: $0.30/1M cached tokens vs GPT-4o's $1.25/1M.
This means for applications with large system prompts (e.g., codebases, long documents, extensive context) that benefit heavily from caching, Claude can be substantially cheaper than GPT-4o at scale. A Claude application with a 90% cache hit rate on a 100K-token system prompt can reduce costs by 70%+ compared to non-cached pricing.
Google Gemini 2.0 Pro: Most Competitive Base Pricing
Gemini 2.0 Pro at $1.25/1M input + $5.00/1M output offers the most competitive frontier model pricing. Gemini also offers a 1M token context window natively — significantly larger than GPT-4o's 128K and Claude's 200K windows.
For applications requiring very long context (processing large codebases, lengthy documents, extended conversations), Gemini's combination of competitive pricing and massive context window makes it particularly attractive.
When to Use Each Frontier Model
Efficient Models: Best Price-Performance Ratio in 2026
For many production applications, frontier models are overkill. The efficient tier — models that offer 70–85% of frontier model capability at 5–20% of the cost — should be your default choice unless you specifically need frontier-level capability.
Gemini Flash 2.0: The New Cost Leader
Gemini Flash 2.0 at $0.10/1M input + $0.40/1M output is arguably the most transformative model in the 2026 market. At these prices, processing 1 billion tokens (roughly 750 million words) costs just $100–$400. For high-volume applications, the economics are revolutionary.
Gemini Flash 2.0 performs comparably to GPT-4o on many benchmarks while costing 25x less on input tokens. For non-creative tasks like classification, extraction, summarization, and Q&A, it is often the clear first choice.
GPT-4o mini: OpenAI's Value Tier
GPT-4o mini at $0.15/1M input + $0.60/1M output offers excellent performance on reasoning and coding tasks at a fraction of GPT-4o's cost. It is the model powering most cost-sensitive OpenAI API applications in 2026.
Claude Haiku 3.5: Fast and Affordable for Agentic Tasks
At $0.80/1M input + $4.00/1M output, Claude Haiku 3.5 is more expensive than Gemini Flash and GPT-4o mini but delivers better performance on instruction-following and tool use — two capabilities that matter enormously for agentic applications. For multi-step agent workflows where reliability matters, Haiku 3.5 often delivers better total cost-of-reliability than cheaper alternatives.
DeepSeek V3: The Wildcard
DeepSeek V3 at $0.27/1M input + $1.10/1M output delivers performance that matches or exceeds many frontier models at less than 10% of their cost. The catch: it is a Chinese model, which raises data residency and compliance concerns for many enterprise applications. For research, personal projects, and non-sensitive applications, it is exceptional value.
Open Source LLMs: Llama, Mistral, and Qwen Pricing in 2026
Open source models hosted on inference providers offer competitive alternatives to proprietary APIs:
Self-hosting open source models on GPU infrastructure offers the lowest per-token cost at scale but requires significant upfront infrastructure investment and ongoing engineering maintenance. The break-even point vs. managed APIs is typically around $10,000–$50,000/month in API costs.
Prompt Caching: The Biggest LLM Cost Reducer in 2026
Prompt caching is the single most impactful cost reduction technique for most production LLM applications in 2026. Here's how it works and how to maximize its benefit:
What Is Prompt Caching?
When you send the same beginning of a prompt (system prompt, context, examples) repeatedly across many API calls, prompt caching stores the KV cache of that prefix and reuses it — dramatically reducing both latency and cost for the cached portion.
Caching Economics by Provider
Real-World Caching Impact
For a coding assistant with a 50K-token system prompt (codebase context), processing 10,000 requests per day:
- Without caching: 50K tokens × 10,000 requests = 500M tokens/day = ~$1,500/day (Claude Sonnet)
- With 90% cache hit rate: (5M new tokens × $3.00 + 495M cached tokens × $0.30) = $15 + $148.50 = $163.50/day
- Daily savings with caching: $1,336.50 (89% cost reduction)
Caching requires careful engineering: prompts must have stable prefixes, cache TTLs must be understood (Anthropic caches for 5 minutes to 1 hour depending on configuration), and cache warm-up costs must be accounted for.
Batch Processing vs. Real-Time APIs: The 50% Discount
For non-time-sensitive workloads, batch processing APIs offer substantial discounts:
- OpenAI Batch API: 50% discount on all models, results within 24 hours
- Anthropic Message Batches: 50% discount, results within 24 hours
- Google Batch Predictions: 50% discount on Gemini models
Ideal batch workloads include: nightly data processing, bulk document classification, content generation for SEO pages, product description generation, and any workflow where you can tolerate hours of latency.
Combining batch processing with prompt caching can reduce LLM costs by 70–95% compared to real-time uncached API calls — making many previously cost-prohibitive AI applications economically viable.
How to Estimate Your Monthly LLM API Costs
Use this framework to project your monthly LLM costs accurately:
Step 1: Measure Your Token Profile
For 100 typical requests in your application, measure: average input tokens, average output tokens, system prompt size, and how often the system prompt changes. Most applications have a consistent token profile that can be measured from a sample.
Step 2: Estimate Cache Hit Rate
If your system prompt is stable across many requests, your cache hit rate for those tokens can be 80–95%. If every request has a unique context, cache benefit is minimal.
Step 3: Apply the Formula
Monthly cost = (Requests/month × ((uncached_tokens × input_price) + (cached_tokens × cache_price) + (output_tokens × output_price))) / 1,000,000
Our LLM Cost Calculator does this automatically — enter your token profile and get a monthly cost breakdown across all major models with the optimal model recommendation for your use case.
Frequently Asked Questions
Which LLM has the cheapest API pricing in 2026?
For absolute lowest cost, Gemini Flash 8B at $0.0375/1M input + $0.15/1M output is the cheapest major model API. For the best price-to-performance ratio, Gemini Flash 2.0 ($0.10/$0.40 per 1M) and GPT-4o mini ($0.15/$0.60 per 1M) are the leading choices. Self-hosted open source models can be cheaper at sufficient scale.
Is Claude Sonnet more expensive than GPT-4o?
Claude Sonnet 4 has higher output token prices ($15/1M vs GPT-4o's $10/1M) but a dramatically cheaper cache price ($0.30/1M vs $1.25/1M). For cache-heavy applications, Claude is often cheaper in practice despite the higher list prices. For low-cache-rate output-heavy applications, GPT-4o is cheaper.
How much has LLM pricing changed since 2023?
LLM pricing has fallen dramatically. GPT-4 in 2023 cost $30/1M input + $60/1M output. By 2026, GPT-4o — a significantly more capable model — costs $2.50/$10 per 1M tokens, an 88–83% price reduction over 3 years. The trend of 50–70% annual price reductions has continued consistently.
What is a token in LLM pricing?
A token is roughly 3/4 of a word or about 4 characters of English text. 1,000 tokens ≈ 750 words ≈ 3 pages of text. Input tokens are the text you send to the model (prompt + context); output tokens are the text the model generates in response. Output tokens are typically 3–5x more expensive than input tokens because generation is computationally intensive.
Calculate Your Exact LLM API Costs
Enter your token usage profile to get a monthly cost estimate across all major models — with prompt caching and batch pricing included.