LLM API Pricing Comparison 2025: Every Major Model Ranked by Cost

Why API pricing matters more than ever

LLM API costs are now a top-three line item for most AI-powered products. As adoption scales from prototypes to production, what started as a manageable $50/month experimentation budget can balloon into thousands of dollars before anyone notices. The difference between choosing the right model and defaulting to the most familiar one is often a 10x or even 80x cost difference per request.

The challenge is that pricing structures vary dramatically across providers. Some charge more for input tokens, others weight output tokens heavily. Context window sizes differ. Speed varies. And the cheapest model is not always the worst -- in many cases, a model costing 90% less performs identically for straightforward tasks.

This guide breaks down the current API pricing landscape for every major model, calculates real costs across common use cases, and shows you how to minimize spend without sacrificing output quality.

The full pricing table

Here is a side-by-side comparison of the six most widely used LLM APIs as of early 2025. All prices reflect standard API access (not batch or cached pricing tiers).

Several things stand out immediately. The spread between the cheapest and most expensive models is enormous: Gemini 3 Flash input tokens cost $0.10 per million, while Claude Sonnet 4.5 and Grok 3 charge $3.00 -- a 30x difference. On the output side, the gap is even wider: $0.40 vs $15.00, a 37.5x spread.

This does not mean you should route everything through Gemini 3 Flash. It means you need to understand what each dollar buys you, and whether your specific use case demands frontier-model capability or not.

For detailed per-model breakdowns, see our GPT-5 API pricing analysis and Claude API pricing guide.

Cost per use case: Real numbers

Abstract per-token pricing is hard to reason about. Here is what each model actually costs across four common use cases, calculated from the token counts you would see in production.

Simple chat (500 input tokens, 200 output tokens)

A typical single-turn chatbot interaction -- a user question and a model response.

For simple chat, Gemini 3 Flash costs $1.30 per month for 10,000 requests. GPT-5.2 costs $32.50 for the same volume -- 25x more. If your chatbot handles straightforward Q&A, classification, or extraction, the frontier models are overkill.

Code generation (1,000 input tokens, 2,000 output tokens)

A developer prompt with context, generating a function or code block.

Code generation is output-heavy, which means the output token price dominates. Claude Sonnet 4.5 and Grok 3 are the most expensive here because of their $15/1M output rate. GPT-5.2 is noticeably cheaper despite being a frontier model, thanks to its lower $10/1M output pricing. For code where quality matters, GPT-5.2 offers the best cost-to-quality ratio among frontier models. For boilerplate and simple code, DeepSeek V3 and Llama 4 Maverick deliver solid results at a fraction of the price.

Document summarization (10,000 input tokens, 500 output tokens)

Summarizing a long document, report, or article. Input-heavy workload.

Summarization is where Gemini 3 Flash dominates. Its $0.10/1M input pricing combined with a 1M token context window makes it the clear choice for processing large documents. You can feed it an entire book-length document in a single request for less than a penny. Claude Sonnet 4.5 has the second-largest context window at 200K, but at 30x the input cost.

Batch processing at scale (1M requests/month)

The numbers change significantly at high volume. Here is what each model costs for 1 million monthly requests at the simple chat profile (500 input, 200 output tokens per request):

At one million requests per month, the model choice is a $130 vs $4,500 decision. That is the difference between a rounding error and a material budget line item. Even within frontier models, GPT-5.2's $3,250 is 28% cheaper than Claude Sonnet 4.5 or Grok 3 at $4,500. For our full ranking of models by price-to-value ratio, see our cheapest LLM API comparison.

Free and near-free options

Not every model requires a significant financial commitment. Several options are effectively free for low-to-moderate usage:

Gemini 3 Flash offers the lowest paid pricing of any major model. At $0.10 per million input tokens, processing 10,000 typical requests costs about $1.30. For prototyping, internal tools, and low-traffic applications, this is close enough to free that it barely registers on a budget.

Llama 4 Maverick is available through multiple inference providers at varying prices. Because it is an open-weight model, some providers offer free tiers or heavily subsidized pricing to attract users to their platforms. Self-hosting eliminates per-token costs entirely, though you take on infrastructure costs instead.

DeepSeek V3 sits in a similar price bracket, offering strong multilingual and reasoning capabilities at roughly one-tenth the cost of frontier models.

LLMWise includes access to free-tier models alongside premium options. You can start with zero-cost models and only pay when you need frontier-model quality -- no upfront commitments, no minimum spend.

How to reduce your API costs

Knowing the prices is step one. Reducing your actual spend requires deliberate architecture decisions.

Use cheaper models for simple tasks

The single highest-impact optimization is tiered routing. If 60% of your requests are straightforward -- classification, extraction, simple Q&A, translation -- routing those to Gemini 3 Flash instead of GPT-5.2 cuts that portion of your bill by over 95%. Most teams find that the quality difference on simple tasks is negligible. For an in-depth walkthrough of this strategy, see our guide to reducing LLM API costs.

Let Auto mode pick the cost-effective model

Manual tiering works but requires ongoing maintenance. LLMWise's Auto mode classifies each request at zero additional latency and routes it to the cheapest model that can handle the task well. Code goes to the strongest code model, translations go to the cheapest capable model, and complex reasoning goes to frontier models. You get intelligent routing without building and maintaining a classifier yourself.

Bring Your Own Key for volume discounts

If you are sending high volume to a single provider, direct API contracts often come with volume discounts, committed-use pricing, or batch processing rates that are significantly cheaper than pay-as-you-go. LLMWise's BYOK feature lets you plug in your own provider keys and route directly to the provider with zero markup, while still using LLMWise for orchestration, failover, and observability.

Optimize your prompts

Token count drives cost. Three concrete steps that pay for themselves immediately:

• Trim system prompts. A 2,000-token system prompt repeated on every request adds up. Cut it to 500 tokens and save 75% on that overhead.
• Set explicit max_tokens. Without a cap, models generate until they hit the context limit. If you need 200 words, set max_tokens: 300.
• Compress conversation history. Summarize earlier turns instead of sending the full thread. A 20-turn conversation can exceed 10K tokens; a summary captures the context in under 500.

Accessing all models through one API

The biggest hidden cost in multi-model architectures is not the API pricing itself -- it is the engineering overhead of maintaining separate integrations, handling different error formats, managing multiple API keys, and building your own failover logic. Most teams default to one provider not because it is optimal, but because supporting multiple providers is too much work.

LLMWise eliminates that overhead with a single API endpoint that routes to every model in the table above, plus others. You send one request, specify the model (or let Auto mode choose), and LLMWise handles the rest: provider routing, streaming, failover via circuit breakers, and unified billing.

Key advantages of the unified approach:

• One integration, nine models. Switch models by changing a single parameter. No new SDKs, no new authentication flows.
• Credit-based billing. Pay per use across all providers with one balance. No separate subscriptions, no minimum commitments.
• Built-in failover. If one provider goes down, Mesh mode automatically routes to the next available model in your fallback chain.
• Compare before you commit. Run the same prompt against multiple models side by side in Compare mode to validate which model performs best for your use case.
• BYOK when it makes sense. Use LLMWise credits for convenience on low-volume routes, and your own keys for high-volume endpoints where direct pricing is cheaper.

The right LLM for your application is not always the same model for every request. With unified access to every major provider, you can match each task to its most cost-effective model -- and stop overpaying for work that does not need a frontier model.