LLM API Pricing Comparison 2026: Which Is Cheapest—GPT, Claude, Gemini, or DeepSeek?

LLM API pricing still varies dramatically in 2026. Processing the same number of input and output tokens can cost several times more on one model than another. For enterprises, however, the model with the lowest price per million tokens is not necessarily the one with the lowest real-world cost.

Among representative high-capability models, GPT-5.6 Sol is priced at $4 per million input tokens and $20 per million output tokens, while Claude Sonnet 5 costs $2 and $10 respectively. Gemini 3.1 Pro Preview starts at $2 per million input tokens and $12 per million output tokens for prompts up to 200K tokens. DeepSeek V4-Pro is considerably cheaper during off-peak hours, with cache-miss input at about $0.66 per million tokens and output at about $1.98 per million.

On base token pricing alone, DeepSeek V4-Pro currently has a clear cost advantage among these high-capability models. But once lower-cost model tiers are included, models such as GPT-5.6 Luna and Gemini 3.1 Flash-Lite also reach very low price points.

The more useful comparison therefore needs to consider Input Cost, Output Cost, Cached Input, Long-Context Pricing, Batch Discounts, and Cost per Successful Task.

How Does LLM API Pricing Work in 2026?

Most major LLM APIs charge according to Token Usage, where a token is roughly three-quarters of an English word. At its simplest, teams need to document both input and output token usage for accurate billing, and API cost can be calculated from the input rate and output rate:

API Cost = Input Tokens × Input Price + Output Tokens × Output Price

Suppose a request contains 100,000 input tokens and generates 20,000 output tokens. If the model costs $2 per million input tokens and $10 per million output tokens, the total cost would be:

100K × $2/M + 20K × $10/M = $0.40

In practice, output tokens cost much more than input tokens, with output tokens cost often running about 5x higher at Anthropic and 6x at OpenAI. Production workloads are usually more complicated.

Some providers offer large discounts for Cached Input. Long-context requests may trigger higher rates, while Batch Processing can reduce costs for asynchronous workloads. Reasoning models may also consume additional reasoning tokens, and tools such as Web Search or Computer Use can introduce separate charges.

A more realistic cost model for llm pricing is therefore based on token counts, including each input and output token, plus other usage drivers:

Total Cost = Input + Cached Input + Output / Reasoning + Tools + Retrieval + Retries

For Agent applications, another variable becomes particularly important: how many model calls and Agent Steps are required to complete one successful task.

GPT vs. Claude vs. Gemini vs. DeepSeek: How Much Do Their APIs Cost?

A useful starting point is to compare representative high-capability models: GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro Preview, and DeepSeek V4-Pro.

The following figures represent public API pricing around early September 2026 and are shown in USD per 1 million tokens; the list price and published price are useful starting points for comparison by model name, but real costs still depend on workload.

Input prices across the market span roughly a 50x range, from $0.10 to $5.00 per million tokens, which is why comparing different models against a clear pricing table matters.

• For DeepSeek, standard input in this table refers to cache-miss input. The USD figures are approximate conversions from DeepSeek’s official CNY pricing and can vary with exchange rates.

Among these high-capability models, DeepSeek V4-Pro has the lowest base token pricing. Claude Sonnet 5 and Gemini 3.1 Pro sit in the middle, while GPT-5.6 Sol and Claude Opus 5 have higher output prices.

That still does not mean DeepSeek is always the cheapest option in production. Model capability, Token Consumption, Latency, retries, and Task Completion Rate can all change the final economics.

How Much Does the GPT API Cost?

OpenAI’s GPT-5.6 family provides multiple model tiers rather than a single price point.

GPT-5.6 Sol is the flagship option for complex professional workloads. Where gpt 5.4 served as an earlier flagship reference point, GPT-5.6 now anchors the current pricing tiers. Its current promotional pricing is $4/M Input, $0.40/M Cached Input, and $20/M Output.

OpenAI also offers lower-cost tiers. GPT-5.6 Terra targets a balance between intelligence and cost, while GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads.

OpenAI’s July 30, 2026 price changes included an 80% cut to GPT-5.6 Luna rates.

This makes the statement "GPT API is expensive" too simplistic.

Using Sol for every request can be relatively expensive, but classification, extraction, simple summarization, and large-scale content processing may not require a flagship model, especially as new models keep reshaping the tradeoffs.

A production architecture could instead use:

Simple Task → LunaMedium Task → TerraComplex Task → Sol

The real optimization target is therefore not the price of one GPT model, but whether the cheapest model still meets quality needs and delivers the best value for the full workload.

Why Does GPT-5.6 Sol Long-Context Pricing Matter?

GPT-5.6 Sol offers a context window of roughly 1.05 million tokens, but not every request is billed at the base rate.

When input exceeds 272K tokens, Long-Context Pricing applies to the entire request. Input and Cached Input are charged at 2× the standard rate, while Output is charged at 1.5× the standard rate.

That effectively changes the standard rates from:

$4/M Input + $20/M Output

to approximately:

$8/M Input + $30/M Output

for qualifying long-context requests.

This is particularly important for Large Repositories, Legal Documents, Long-Document Analysis, or Agent systems that continuously accumulate very large contexts.

A 1M-token Context Window does not mean an application should always send 1M tokens. In many cases:

Knowledge Base → Retrieval → Relevant Context → LLM

can be substantially more cost-efficient than repeatedly sending an entire corpus. Cutting prompt length by 50% will usually cut input spend by about 50% too, so prompt length is often the main driver of long-context cost.

How Much Does the Claude API Cost?

Anthropic api access includes multiple pricing tiers for this comparison, not one Claude price: two important tiers here are Claude Sonnet 5 and Claude Opus 5.

Claude Sonnet 5 costs $2/M Input and $10/M Output, positioning it as a strong balance between capability and production cost.

Claude Opus 5 sits in a higher-priced tier at $5/M Input and $25/M Output. It is better suited to workloads where additional model capability can justify higher inference costs, such as complex Coding, demanding Agent tasks, and high-value professional workflows. In practice, that premium only makes sense when it delivers better value on complex work rather than simply being the higher tier.

The two models therefore address different economics:

Sonnet 5 → General Agentic Workloads / Coding / Knowledge Work

Opus 5 → High-Value Coding / Complex Agents / Professional Work

For large-scale production workloads, Sonnet 5 may offer more manageable unit economics. Opus 5 makes more sense when improvements in Task Completion or output quality have substantial business value, and a 50% reduction in prompt length equals 50% savings.

How Much Can Claude Prompt Caching and Batch Processing Save?

Anthropic’s cost optimization options extend beyond model selection.

Prompt Caching can significantly reduce the cost of repeatedly sending the same System Prompt, instructions, or knowledge context. Anthropic has indicated that caching can reduce prompt-related costs by up to roughly 90% in applicable scenarios, while Batch Processing can offer discounts of up to 50%.

Consider a Customer Service Agent where every request contains:

System Prompt + Company Policy + Product Knowledge + User Query

If the first three components remain largely unchanged, reusing the same text is where Prompt Caching helps most.

Batch Processing is useful for workloads that do not require an immediate response, such as overnight document processing, Dataset Labeling, Content Classification, and large-scale Evaluation. Most teams should monitor costs per model because cache hit rates and batch usage can materially change spend.

This means comparing Claude purely through the headline $2/$10 or $5/$25 rates can underestimate the impact of caching and asynchronous processing.

How Much Does the Gemini API Cost?

Google Gemini uses a more segmented pricing structure for Gemini.

For Gemini 3.1 Pro Preview, prompts of up to 200K tokens are priced at $2/M Input and $12/M Output. Above 200K tokens, pricing increases to $4/M Input and $18/M Output.

Context Caching is also tiered:

Gemini’s caching economics also need to account for Context Cache Storage fees. This matters when large cached contexts are retained for extended periods.

Another important feature is the availability of different processing tiers. Providers also often offer free tiers or trial credits for early testing before paid usage scales. Batch and other lower-priority processing modes can substantially reduce costs compared with Standard requests.

For workloads that do not require immediate responses, this pricing structure can make a major difference.

Is Gemini Flash-Lite Better Than Pro for High-Volume AI Applications?

If the goal is to minimize API cost, comparing only flagship or Pro models is not enough.

Gemini’s Flash-Lite tier is designed for high-volume, cost-sensitive workloads such as Translation, Classification, Data Processing, and simpler Agent tasks.

At current pricing, Gemini 3.1 Flash-Lite can cost approximately:

$0.25/M Input + $1.50/M Output

with Batch or Flex-style processing potentially reducing those rates further. Free tiers or trials are commonly offered for early testing by providers. The cheapest model is only the best option if its quality clears your threshold.

This can make Flash-Lite the best value for simple workloads when cost matters more than top-end capability.

A simple routing strategy could be:

High Complexity → Gemini ProHigh Volume / Simple Task → Gemini Flash-Lite

For applications processing billions of tokens, selecting the right model tier can matter more than selecting the Provider itself.

Why Is the DeepSeek API So Cheap?

DeepSeek V4-Pro uses Peak and Off-Peak pricing.

During Off-Peak periods, its approximate USD rates are:

$0.022/M Cache-Hit Input$0.66/M Cache-Miss Input$1.98/M Output

DeepSeek V4 Flash sits below it as the lower-cost fast tier in the broader family.

Peak pricing is approximately:

$0.044/M Cache-Hit Input$1.32/M Cache-Miss Input$3.96/M Output

Even at Peak rates, DeepSeek V4-Pro remains substantially cheaper on a per-token basis than GPT-5.6 Sol, Claude Sonnet 5, and Gemini 3.1 Pro.

DeepSeek V4-Pro also offers a 1M-token Context Window, supports Thinking and Non-Thinking modes, and provides Tool Calls and Responses API support. It is therefore not simply a lightweight model optimized only for low-cost generation.

Time-based pricing also creates additional optimization opportunities, and for some workloads it matters more than the sticker price. Batch document processing, code analysis, or synthetic data generation can potentially be scheduled during lower-cost periods.

Production systems still need to consider Task Accuracy, Latency, Availability, Rate Limits, and Provider Infrastructure. Token Price alone should not determine model selection.

How Much Would the Same Task Cost Across Different LLM APIs?

Assume one task produces these token counts:

100,000 Input Tokens + 20,000 Output Tokens

Ignoring Caching, Batch Discounts, Tool Costs, and Retries, the base API cost would look approximately like this:

The same task can still produce different token counts because providers tokenize the same text differently.

Under these assumptions, DeepSeek V4-Pro has the lowest base cost among the high-capability models compared.

But the result changes when cost-efficient tiers are included.

For the same workload, GPT-5.6 Luna would cost approximately:

$0.02 + $0.024 = $0.044

A Gemini 3.1 Flash-Lite request at $0.25/M Input and $1.50/M Output would cost approximately:

$0.025 + $0.03 = $0.055

DeepSeek V4 Flash would come in at $0.028 + $0.0056 = $0.0336, since its output is priced at $0.28 per million tokens.

Your monthly cost depends on request volume and usage patterns, not just one-request math.

This illustrates an important distinction: "Which provider is cheapest?" and "Which model is cheapest?" are not the same question.

Why Doesn’t the Lowest Token Price Always Mean the Lowest Real Cost?

Suppose Model A has a lower published price than Model B, but Model A requires 20 Tool Calls to complete an Agent task while Model B completes it in eight.

If Model A also generates more Output Tokens, makes more failed attempts, and triggers more retries, its real cost advantage may disappear.

For Agent workloads, a more useful metric is:

Cost per Successful Task = Token Cost + Tool Cost + Agent Steps + Retries + Failed Attempts

A Coding Agent, for example, may follow a workflow such as:

Repository Search → Reasoning → Code Generation → Tool Call → Test → Error → Retry → Test

Some models consume more tokens and retries for the same job.

A more expensive model that completes the task correctly on its first attempt may ultimately cost less than a cheaper model that repeatedly fails.

Production LLM evaluation should therefore measure:

Task Completion Rate → Average Tokens → Agent Steps → Tool Calls → Latency → Cost per Successful Task → Best Value for Your Budget

This provides a much better picture of real AI economics than $/1M Tokens alone.

Which LLM API Is Cheapest in 2026?

The answer depends on whether you compare by provider, by model, or by completed task.

Among GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro, and DeepSeek V4-Pro, DeepSeek V4-Pro currently has the clearest base Token Price advantage.

The picture becomes more complicated when each Provider’s cost-efficient tiers are included. GPT-5.6 Luna and Gemini Flash-Lite can both reach extremely low prices for simpler workloads.

Qwen3.7 Flash appears to be the cheapest LLM API on headline input pricing, but it should still be tested for workload fit.

There is little reason to send every request to the strongest or most expensive model. Mature AI systems increasingly route requests according to task complexity.

How Can Gate.AI Help Optimize Multi-Model API Costs?

As pricing differences between Providers and model tiers grow, Cost can become a core Model Routing variable instead of forcing every request through a single LLM.

Through a unified multi-model access platform such as Gate.AI, developers can evaluate different models from OpenAI, Anthropic, Google, DeepSeek, and other Providers through a more consistent access layer while measuring token counts, Latency, Task Completion Rate, actual task cost, and llm costs per model.

A routing architecture might look like:

Request → Task Classification → Model Routing → Execution → Cost / Quality Evaluation

Most teams lack clear spend visibility until usage is attributed by model name.

Simple Classification, Extraction, or Translation workloads can be routed toward Cost-Efficient Models, while difficult Coding, Research, or Agent tasks can use more capable models.

The evaluation should therefore move beyond:

Price per 1M Tokens

and calculate:

Cost per Successful Task

This makes it possible to compare llm costs and determine whether a same model with a higher list price or published price actually delivers best value because models consume budgets differently, even when the advertised pricing table or api access rate looks attractive. For example, the cheapest LLM API is Qwen3.7 Flash at $0.03 input, and as of September 2026, Qwen3.7 Flash costs $0.03 input and $0.13 output, but the tokens bill can still vary by workload. A lightweight LLM API pricing calculator view can help forecast monthly cost before deployment.

Summary

LLM API pricing still varies widely in 2026. Among the high-capability models compared here, DeepSeek V4-Pro has the lowest base Token Price, while Claude Sonnet 5 and Gemini 3.1 Pro offer a more balanced capability-to-cost position. GPT-5.6 Sol and Claude Opus 5 are more focused on complex, high-value workloads.

For production AI, however, the real optimization target is Cost per Successful Task, not simply the lowest price per million tokens. Caching, Batch Processing, Model Routing, and selecting the right model tier can reduce total costs substantially.

FAQ

What is the cheapest LLM API in 2026?

Among GPT-5.6 Sol, Claude Sonnet 5, Gemini 3.1 Pro, and DeepSeek V4-Pro, DeepSeek V4-Pro currently has the lowest base Token Price. Its off-peak rates are approximately $0.66 per million cache-miss input tokens and $1.98 per million output tokens. Across the broader market, Qwen3.7 Flash currently has the lowest headline input list price at $0.03 per million tokens, though the published price for a given model name is only one part of LLM pricing and not always the best proxy for the cheapest model in production.

Which API is cheaper: GPT, Claude, or Gemini?

Among the high-capability models compared here, Claude Sonnet 5 costs $2/M Input and $10/M Output, Gemini 3.1 Pro starts at $2/M Input and $12/M Output, GPT-5.6 Sol costs $4/M Input and $20/M Output, and Qwen3.7 Flash costs $0.03 input and $0.13 output. To compare LLM costs accurately, check each provider’s input rate against its output rate, then account for your token mix across different models, because even the same model family—or the same model—can look cheaper or more expensive once caching, output mix, and context length are included.

Does GPT have cheaper API models than GPT-5.6 Sol?

Yes. GPT-5.6 Luna is designed for high-volume, cost-sensitive workloads and is priced much lower than Sol. OpenAI also cut GPT-5.6 Luna prices sharply in late July 2026, reinforcing its low-cost role. GPT-5.6 Terra provides a middle tier and often the best value between cost and capability. Using Model Routing can therefore substantially reduce average GPT API costs beyond the published price or list price, and you can compare by model name while watching for future price changes.

How much can Prompt Caching reduce LLM API costs?

The savings depend on the Provider and workload. Cached Input can be dramatically cheaper than standard Input when large prompts or knowledge contexts are repeatedly reused, and savings improve further when teams keep stable prompt reuse while trimming prompt length so the same text is sent more efficiently. Applications with stable System Prompts, instructions, or context can therefore benefit substantially from caching, though the final tokens bill still varies by tokenizer behavior and overall llm costs.

What is the best way to compare LLM API costs?

Enterprises should measure more than the price per million tokens. A production evaluation should include Task Completion Rate, Token Usage, Tool Calls, Agent Steps, Retry Rate, Latency, and Cost per Successful Task. For AI Agents in particular, this gives a much more realistic picture of total operating cost.