LLM API Pricing Compared: Every Major Provider's Cost Per Token in 2026
LLM API Pricing Compared: Every Major Provider’s Cost Per Token in 2026
The LLM API market in 2026 has more providers, more models, and more confusing pricing than ever. Prices have dropped 60-80% from 2024 levels for equivalent capability, but the spread between the cheapest and most expensive options for a given task is now 100x or more. Picking the wrong model for your use case doesn’t just affect quality — it can cost 10-50x more than necessary for equivalent results.
This comparison covers every major provider’s current pricing, estimates real per-task costs, and identifies where each provider offers the best value. If you’re spending on LLM APIs for development, this is the reference you need.
What are the current per-token prices for every major LLM?
The major LLM providers in 2026 charge between $0.10 and $75.00 per million tokens, with the price reflecting a combination of model capability, context window size, and provider margin. The table below captures current pricing for every model a developer is likely to use in production or daily coding work.
Anthropic (Claude)
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| Claude 4 Opus | $15.00 | $75.00 | 200K | Complex reasoning, architecture |
| Claude 4 Sonnet | $3.00 | $15.00 | 200K | Daily coding, analysis |
| Claude 3.5 Haiku | $0.80 | $4.00 | 200K | Fast tasks, boilerplate |
OpenAI
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| o3 | $10.00 | $40.00 | 200K | Complex reasoning, math |
| o4-mini | $1.10 | $4.40 | 200K | Balanced reasoning + cost |
| GPT-4o | $2.50 | $10.00 | 128K | General coding, chat |
| GPT-4o mini | $0.15 | $0.60 | 128K | High-volume, simple tasks |
Google (Gemini)
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| Gemini 2.5 Pro | $1.25 | $10.00 | 1M | Long context, analysis |
| Gemini 2.5 Flash | $0.15 | $0.60 | 1M | Fast, cost-efficient |
| Gemini 2.0 Flash | $0.10 | $0.40 | 1M | Cheapest capable model |
Mistral
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| Mistral Large | $2.00 | $6.00 | 128K | European hosting, coding |
| Mistral Medium | $0.40 | $1.20 | 128K | Balanced cost and quality |
| Mistral Small | $0.10 | $0.30 | 128K | Lightweight tasks |
Hosted Open-Source (via Groq, Together AI)
| Model / Provider | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Best For |
|---|---|---|---|---|
| Llama 3.1 70B (Groq) | $0.59 | $0.79 | 128K | Fast inference, budget |
| Llama 3.1 405B (Together) | $3.50 | $3.50 | 128K | Open-source, fine-tuning |
| DeepSeek V3 (DeepSeek) | $0.27 | $1.10 | 128K | Cost-efficient coding |
| Qwen 2.5 72B (Together) | $0.90 | $0.90 | 128K | Multilingual tasks |
Pricing changes frequently as providers compete. These figures are accurate as of early 2026, but check each provider’s pricing page before making commitments. For developers who want to track actual costs as they accumulate, tools like FavTray monitor Claude and OpenAI spending in real time.
How much does each model cost per real-world task?
A typical code review (200 lines of code) costs between $0.001 and $0.15 depending on the model chosen, while a complex multi-turn debugging session ranges from $0.10 to $8.00. The per-token price only tells half the story — what matters is how many tokens each model uses to complete the same task at acceptable quality.
Here’s what common developer tasks cost across the major models:
| Task | Claude Sonnet | GPT-4o | Gemini 2.5 Pro | GPT-4o mini | Gemini Flash |
|---|---|---|---|---|---|
| Code review (200 lines) | $0.04 | $0.03 | $0.03 | $0.003 | $0.002 |
| Bug fix (single function) | $0.08 | $0.07 | $0.06 | $0.008 | $0.005 |
| Feature implementation (200 LOC) | $0.45 | $0.52 | $0.40 | $0.06 | $0.04 |
| Debugging session (5 turns) | $2.80 | $3.40 | $2.60 | $0.35 | $0.22 |
| Documentation (1,000 words) | $0.12 | $0.10 | $0.09 | $0.01 | $0.008 |
| Test generation (full file) | $0.30 | $0.25 | $0.22 | $0.03 | $0.02 |
| Architecture analysis | $1.20 | $1.50 | $1.10 | $0.15 | $0.10 |
The mini/flash models are 10-15x cheaper for every task. The quality tradeoff is real but smaller than the price gap suggests: for straightforward tasks like code review and documentation, GPT-4o mini and Gemini Flash produce results within 80-90% of the premium models’ quality. For complex reasoning tasks like debugging and architecture analysis, the premium models justify their cost through fewer iterations and better accuracy.
A 2025 study by Eval.ai found that premium LLMs (Sonnet, GPT-4o, Gemini Pro) produced correct solutions on the first attempt 45-55% of the time for complex coding tasks, compared to 25-35% for budget models (Eval.ai, “LLM Coding Benchmarks,” 2025). When the budget model fails on the first attempt and you need to iterate, the cost savings disappear.
Which provider is cheapest for high-volume API usage?
For high-volume usage exceeding 100 million tokens per month, Google Gemini offers the lowest cost at scale with Gemini 2.0 Flash at $0.10/$0.40, followed by Mistral Small at $0.10/$0.30 and GPT-4o mini at $0.15/$0.60. At these volumes, the per-token price difference between providers translates to thousands of dollars per month.
Monthly cost estimates at different volume tiers:
| Monthly Volume | Claude Sonnet | GPT-4o | Gemini 2.5 Pro | GPT-4o mini | Gemini Flash |
|---|---|---|---|---|---|
| 1M tokens (light) | $9.00 | $6.25 | $5.63 | $0.38 | $0.28 |
| 10M tokens (moderate) | $90.00 | $62.50 | $56.25 | $3.75 | $2.75 |
| 100M tokens (heavy) | $900 | $625 | $563 | $37.50 | $27.50 |
| 1B tokens (enterprise) | $9,000 | $6,250 | $5,625 | $375 | $275 |
Estimates assume a 50/50 input/output token split. Actual costs vary based on your input-to-output ratio.
At the enterprise tier, the difference between Claude Sonnet and Gemini Flash is $8,725/month for the same token volume. This is why large-scale applications almost always route simple queries to budget models and reserve premium models for complex tasks. The routing decision alone can reduce API costs by 70-80%.
For individual developers, the volume is much lower — typically 5-20 million tokens per month. At these levels, the absolute dollar differences between providers are $30-150/month, making model quality and developer experience more important factors than pure price.
How should you choose between quality tiers?
Use premium models (Sonnet, GPT-4o, Gemini Pro) for tasks where accuracy saves iteration time — debugging, architecture design, and complex code generation. Use budget models (Haiku, GPT-4o mini, Gemini Flash) for high-volume tasks where 80% accuracy is acceptable — formatting, boilerplate, documentation, and simple refactors. The 10-15x price difference between tiers makes this routing decision the single highest-leverage cost optimization.
A practical decision framework:
| Task Characteristic | Recommended Tier | Why |
|---|---|---|
| Requires multi-step reasoning | Premium | Budget models make errors that cascade |
| Single-turn, well-defined output | Budget | Premium reasoning is wasted |
| Code that runs in production | Premium | Bugs from budget models cost more than the savings |
| Internal documentation | Budget | Minor quality differences don’t matter |
| Security-sensitive code review | Premium | Missing a vulnerability is expensive |
| Data transformation / formatting | Budget | Pattern-following, not reasoning |
| Exploring unfamiliar APIs | Premium | Better accuracy prevents false starts |
Developers who adopt this tiered approach typically reduce their monthly AI API costs by 40-60% compared to using a single model for everything. The key is making the tier decision habitual, not something you think about for each interaction.
FavTray helps implement this strategy by showing per-session costs in real time. When you see a simple documentation task costing premium-model rates, it’s a natural reminder to switch to a budget model.
What pricing trends should developers expect in 2026 and beyond?
LLM API prices have dropped 70-80% since 2024 for equivalent capability and are expected to continue declining 40-50% annually as competition intensifies and inference costs fall. However, the newest frontier models maintain premium pricing — the price drop applies to established capability levels, not the cutting edge.
Historical pricing trends for context:
| Model Tier | 2024 Price (per 1M out) | 2025 Price | 2026 Price | Drop |
|---|---|---|---|---|
| Premium (GPT-4 class) | $60.00 | $30.00 | $10-15 | 75-83% |
| Standard (GPT-4o class) | $15.00 | $10.00 | $10.00 | 33% |
| Budget (mini class) | $2.00 | $0.60 | $0.30-0.60 | 70-85% |
The implication for developers is that model costs will continue to decrease, but new capability tiers will maintain premium pricing. The most cost-effective strategy is to continuously evaluate whether tasks currently routed to premium models could be handled by the latest budget models, which improve significantly with each generation.
According to ARK Invest’s research, inference costs per token are falling at approximately 50% per year driven by hardware improvements, model distillation, and competitive pressure (ARK Invest, “Big Ideas 2026”). This means that your current AI spending level buys roughly twice as much capability each year — if you actively manage which models you use for which tasks.
Frequently Asked Questions
What is the cheapest LLM API in 2026?
The cheapest capable LLM APIs in 2026 are Google's Gemini 2.0 Flash at $0.10/$0.40 per million tokens and OpenAI's GPT-4o mini at $0.15/$0.60. For open-source hosted options, Groq's Llama 3 70B at $0.59/$0.79 offers strong performance at low cost. The cheapest option depends on your quality requirements.
How much does it cost to process 1 million tokens with GPT-4o?
Processing 1 million tokens with GPT-4o costs $2.50 for input and $10.00 for output. A typical API call with 2,000 input tokens and 1,000 output tokens costs approximately $0.015. In practice, most developers spend $0.01-0.05 per interaction depending on context size and response length.
Is Claude or GPT-4o cheaper for coding tasks?
GPT-4o is 17-33% cheaper per token than Claude 3.5 Sonnet ($2.50/$10 vs $3/$15 per million tokens). However, Claude tends to complete complex coding tasks in fewer iterations, which can make the total cost comparable or lower. For simple single-turn tasks, GPT-4o is consistently cheaper.
Why are output tokens more expensive than input tokens?
Output tokens cost 3-5x more than input tokens because generation requires significantly more compute than processing input. Each output token involves a full forward pass through the model, while input tokens can be processed in parallel batches. This pricing reflects the actual computational cost difference.
How do Google Gemini API prices compare to OpenAI and Claude?
Gemini 2.5 Pro at $1.25/$10 per million tokens is cheaper than both Claude Sonnet ($3/$15) and GPT-4o ($2.50/$10) on input, and comparable on output. Gemini 2.5 Flash at $0.15/$0.60 directly competes with GPT-4o mini at $0.15/$0.60, making Google's offerings among the most cost-competitive in 2026.