LLM Pricing Comparison
Every provider quotes two prices — input and output per million tokens — and neither tells you what a model actually costs to run. This comparison lists both prices for 16 production models, adds a blended 4:1 price you can compare directly, and flags the caveats the pricing pages leave out.
LLM API pricing table (July 2026)
| Provider | Model | Input $/1M | Output $/1M | Blended 4:1 | Best for | Watch out for |
|---|---|---|---|---|---|---|
| OpenAI | GPT-5.6 Sol | $5.00 | $30.00 | $10.00 | Frontier reasoning and agentic coding | Premium output price adds up fast on long answers |
| OpenAI | GPT-5.6 Terra | $2.50 | $15.00 | $5.00 | Strong default for production chat | 2.5× Luna's price for gains you may not need |
| OpenAI | GPT-5.6 Luna | $1.00 | $6.00 | $2.00 | High-volume chat with solid quality | Weaker on multi-step reasoning than Terra and Sol |
| OpenAI | GPT-5.4 Mini | $0.75 | $4.50 | $1.50 | Cost-sensitive assistants and summarization | Previous generation — benchmark before committing |
| OpenAI | GPT-5.4 Nano | $0.20 | $1.25 | $0.41 | Classification, extraction, routing | A workhorse, not a reasoner — keep tasks narrow |
| Anthropic | Claude Fable 5 † | $10.00 | $50.00 | $23.40 | The hardest reasoning and long-horizon agent work | Highest list price here, plus ~30% tokenizer overhead |
| Anthropic | Claude Opus 4.8 † | $5.00 | $25.00 | $11.70 | Premium coding and analysis below Fable's price | Tokenizer overhead applies (~30% more tokens) |
| Anthropic | Claude Sonnet 5 † | $2.00 | $10.00 | $4.68 | Balanced quality and cost for production agents | Corrected blended cost sits closer to Terra than list price suggests |
| Anthropic | Claude Haiku 4.5 | $1.00 | $5.00 | $1.80 | Fast, cheap chat with Claude behaviour | Quality gap versus Sonnet shows on complex tasks |
| Gemini 3.5 Flash | $1.50 | $9.00 | $3.00 | Long-context and multimodal work at a mid price | Output at $9.00 is pricier than the input price suggests | |
| Gemini 3.1 Pro | $2.00 | $12.00 | $4.00 | Long-document analysis and grounded answers | Listed price applies up to 200K-token contexts | |
| Gemini 2.5 Flash-Lite | $0.10 | $0.40 | $0.16 | Bulk cheap tasks: tagging, drafts, filtering | Older lightweight model — expect quality limits | |
| DeepSeek | DeepSeek V4 Flash | $0.14 | $0.28 | $0.17 | The cheapest capable chat model per token | Weigh data-residency and compliance requirements |
| DeepSeek | DeepSeek V4 Pro | $0.44 | $0.87 | $0.52 | Budget reasoning far below Big-3 prices | Throughput and rate limits vary by region and load |
| Open weights | Llama 3.3 70B (Together AI) | $0.88 | $0.88 | $0.88 | Open weights with identical in/out pricing | You can also self-host it — compare the break-even first |
| Open weights | gpt-oss-120B (Together AI) | $0.15 | $0.60 | $0.24 | Cheap open-weights generalist via hosted endpoints | Newer model line — benchmark on your own tasks |
† Anthropic's newest models tokenize the same text into roughly 30% more tokens, so their blended column includes that correction; the raw input/output columns are list prices as published. All prices are for standard (non-batch) API usage and exclude prompt-caching discounts and volume deals. Providers change prices without notice — treat the live-refreshed table above as the source of truth, not screenshots of it.
Why output tokens cost more than input tokens
Reading is cheap; writing is expensive. When a model ingests your prompt, it processes all input tokens in one parallel pass (the prefill). Generating the answer is sequential: each output token requires a full forward pass through the model, while your entire context sits in GPU memory for the duration. One output token therefore costs the provider many times the compute of one input token — and the price sheet mirrors that, with output typically 2–6× input (6× for GPT-5.6 Sol and Gemini 3.5 Flash, 5× for Claude models, but only 2× for DeepSeek V4 Flash).
The practical consequence: your input:output mix changes which model is cheapest. Input-heavy workloads — RAG pipelines that stuff thousands of context tokens per request, classification, document analysis — should weight the input column. Output-heavy workloads like long-form writing should weight the output column, where the spread between providers is widest. If you're building retrieval, the RAG Cost Calculator does this split for you.
How to compare models fairly: the blended price
blended $/1M = (4 × input price + output price) ÷ 5monthly cost = blended $/1M × total tokens per month (in millions)
Most production traffic is input-heavy — around four input tokens for every output token once you count system prompts, history and context. Weighting the two prices at that 4:1 ratio gives a single comparable number per model: the blended column in the table. If your real mix differs, re-weight accordingly; the LLM Token Cost Calculator has a ratio input that does the arithmetic live, against the same daily price feed.
Worked example. GPT-5.6 Terra at 4:1 blends to (4 × $2.50 + $15.00) ÷ 5 = $5.00 per million tokens. Claude Sonnet 5 blends to (4 × $2.00 + $10.00) ÷ 5 = $3.60 on paper — but Anthropic's tokenizer splits the same text into ~30% more tokens, so the effective blended price is $3.60 × 1.3 = $4.68. List prices make Sonnet look 28% cheaper than Terra; the corrected number narrows the gap to about 6%.
Which model for which job
Frontier tier ($10–$23 blended). GPT-5.6 Sol and Claude Fable 5 are for work where a wrong answer costs more than the tokens: complex agent pipelines, hard code generation, high-stakes analysis. Nobody should run bulk traffic here — the winning pattern routes only the hardest 5–10% of requests up to this tier.
Production tier ($2–$5 blended). GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.1 Pro and GPT-5.6 Luna is where most products should land: strong general quality at single-digit blended prices. Within the tier the differences are workload-specific — run a week of your real traffic through the OpenAI, Claude and Gemini calculators before you decide.
Budget tier (under $2 blended). GPT-5.4 Mini and Nano, Claude Haiku 4.5, Gemini 2.5 Flash-Lite and the DeepSeek models handle classification, extraction, routing, and simple chat at a tenth of production-tier prices. The trap is quality drift on tasks that quietly got harder — keep an evaluation set and re-test.
Open weights. Hosted endpoints like Together AI price Llama 3.3 70B at a flat $0.88 and gpt-oss-120B at $0.24 blended — and the same weights can run on your own GPU. Whether that's worth it is a volume question: the self-hosting break-even guide walks through the math.
Frequently asked questions
Related tools and guides
- LLM Token Cost Calculator — turn any row of this table into your actual monthly bill
- OpenAI API cost calculator — GPT-5.6 and GPT-5.4 costs at your volume
- Claude API cost calculator — Anthropic costs including the tokenizer correction
- Gemini API cost calculator — Google model costs at your volume
- Prompt Caching Cost Savings Calculator — the ~90% discount this table's prices don't show