AI Model API Pricing Tracker · Q2 2026

AI model pricing in Q2 2026 spans nearly two orders of magnitude per token, and three more once you account for cached, batch, and reasoning-mode multipliers. The result is a procurement landscape where the wrong model choice on a high-volume workload can be a ten-times cost decision, made without anybody noticing for a quarter.

We track 30 models from 12 providers across input, output, cached-read, cache-write, and batch tier rates. The full data lives below, organised so that engineering and finance teams can run a real cost model rather than re-add per-token rates from memory. The headline numbers as of April 23, 2026: Claude Opus 4.7 at $5/$25, GPT-5.5 at $5/$30, GPT-5.5 Pro at $30/$180, Gemini 3 Pro Deep Think at $4/$20, DeepSeek V4 at $0.40/$1.60, and Llama 4 405B hosted at $0.70/$2.80.

Per-token rates are the headline; they are not the budget. The last section translates rack-rate pricing into real cost-per-successful -task numbers for six common workflows, because that is the unit most procurement RFPs are quietly converging on.

Below is the chart we update every quarter. It plots output rate per 1M tokens against the rough quality tier of each model so the cost-quality frontier is visible at a glance. The bars are normalised to GPT-5.5 Pro at output ($180) for visual comparison — the actual rate label sits to the right of each bar.

Read this chart as a positioning map, not a buying guide. A 113× output spread between DeepSeek V4 and GPT-5.5 Pro does not mean DeepSeek is the right choice 113× as often — it means the cost of picking the wrong tier on a misaligned workload is enormous. The sections below decompose the bands.

Eight models share what we are calling the frontier band — released in the last 12 months, capable across all major capability families (reasoning, code, agentic tool use, long context), and priced in the $3-$30 input / $15-$180 output range. The granular layout below shows each model's rate in every billing dimension.

Open-weight pricing has compressed dramatically since Q1 2026. The major MoE models — Llama 4, Qwen 3, DeepSeek V4 — now sit in a tight $0.20-$0.80 per 1M output band across the leading inference providers. The shift in competitive dynamics is real: open-weight buyers no longer pick a model, they pick a provider on latency, throughput, and reliability.

The headline rates above are the rack-rate input and output. In production, three discount tiers reshape the effective cost: cached read, cache write, and batch. Each major provider ships at least two; OpenAI, Anthropic, and Google ship all three.

The mistake we see most often is procurement teams modelling cost against rack rate and forgetting batch and cache entirely. On workloads where 70%+ of input tokens hit a cached prefix and 30%+ of output is non-interactive, the effective rate sits 60-75% below rack — and that gap is the difference between a viable AI feature and a budget overrun.

For frontier closed-source models, the model and the provider are the same — you pay OpenAI for GPT-5.5, Anthropic for Claude. For open-weight models, you pick. The grid below shows where each provider lands on price, throughput, and reliability for the same Llama 4 405B model. The throughput numbers are what justify the premium tiers.

Per-token rate is the input to a cost model, not the output. The output is cost-per-successful-task — what it actually costs to complete a real workflow end-to-end including retries, output amplification, and tool-loop overhead. The six worked examples below translate the rate table into the unit procurement RFPs increasingly cite.

Three changes since the Q1 2026 tracker matter for cost modelling.

• Open-weight floor dropped roughly 30%. Llama 4 405B hosted output fell from $4.00/1M (Q1) to $2.80/1M (Q2); DeepSeek V4 native landed at $1.60/1M output, undercutting Q1 V3.5 by 18%. The compression is provider-led, not vendor-led — Together, Fireworks, Groq, Cerebras competing on margin.
• Cached-read tier became universal. Q1 2026 had Anthropic and OpenAI shipping cache; Google Gemini 3 Pro joined in late Q1 at the most aggressive 5% rate. Mistral added 10% cached read in February. Every major provider now ships prefix-cache pricing.
• Reasoning-mode cost is now load-bearing. GPT-5.5 Pro at high reasoning_effort is 6× the cost of GPT-5.5 standard; Claude Opus 4.7 with extended thinking adds 2-4×. Gemini 3 Deep Think adds 3×. The reasoning premium has become the most important sub-tier choice on any cost model — bigger than picking between the headline models in some workloads.