Enterprise LLM cost comparison: per-token pricing.
A procurement-grade reference for 2026 LLM economics: published per-token input and output pricing across Anthropic, OpenAI, Google, Meta, Mistral and Cohere, plus the cached-input, batch, reserved-capacity and volume-discount mechanics that decide realised cost. The cheapest model is rarely the right one — the right one is the cheapest that clears the capability bar for the workload.
There is no single "cheapest" model. The cheapest enterprise-grade frontier model is Claude Haiku 4 at $0.25 / $1.25 per million input / output tokens; the priciest are Claude Opus 4 ($15 / $75) and OpenAI o1 ($15 / $60) — a roughly 300× spread on output. Cached-input and batch discounts of 50–90% reshape realised cost, and the single largest lever is choosing the cheapest model that still clears the workload's capability threshold.
01 Key findings
The price spread across frontier models is enormous. Claude Haiku 4 bills $0.25 input / $1.25 output per million tokens; Claude Opus 4 and OpenAI o1 bill $15 input and $75 / $60 output. Model choice, not vendor loyalty, is the dominant cost variable.
Output tokens dominate. Output costs 4–5× input on most frontier models. A workload that looks cheap on input counts can be expensive on output; constraining output length and right-sizing the model is the largest single optimisation in 2026.
Cached input and batch discounts are structural, not marginal. Anthropic discounts cached input up to 90%, Google 75%, OpenAI 50%; batch APIs run 50% off across vendors. On the right workload shape these change unit economics more than any negotiated discount.
The right model is the cheapest that meets the capability threshold. Not the cheapest model overall. Matching model tier to workload class beats blanket standardisation on a single frontier model.
Reserved capacity beats per-token at sustained throughput. Provisioned Throughput (Azure PTU, Bedrock, Vertex GSU, Anthropic reservations) typically saves 25–45% above ~100–300 tokens/sec of steady-state load.
Hidden layers understate TCO by 15–35%. Embeddings, multimodal input tokens, and orchestration / observability infrastructure are absent from published per-token rates but real on the invoice.
02 Per-token pricing
Published per-token pricing across the frontier and mid-tier families as of Q2 2026. Rates reflect standard real-time API access on the vendor's direct endpoint or primary cloud channel (Bedrock for Llama and Claude on AWS, Vertex for Gemini and Claude on GCP). All figures are USD per 1M tokens.
| Model | Vendor | Input / 1M | Output / 1M | Context window |
|---|---|---|---|---|
| Claude Opus 4 | Anthropic | $15.00 | $75.00 | 500K |
| Claude Sonnet 4 | Anthropic | $3.00 | $15.00 | 500K |
| Claude Haiku 4 | Anthropic | $0.25 | $1.25 | 200K |
| GPT-4o | OpenAI | $2.50 | $10.00 | 128K |
| GPT-4o mini | OpenAI | $0.15 | $0.60 | 128K |
| GPT-4.1 | OpenAI | $2.00 | $8.00 | 1M |
| GPT-4.1 mini | OpenAI | $0.40 | $1.60 | 1M |
| o1 (reasoning) | OpenAI | $15.00 | $60.00 | 200K |
| o3-mini (reasoning) | OpenAI | $1.10 | $4.40 | 200K |
| Gemini 2.0 Pro | $1.25 | $5.00 | 2M | |
| Gemini 2.0 Flash | $0.075 | $0.30 | 1M | |
| Gemini 2.0 Flash-Lite | $0.0375 | $0.15 | 1M | |
| Llama 3.1 405B (Bedrock) | Meta / AWS | $5.32 | $16.00 | 128K |
| Llama 3.1 70B (Bedrock) | Meta / AWS | $0.99 | $0.99 | 128K |
| Mistral Large 2 | Mistral | $3.00 | $9.00 | 128K |
| Mistral Small | Mistral | $0.20 | $0.60 | 128K |
| Cohere Command R+ | Cohere | $2.50 | $10.00 | 128K |
| Cohere Command R | Cohere | $0.15 | $0.60 | 128K |
03 Relative cost
Output tokens are the dominant cost driver, so relative output price per 1M tokens is the clearest single view of where each model sits. Bars are scaled to the most expensive model (Claude Opus 4, $75).
A 2,000-token response costs $0.030 on Claude Sonnet 4 ($15/1M) but $0.0025 on Claude Haiku 4 ($1.25/1M). For high-volume workloads where Haiku 4 quality is sufficient, that 12× reduction is the largest single optimisation available in 2026. Constrain output length explicitly and use the most concise model that clears the quality threshold.
04 Cached & batch discounts
Published rates are starting points. Most workloads at scale qualify for cached-input pricing (stable system prompts) or Batch API pricing (no real-time requirement). Discount structures vary materially by vendor.
| Vendor | Cached input discount | Batch API discount | Notes |
|---|---|---|---|
| Anthropic | 90% off (1-hour cache) | 50% off | Cache write costs 125% of standard for 5-minute cache |
| OpenAI | 50% off (5-minute cache) | 50% off | Implicit caching; explicit caching also available |
| Google Gemini | 75% off (context caching) | 50% off | Per-token cache storage fee applies |
| AWS Bedrock (Claude, Llama) | Per-model (matches direct) | 50% off via Batch Inference | Bedrock Provisioned Throughput separately priced |
| Mistral | Not generally available | 50% off | Self-hosted gives full caching control |
| Cohere | Limited | 50% off via batch jobs | Per-tenant negotiation |
A RAG chat app with a stable 30,000-token system prompt and 500-token user prompts: without caching, every request bills 30,500 input tokens at $3/1M on Claude Sonnet 4 (~$0.0915/request). With a 90% cache discount on the system prompt, each request bills 3,000 cached plus 500 standard input tokens (~$0.011/request) — an 88% input-side reduction. At 100,000 requests/day that compounds to about $300,000/year saved on one workload.
05 Cost drivers
Three levers move realised cost more than headline per-token rates: context (how many input tokens each request carries, and whether they are cacheable), caching / batching (workload shape), and output length (the 4–5× multiplier). Beyond those, three hidden layers understate three-year TCO by 15–35% on most workloads.
| Hidden layer | Typical magnitude | Detail |
|---|---|---|
| Embedding generation | <5% of total spend | OpenAI text-embedding-3-large $0.13/1M; Voyage voyage-3 $0.18/1M. Small but a discrete line item that surprises buyers who modelled only generation. |
| Image / multimodal input tokens | Can dominate on OCR-heavy work | A standard image to GPT-4o is ~765–1,105 tokens by resolution. Document OCR, chart and screenshot workloads can dominate token spend. |
| Orchestration & observability | +15–30% of model spend | LangChain / LlamaIndex orchestration, vector database, observability, prompt management and evaluation tooling around the model. |
06 Reserved capacity & volume discounts
For sustained high-throughput workloads, per-token billing is not the cheapest option. Vendor-side reserved capacity typically delivers 25–45% cost reduction above 100–300 tokens/sec of sustained throughput.
| Reserved capacity SKU | Pricing model | Breakeven vs per-token |
|---|---|---|
| Azure OpenAI Provisioned Throughput Unit | Per-PTU per month, reserved capacity | ~150 tokens/sec sustained |
| AWS Bedrock Provisioned Throughput | Per-model-unit per hour | ~200 tokens/sec sustained |
| Anthropic Capacity Reservation | Per-tenant monthly commit | Negotiated per use case |
| Google Vertex Provisioned Throughput | Per-GSU per month | ~100 tokens/sec sustained |
Above roughly $50,000/month in API spend, all the frontier vendors entertain custom commercial terms. Discount realisation tracks committed spend.
| Monthly committed spend | Typical discount band | Notes |
|---|---|---|
| $50K to $100K | 0 to 8 percent | Pay-as-you-go, no commit |
| $100K to $500K | 8 to 18 percent | 12-month commit with quarterly true-up |
| $500K to $2M | 15 to 25 percent | Multi-year commit, custom SLAs |
| $2M+ | 20 to 35 percent | Capacity reservation + bespoke terms |
Token economics are usually identical across cloud channels, but the commercial accounting is not. Claude via AWS Bedrock burns an AWS EDP balance; Azure OpenAI burns Microsoft MACC; Vertex burns a GCP enterprise agreement commit. Channel arbitrage is a commercial-structure decision; capability is unaffected.
07 Cost by workload class
The right model for a workload is the cheapest that meets its capability threshold. Representative monthly cost across the primary workload classes at enterprise scale, with the typical model choice for each.
| Workload | Volume assumption | Recommended model | Monthly cost |
|---|---|---|---|
| Enterprise chat assistant (RAG) | 5M requests / month, 8K tokens avg | Claude Sonnet 4 with caching | $48K to $72K |
| Document analysis (legal review) | 250K documents / month, 50K tokens avg | Claude Opus 4 or Gemini 2.0 Pro | $94K to $187K |
| Code generation (developer assistant) | 800K completions / month, 4K tokens avg | Claude Sonnet 4 or GPT-4o | $28K to $35K |
| Classification / extraction at scale | 200M items / month, 1K tokens avg | Claude Haiku 4 or Gemini Flash | $8K to $32K |
| Multi-step reasoning (analysis) | 50K tasks / month, 20K tokens avg | o1 or Claude Opus 4 extended thinking | $40K to $115K |
08 Model-selection framework
Four factors drive the model decision. Weight them to your workload before committing spend.
Capability threshold
Identify the cheapest model that clears the quality bar for the task, not the cheapest model outright. Over-provisioning capability is the most common source of avoidable LLM spend.
Workload shape
Steady-state, high-throughput workloads favour reserved capacity; bursty or low-throughput ones favour per-token. Mixed workloads reserve the baseline and burst on per-token.
Prompt structure
Stable system prompts unlock cached-input discounts of 50–90%; latency-tolerant work unlocks 50% batch pricing. Design prompts and pipelines to qualify for both.
Commercial channel
Existing cloud commitments change net cost even when token rates match. Route Claude, Llama or GPT through the channel that burns down an EDP, MACC or GCP commit.
09 Which model when
Classification, extraction and high-volume routing where Claude Haiku 4 or Gemini 2.0 Flash-Lite quality suffices. This is where the 12× cost gap versus frontier models pays off — keep output short.
Broad-population chat with RAG, developer assistants and multimodal work. Claude Sonnet 4 or GPT-4o with cached input is the cost-efficient default for the majority of enterprise workloads.
Long-document analysis (Claude Opus 4 or Gemini 2.0 Pro) and multi-step reasoning (OpenAI o1 or Opus 4 extended thinking). Reserve the premium tier for tasks that genuinely need it. For sovereign deployment, self-host Llama 3.1 or Mistral.
Deeper per-vendor detail lives in our enterprise AI vendor selection framework, ChatGPT Enterprise pricing 2026, Claude Enterprise pricing 2026, OpenAI Enterprise pricing, Gemini Enterprise and Microsoft 365 Copilot pricing 2026. For counsel, see AI procurement advisory, cloud contract negotiation and software licensing advisory.
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