LLM API pricing

How llm-provider access is priced — per-token, split input vs output, with output 2–6× more expensive than input across providers llm-api-pricing-comparison. The 2026 market spans a ~600× cost spread.

Three tiers (mid-2026 snapshot — volatile, cite the source/date)

• Budget — Mistral Small 3.2 $0.10/$0.30 (in/out per 1M); GPT-4.1 Nano $0.10/$0.40; deepseek V3.2 $0.27/$1.10.
• Mid / production — GPT-5.4 $2.50/$15; Claude Sonnet 4.6 $3/$15; Gemini 3.1 Pro $2/$12.
• Frontier / reasoning — GPT-5.4 Pro $30/$180; o3 $15/$60; Claude Opus 4.7 $5/$25.

Frontier snapshot (mid-2026, per 1M in/out — grok-4-5-price-vs-benchmarks): Grok 4.5 $2/$6, Opus 4.8 $5/$25, GPT-5.5 $5/$30, Fable 5 $10/$50. Grok 4.5 undercuts the whole group; Fable 5 sits at the top — pricier than Opus, corroborating the Fable-is-expensive read.

A different axis: speed-priced, no rate card

Most of the market above competes on a per-token rate card. Cerebras doesn’t: as a wafer-scale inference provider (cerebras-inference) reselling open models, it sells throughput, leads on “20× faster,” and publishes no per-million-token price at all. Its paid access is structured as daily-token subscription buckets — Cerebras Code Pro $50/mo → 24M tokens/day, Max $200/mo → 120M tokens/day — plus a $5-credit free trial and a self-serve Developer tier from $10 cerebras-pricing. Both coding tiers were listed sold out (mid-2026), a capacity signal: fast inference is rationed, not just priced. So the market now has two pricing shapes — the per-token rate card, and the speed-provider’s flat-subscription-plus-cap — and cost-per-task comparisons must account for both.

Cost levers (stack to ~25% of list)

• Prompt caching — 50–90% off repeated input prefixes (aggressive at anthropic/OpenAI). At Anthropic it’s also a throughput lever, not just a discount: under cache-aware ITPM, cached input tokens don’t count toward the rate limit either claude-api-rate-limits.
• Batch processing — flat 50% off for non-real-time work.
• Model routing — send simple tasks to budget models; cuts 70–90% of qualifying calls. Bought as a product this is the ai-gateway layer: omniroute ships 19 routing strategies with cost-optimized and headroom variants that pick the cheapest backend still holding quota.
• Prompt compression — squeeze the prompt before it’s billed. omniroute stacks 12 engines and claims 15–95% savings (89% “average”, self-reported). Unlike caching and batching this lever is client-side: it needs no cooperation from the provider, and it attacks tokens-per-task rather than price-per-token.
• Free-tier aggregation — treat many providers’ free pools as one budget. omniroute totals ~1.53B tokens/month across 43 pools, with 15 of them flagged as ToS-questionable. The lever works; the hazard comes attached.

Strategic reads

deepseek disrupted the bottom (OpenAI-compatible + ultra-low cost), pulling the floor down; google undercuts on sticker price; OpenAI offers the widest range; Anthropic competes on consistency + caching. A US frontier lab has now joined the price war: Grok 4.5 prices below the field and argues cost-per-task (per-token price × tokens-per-task) is the metric that matters — xAI claims 4.2× fewer tokens than Opus 4.8, so a lower benchmark still wins on total cost (grok-4-5-price-vs-benchmarks). That reframes the sticker-price comparisons above: tokens-per-task is a hidden multiplier on the headline rate. Because output dominates cost, context/output optimization is the highest-leverage spend control. Pricing is the most volatile fact in this wiki — always date it.

Related

llm-api-pricing-comparison · deepseek · anthropic · llm-provider · llm-benchmarks · cerebras-pricing · cerebras-inference · ai-gateway · omniroute