# LLM API Pricing in 2026: Every Major Model Compared

> Per-million-token prices for Claude, GPT, Gemini, DeepSeek, Mistral, and Grok — plus caching and batch discounts — verified against vendor pricing pages.

Verified June 12, 2026, from vendor pricing pages only. Flagships: Claude Fable 5 $10/$50 per million tokens (in/out), Claude Opus 4.8 $5/$25, GPT-5.5 $5/$30, Gemini 3.1 Pro Preview $2/$12. Workhorses: Claude Sonnet 4.6 $3/$15, GPT-5.4 $2.50/$15, Gemini 3.5 Flash $1.50/$9. Caching cuts input ~90%, batch APIs cut everything 50% — the discounts stack.

All prices are **USD per million [tokens](/glossary/llm-token), standard tier**, read directly from vendor pricing pages on **June 12, 2026**. Prices change; this page is maintained on a refresh cadence (the `Updated` date above is the source of truth), and numbers we couldn't verify on a vendor page are omitted, not estimated.

## Anthropic (Claude)

| Model | Input | Output | Cache read | Batch (in/out) | Context |
| --- | --- | --- | --- | --- | --- |
| Claude Fable 5 | $10.00 | $50.00 | $1.00 | $5.00 / $25.00 | 1M |
| Claude Opus 4.8 | $5.00 | $25.00 | $0.50 | $2.50 / $12.50 | 1M |
| Claude Sonnet 4.6 | $3.00 | $15.00 | $0.30 | $1.50 / $7.50 | 1M |
| Claude Haiku 4.5 | $1.00 | $5.00 | $0.10 | $0.50 / $2.50 | 200K |

Cache writes cost 1.25x input (5-minute TTL) or 2x (1-hour); cache reads are 0.1x input. Batch is 50% off and **stacks with caching**. Notably, the 1M-context models include the full window at standard per-token pricing — no long-context surcharge.

## OpenAI (GPT)

| Model | Input | Cached input | Output | Batch | Context |
| --- | --- | --- | --- | --- | --- |
| GPT-5.5 | $5.00 | $0.50 | $30.00 | 50% off | 1M |
| GPT-5.5-pro | $30.00 | — | $180.00 | — | ~1M |
| GPT-5.4 | $2.50 | $0.25 | $15.00 | 50% off | 1M |
| GPT-5.4-mini | $0.75 | $0.075 | $4.50 | 50% off | 400K |
| GPT-5.4-nano | $0.20 | $0.02 | $1.25 | 50% off | 400K |

Cached input is 0.1x across the lineup; Flex tier matches batch pricing, Priority tier runs 2.5x. The pro tiers ($30/$180) are the premium-reasoning outliers of the whole market.

## Google (Gemini API)

| Model | Input | Output | Cache read | Batch | Context |
| --- | --- | --- | --- | --- | --- |
| Gemini 3.1 Pro Preview | $2.00 (≤200K) / $4.00 (>200K) | $12.00 / $18.00 | $0.20 / $0.40 | 50% off | ~1M |
| Gemini 3.5 Flash | $1.50 | $9.00 (incl. thinking) | $0.15 | 50% off | ~1M |
| Gemini 3.1 Flash-Lite | $0.25 | $1.50 | $0.025 | 50% off | ~1M |

Google is the one major vendor with long-context tiering: the Pro model's per-token price roughly doubles beyond 200K of context. Cache storage bills separately per MTok-hour.

## DeepSeek, Mistral, xAI

| Model | Input | Output | Notes |
| --- | --- | --- | --- |
| DeepSeek V4 Flash | $0.14 | $0.28 | Cache hits ~$0.003; 1M context |
| DeepSeek V4 Pro | $0.435 | $0.87 | Cache hits ~$0.004; 1M context |
| Mistral Medium 3.5 | $1.50 | $7.50 | Premium tier; batch 50% |
| Mistral Large 3 | $0.50 | $1.50 | Open-weights flagship (not a typo: priced below Medium 3.5) |
| Mistral Small 4 | $0.10 | $0.30 | Budget tier |
| Grok 4.3 (xAI) | $1.25 | $2.50 | 1M context |

DeepSeek remains the proprietary-price disruptor — its V4 Flash undercuts every Western budget tier while claiming a 1M window. (Its legacy `deepseek-chat`/`deepseek-reasoner` aliases retire July 24, 2026.)

## Open-weights via hosts

Reference serverless prices (host pricing, not vendor MSRP), June 12, 2026 — Together AI / Fireworks:

| Model | Together (in/out) | Fireworks (in/out) |
| --- | --- | --- |
| DeepSeek V4 Pro | $2.10 / $4.40 | $1.74 / $3.48 |
| Kimi K2.6 | $1.20 / $4.50 | $0.95 / $4.00 |
| Qwen 3.6 Plus | $0.50 / $3.00 | $0.50 / $3.00 |
| GLM-5.1 | $1.40 / $4.40 | $1.40 / $4.40 |
| GPT-OSS-120B | — | $0.15 / $0.60 |

Hosted open-weights now sit squarely inside the proprietary mid-tier price band — the lever that keeps the whole market's pricing honest, and the input to any [self-host decision](/guides/mlops/self-host-vs-api-llm).

## Reading the table like an engineer

Three structural facts matter more than any single cell. **Output dominates**: at 3–6x input everywhere — and with [reasoning](/glossary/reasoning-model) thinking-tokens billed as output — verbose responses and deep deliberation drive bills more than prompt size. **The discount stack is enormous**: [prompt-cache](/glossary/prompt-caching) reads at ~0.1x input plus [batch](/glossary/batch-inference) at 50% compose to ~95% off for cacheable offline work — engineering for the stack beats switching vendors. **Tiers beat brands**: every vendor offers frontier/workhorse/budget rungs; [matching the tier to the task](/guides/getting-started/choosing-the-right-model) and measuring **cost per completed task** (not per token) is where the money actually is — the full playbook is [LLM Cost and Latency Engineering](/guides/advanced/llm-cost-latency-engineering).

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_Source: https://agentscamp.com/guides/advanced/llm-api-pricing-2026 — Guide on AgentsCamp._