LLM Token Costs in 2026: Complete Provider Comparison

OpenAI's cached input pricing (50% discount) applies automatically when the same prefix appears in recent requests. The o-series (reasoning) models cost significantly more but deliver dramatically better performance on multi-step reasoning tasks — worth it for complex planning agents, not for simple extraction.

Google Gemini

Gemini 2.5 Flash is the most cost-effective model for bulk document processing in 2026. At $0.10/MTok input, it processes 10 million tokens for $1. The 1M-token context window means you can send an entire 700-page PDF in a single call without chunking. Gemini's pricing tiers by context length — watch for the 128K threshold.

DeepSeek

DeepSeek-V3 is the most important pricing development in the 2026 market. At $0.27/MTok input — roughly 10× cheaper than GPT-4o — it delivers competitive performance on coding, reasoning, and instruction-following tasks. For high-volume production workflows that don't require frontier-model capability, DeepSeek has changed the cost calculus entirely.

Groq (Llama 4)

Groq's LPU-based inference offers the fastest time-to-first-token in the market — often under 100ms. For real-time voice applications, streaming interfaces, and latency-sensitive agents, Groq's speed advantage can justify the slightly higher cost compared to DeepSeek. Llama 4 Scout at $0.11/MTok input is currently the cheapest capable model from a major provider.

Cerebras

Cerebras offers Groq-competitive inference speeds with similar pricing. Both providers are primarily valuable for latency-sensitive use cases where open-weight models are acceptable.

Cost optimization strategies

1. Use context caching for repeated system prompts

If your agent has a large system prompt (tool definitions, instructions, personas), Anthropic and OpenAI both offer 50–80% discounts on cached tokens. Enable caching, keep your system prompt prefix stable, and let the cache work. On workflows running hundreds of times per day, this alone can cut input costs by 30–50%.

2. Route tasks to the right model tier

Not every task needs GPT-4o or Claude Sonnet. Build a routing layer:

• Simple extraction/classification → Gemini 2.0 Flash or Llama 4 Scout
• Standard reasoning → GPT-4.1 mini or DeepSeek-V3
• Complex multi-step → Claude Sonnet 4 or GPT-4o
• Research/planning → o3 or Claude Opus 4 (use sparingly)

A properly tiered routing strategy typically reduces average token cost by 60–70% with negligible quality impact on overall pipeline output.

3. Trim context aggressively

Output tokens are 3–5× more expensive than input tokens, but input token bloat is the silent budget killer. Audit your prompts. Remove examples that aren't needed for the specific task. Don't inject entire documents when you only need a summary. In a 1,000-token context, 200 unnecessary tokens across 10,000 daily calls costs $0.50/day — $180/year — for nothing.

4. Batch non-real-time work

OpenAI's Batch API offers 50% discounts for asynchronous processing with up to 24-hour turnaround. For document processing, data enrichment, and report generation that doesn't need to return in real-time, batch mode is the highest-ROI cost reduction available.

5. Cache at the application layer

Before calling any LLM, check if you've answered this exact query recently. A simple Redis cache with 1-hour TTL eliminates redundant API calls for repeated questions. In a customer support bot, 20–30% of queries are semantically identical to recent queries — caching them costs nothing and returns results instantly.

BYOK in AACFlow

AACFlow supports Bring Your Own Key (BYOK) for all major providers. You connect your API key once; AACFlow routes workflow calls through your key, not ours. This means you benefit from your existing provider agreements, volume discounts, and enterprise tiers directly. Usage appears in your provider dashboard alongside your other applications.

AACFlow's billing UI shows per-workflow token spend breakdowns, so you can identify which workflows are expensive and optimize them — without digging through provider logs.

The right model for your workload is almost never the most expensive model. In 2026, the gap between frontier models and capable smaller models has narrowed enough that choosing the right model for each task is the most impactful cost optimization available.