ai pricing

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Show HN: CostPerPrompt – Live AI API pricing and real-workload cost calculators
11 hours ago
- AI pricing is based on tokens (roughly 3/4 of a word), with separate charges for input and output; output is typically 3-5x more expensive than input.
- Prompt caching can reduce repeated input costs by up to 90%, and batch processing offers ~50% discounts, which many cost estimates ignore.
- A live table shows pricing for 232+ flagship models, including GPT-5.x, Claude 5 variants, DeepSeek V4, Gemini 3.x, Grok 4.x, and GLM 5.2, with costs ranging from $0.09 to $10.00 per million input tokens.
- Calculators are provided to convert token prices into real monthly costs for chatbots, agents, or API workloads.
Why are cached input tokens cheaper with AI services?
6 days ago
- Cached input tokens are cheaper because the GPU avoids recomputing the same intermediate state (KV cache) for repeated conversation prefixes.
- Key-value caching (KV caching) stores the model's internal state after processing initial messages, so subsequent calls with the same prefix skip redundant computation.
- Providers pass these compute savings to users as lower per-token prices for cache hits compared to cache misses.
- To maximize cache hits, avoid changing inference settings or previous messages between prompts in an application.
Managed agents are the new Lambda
6 days ago
- Managed agents are cloud-hosted agent harnesses (e.g., Claude Code) that run 24/7, allow background operations, and provide security and sandboxing benefits.
- Vendor lock-in is a major risk with managed agents; switching between agent harnesses (e.g., Claude Code to Codex) is easy locally but becomes difficult once deeply integrated into a provider's cloud.
- Anthropic's recent pricing changes for non-interactive Claude Code usage (5-20x increase) highlight the danger of lock-in and give OpenAI an opening with Codex's more flexible allowances.
- Solutions to avoid lock-in include self-hosting managed infrastructure (e.g., running Docker with OpenCode) or using third-party platforms that support multiple model providers.
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Claude Opus 5 pricing and launch-day cost-per-task result
9 days ago
- Anthropic released Claude Opus 5 on July 24, 2026, at the same standard token price as Opus 4.8, offering a direct value upgrade.
- Opus 5 is now the default model on Claude Max and available on Claude Pro, with API model ID claude-opus-5.
- Anthropic claims Opus 5 more than doubled Opus 4.8's performance on Frontier-Bench v0.1 and came within 0.5% of Fable 5's peak score on CursorBench 3.2 at roughly half the cost.
- Pricing stays flat per token, but task efficiency gains (fewer turns, tool calls, or retries) may lower real costs; early tests showed 26% fewer generated tokens on one legal workflow.
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The real prices of frontier models. Tokens * Price, right?
20 days ago
- Token counts for the same content vary significantly between AI models due to different tokenizers, which is not reflected in the advertised price per million tokens.
- Anthropic's new tokenizer (used in Claude models like Opus 4.8 and Sonnet 5) increases token counts by about 30-32% compared to its old tokenizer, despite unchanged headline prices, leading to higher effective costs.
- The tokenizer divergence is most pronounced for code, especially TypeScript, where Claude's new tokenizer uses 1.73x more tokens than OpenAI's GPT (with o200k tokenizer), compared to 1.4x for English prose.
- Effective pricing should account for tokenizer differences; for example, Claude Opus 4.8 has an effective price of $7.50/$37.50 per million tokens for input/output, compared to its headline $5.00/$25.00, when adjusted for token count.
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The unbearable cheapness of open weight models
a month ago
- The user compares AI model pricing, noting that DeepSeek V4 is much cheaper than Anthropic and OpenAI's 'frontier' models, with a nearly 50x price difference based on tokens.
- There's concern that Anthropic and OpenAI have high costs and may not be able to reduce prices significantly to compete with cheaper models like DeepSeek or Xiaomi's Mimo.
- The user questions if lower costs for some models are due to being open-weight and stress-tested by many users, or if they're offered as loss leaders to drive prices down.
- It's suggested that OpenAI and Anthropic maintain high prices by manufacturing scarcity, using luxury branding, and gating access to 'frontier' models as status symbols.
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ai pricing

Show HN: CostPerPrompt – Live AI API pricing and real-workload cost calculators

Why are cached input tokens cheaper with AI services?

Managed agents are the new Lambda

Claude Opus 5 pricing and launch-day cost-per-task result

The real prices of frontier models. Tokens * Price, right?

The unbearable cheapness of open weight models