The landscape of Artificial Intelligence has shifted toward high-efficiency architectures. For software engineers, DevOps teams, and tech startups building AI-powered applications, choosing between the ChatGPT API (OpenAI) and the DeepSeek API is no longer just a benchmark comparison—it is a critical infrastructure financial decision. While OpenAI’s flagship models remain an industry standard for multi-modal tasks, DeepSeek has disrupted traditional market economics by offering comparable performance at a fraction of the operational cost. This deep dive evaluates the technical infrastructure, raw token pricing, and cost-optimization strategies—such as prompt caching —to help you scale production applications efficiently. Core Architecture and API Infrastructure Efficiency To understand why API pricing differs so dramatically between t...
Most cost reductions come from three structural changes, not from prompt micro-tuning. Done in order, they typically remove half to two thirds of a bill without a measurable quality drop. Step 1: Find where the money actually goes Log cost per request alongside the feature, endpoint and user tier that produced it. In most applications a small share of requests dominates spend: long-context calls, agent loops that iterate, and retries. Optimising a high-volume cheap endpoint feels productive and moves the total by almost nothing. Step 2: Route by difficulty Send every request to the cheapest model that reliably passes evaluation for that task class, and reserve the flagship model for requests that genuinely need it. The spread makes this worthwhile: $0.10 on GPT-6 Luna against $10.00 on Claude Fable 5.1, a 100x difference on input. A router needs a cheap classifier — a few hundred input tokens on a small model — and a fallback path when the small model's confidence is low. Start w...
Prompt caching lets a provider reuse work it has already done. When you send a request whose opening tokens exactly match a prefix the provider has processed recently, it skips that portion and bills the reused tokens at a cache-read rate instead of the full input rate. For applications with long system prompts, fixed schemas or repeated document context, this is the largest single lever on API spend. How much cheaper is a cache read The discount differs sharply by provider. The table below shows the cache-read price against the fresh input price for every model in the dataset that publishes one. Model Input Cached input Discount Cache write DeepSeek V4.1 Flash $0.30 $0.0060 98% — Claude Fable 5.1 $10.00 $0.25 97.5% $12.50 DeepSeek V4 Pro $1.32 $0.04 96.7% — GPT-6.1 Sol $2.00 $0.10 95% $2.50 GPT-6 Astra $10.00 $1.00 90% $12.50 GPT-6 Sol $2.00 $0.20 90% $2.50 GPT-6 Luna $0.10 $0.01 90% $0.13 GPT-5.6 Sol $4.00 $0.40 90% $5.00 GPT-5.6 Terra $2.00 $0.20 90% $2.50 GPT-5.6 Luna...
Anthropic's price list looks almost boringly flat. Five models, four of them clustered between $1 and $10 per million input tokens, and one workhorse
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Anthropic's price list looks almost boringly flat. Five models, four of them clustered between $1 and $10 per million input tokens, and one workhorse — Claude Sonnet 5 — sitting at $2/$10 since August 2026. Compared with OpenAI's thirteen tiers and Google's introductory discounts, it reads like a company that has stopped competing on price.
That reading is wrong, and it costs teams money in both directions. Three rules inside Anthropic's pricing decide whether Claude is the expensive option or the cheap one: a cache-write premium that is the highest in the market, a cache-read cut on the flagship that is the deepest, and the absence of a long-context surcharge up to one million tokens. The last one alone can halve a bill against a comparable OpenAI model.
Output tokens cost 4–5× input tokens on every current model.
Every Claude rate, October 2026
Model
Input
Cached
Write
Output
Context
Batch
Claude Fable 5.1
$10.00
$0.25
$12.50
$50.00
1M
Yes
Claude Opus 5
$5.00
$0.50
$6.25
$25.00
1M
Yes
Claude Sonnet 5
$2.00
$0.20
$2.50
$10.00
1M
Yes
Claude Sonnet 4.6
$3.00
$0.30
$3.75
$15.00
1M
Yes
Claude Haiku 4.5
$1.00
$0.10
$1.25
$5.00
200K
Yes
Verified October 2026. Fable 5.1 shipped on 1 September 2026 and cut its cached-read rate by 75%, from $1.00 to $0.25. Sonnet 5's $2/$10 was confirmed permanent on 10 August 2026. Opus 5 kept Opus 4.8's rates when it launched on 24 July 2026.
Two workloads, five bills
The first workload is a support assistant: 1,000 conversations, 4,000 input tokens and 600 output tokens each. The second is a RAG pipeline that reuses a 45,000-token retrieval block on a 50,000-token prompt, with a 1,000-token answer.
Model
Support chat
Cached RAG
Cache saving on RAG
Claude Fable 5.1
$70.00
$111.80
−66%
Claude Opus 5
$35.00
$72.76
−63%
Claude Sonnet 5
$14.00
$29.10
−74%
Claude Sonnet 4.6
$21.00
$43.66
−73%
Claude Haiku 4.5
$7.00
$14.55
−69%
Without caching, that RAG workload on Sonnet 5 costs $110: 50 million input tokens at $2 plus one million output at $10. With caching it costs $29.10. The saving is not a marginal optimisation — it is a different product economics, and it comes almost entirely from the cached-input rate being 10% of base.
The cache-write premium: highest in the market, still worth paying
Anthropic charges more than anyone to write a cache. Fable 5.1 bills $12.50 per million tokens to store a prefix, against a $10 base input rate. Sonnet 5 bills $2.50 against $2.00. That is a 25% premium, and it looks like a penalty until you do the division.
For Sonnet 5: 2.50 ÷ (2.00 − 0.20) = 1.39. For Opus 5: 6.25 ÷ 4.50 = 1.39. For Haiku 4.5: 1.25 ÷ 0.90 = 1.39. The number is identical across the range because Anthropic prices the write at exactly 1.25× input and the read at exactly 10%.
The practical rule is therefore unusually clean: if a prefix is reused twice, cache it. Not ten times — twice. A support bot that handles ten conversations an hour with the same 3,000-token system prompt is throwing away roughly 85% of its input spend.
Fable 5.1 is the exception that proves the rule. Its cached read is $0.25 — 2.5% of base, not 10% — because of the September cut. Break-even there is 12.50 ÷ 9.75 = 1.28 calls. Anthropic's most expensive model has the fastest cache payback in its own lineup.
No long-context cliff — and that is the real differentiator
This is where Anthropic separates from OpenAI. Claude bills the same rate at 900,000 tokens as at 9,000. OpenAI moves every GPT-6 model to a long-context tier above 272,000 input tokens, where GPT-6 Sol doubles from $2/$10 to $4/$15.
Put a 500,000-token document in front of both:
Model
500K input
2K output
Cost per call
Claude Sonnet 5
$1.00
$0.02
$1.02
GPT-6 Sol (long-context tier)
$2.00
$0.03
$2.03
Claude Opus 5
$2.50
$0.05
$2.55
GPT-6 Astra (long-context tier)
$10.00
$0.15
$10.15
At 500K tokens, Sonnet 5 is exactly half the price of GPT-6 Sol — despite identical sticker rates. The difference is not a discount; it is the absence of a surcharge. For contract review, codebase analysis, long audio transcripts or multi-document research, that gap compounds across every call.
Google applies a similar surcharge above 200,000 tokens on Gemini 3.1 Pro and both Grok models, so the pattern is industry-wide. Anthropic is currently the only major provider that sells a full million-token window at a flat rate — and it is the single strongest reason to choose Claude for long-document work. The mechanics are in long-context cost explained.
Batch: half price, same 24-hour wait
All five models support the Batch API at roughly 50% off input and output. A nightly classification job of 10,000 requests — 10,000 input and 2,000 output tokens each — costs $400 on Sonnet 5 at list and $200 in batch. Nothing about the model changes; only the deadline does.
Which Claude to use
High-volume classification, moderation, extraction — Haiku 4.5 at $1/$5. Its 200K context is the constraint, not its price.
Default product workhorse — Sonnet 5 at $2/$10. It is the only model here that is simultaneously cheap, 1M-context and cache-friendly.
Long-document analysis — Sonnet 5 again, for the reason above: no surcharge, and 74% of the RAG bill disappears when the document is cached.
Hard reasoning where errors are expensive — Opus 5 at $5/$25. It costs 2.5× Sonnet and buys judgement, not throughput.
Flagship evaluation work — Fable 5.1 at $10/$50, but only with caching enabled; uncached RAG on Fable is the most expensive line item in this entire dataset.
FAQ
How much does Claude Sonnet 5 cost per million tokens?
$2.00 input, $10.00 output, $0.20 cached input and $2.50 to write a cache entry. Batch mode halves input and output. There is no long-context surcharge at any point up to the 1M window.
Is Claude more expensive than GPT-6?
At identical sticker prices — $2/$10 for both Sonnet 5 and GPT-6 Sol — the answer depends on prompt length. Below 272K tokens they are close, with GPT-6.1 Sol slightly ahead thanks to a $0.10 cached rate. Above 272K, Claude is half the price because OpenAI doubles its rates and Anthropic does not.
Why is the cache-write fee so high?
It is 1.25× the input rate on every model, which is higher than Google's and DeepSeek's (both zero) and OpenAI's (also 1.25×). It still pays back on the second reuse, so the premium matters only for prompts used exactly once.
Does Haiku support the full context window?
No. Haiku 4.5 caps at 200,000 tokens; every other Claude model in this dataset reaches 1,000,000.
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