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...
Google's Gemini price list is the only one in this dataset with an expiry date. Gemini 3.8 Flash and 3.7 Flash both bill $0.75 per million input token
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Google's Gemini price list is the only one in this dataset with an expiry date. Gemini 3.8 Flash and 3.7 Flash both bill $0.75 per million input tokens and $3.75 per million output — and both double on 1 January 2027. The introductory rate is not a rumour or a footnote in a blog post; it is a promotional price with a published end date, which makes it the single most important line in any 2027 budget built on Gemini.
Everything else about the lineup is genuinely competitive: six models, a million-token window on five of them, no cache-write fee at all, and the cheapest output rate of any non-Chinese provider. This guide separates the rates that will hold from the ones that will not.
The cheapest model depends on the input-to-output ratio of your workload.
Every Gemini rate, October 2026
Model
Input
Cached
Write
Output
Context
Long-context tier
Gemini 3.8 Flash (intro)
$0.75
$0.075
None
$3.75
1M
Above 200K: $1.50 / $7.50
Gemini 3.7 Flash (intro)
$0.75
$0.075
None
$3.75
1M
Above 200K: $1.50 / $7.50
Gemini 3.5 Flash
$1.50
$0.15
None
$9.00
1M
None
Gemini 3.5 Flash-Lite
$0.30
$0.03
None
$2.50
1M
None
Gemini 3.1 Pro (preview)
$2.00
$0.20
None
$12.00
1M
Above 200K: $4.00 / $18.00
Gemini 2.5 Pro
$1.25
$0.125
None
$10.00
1M
Above 200K: $2.50 / $15.00
Verified October 2026. The Flash 3.8 and 3.7 introductory rates end on 31 December 2026 and double on 1 January 2027. Gemini 3.1 Pro is preview pricing. All six models support the Batch API.
What a real workload costs
Support chat: 1,000 conversations, 4,000 input and 600 output tokens each. Cached RAG: 1,000 requests with a 50,000-token prompt, 45,000 of it a reusable retrieval block, and a 1,000-token answer.
Model
Support chat
Cached RAG
Nightly batch
Gemini 3.8 Flash
$5.25
$10.87
$150 → $75 in batch
Gemini 3.7 Flash
$5.25
$10.87
$150 → $75 in batch
Gemini 3.5 Flash
$11.40
$22.37
$300 → $150 in batch
Gemini 3.5 Flash-Lite
$2.70
$5.35
$80 → $40 in batch
Gemini 3.1 Pro
$15.20
$30.99
$440 → $220 in batch
Gemini 2.5 Pro
$11.00
$22.12
$325 → $162.50 in batch
Two observations. Flash-Lite is the cheapest way to run a support bot of any model in this dataset except GPT-6 Luna — $2.70 per thousand conversations. And 3.8 Flash sits within 2× of Flash-Lite on chat but roughly 2× cheaper than 3.5 Flash, which is the older model it replaced: Google has been cutting Flash prices, not raising them, right up until the January deadline.
The January doubling: budget for 2×, not 1×
On 1 January 2027, Gemini 3.8 Flash and 3.7 Flash move from $0.75/$3.75 to $1.50/$7.50. The same support workload above goes from $5.25 to $10.50 per thousand conversations. The nightly batch job goes from $75 to $150. A product doing 200,000 conversations a month moves from $1,050 to $2,100 — a $12,600 annual difference that appears in no invoice until it does.
Workload
2026 (intro)
2027 (standard)
Increase
1,000 support conversations
$5.25
$10.50
+100%
1,000 cached RAG requests
$10.87
$21.74
+100%
Nightly batch (10K requests)
$75.00
$150.00
+100%
200K conversations / month
$1,050
$2,100
+$12,600/yr
The honest reading is not "Gemini is a trap". It is that the introductory price is a trial, and the standard price is the real comparison. At $1.50/$7.50, 3.8 Flash still undercuts Claude Sonnet 5 on input and matches it on output, but it stops being the obvious default. Any model-selection decision made in late 2026 should be stress-tested against the 2027 rate.
The 200K cliff is lower than OpenAI's
Google applies a long-context surcharge earlier than anyone else. Above 200,000 input tokens, Gemini 3.8 Flash doubles to $1.50/$7.50 and Gemini 3.1 Pro moves from $2/$12 to $4/$18. OpenAI's threshold is 272,000; Anthropic has none at all up to 1M.
For a 300,000-token document, the difference is concrete:
Model
300K input cost
vs base rate
Claude Sonnet 5
$0.60
1×
Gemini 3.8 Flash (tier)
$0.45
2× its own base
Gemini 3.1 Pro (tier)
$1.20
2× its own base
GPT-6 Sol (tier)
$1.20
2× its own base
Gemini is still the absolute cheapest at that length — but the surcharge halves the advantage, and it arrives 72,000 tokens earlier than OpenAI's. If your retrieval layer routinely lands between 200K and 272K tokens, Gemini charges the tier and GPT-6 does not. Details in long-context cost explained.
Free cache writes change the caching decision
Google publishes no cache-write fee on any Gemini model. OpenAI and Anthropic both charge 1.25× the input rate to store a prefix; Google charges nothing, and then bills cached reads at 10% of base.
That flips the usual calculation. On Anthropic you need two uses to break even. On Gemini, caching is free to try: the first reuse is already profitable, and a prefix used twice costs 55% less than sending it twice uncached. For RAG pipelines with a stable document block, the cached RAG row above — $10.87 instead of $21.74 — is the entire difference between a viable product and a marginal one.
Batch and the rest
All six models support the Batch API at roughly half price, so the nightly job column above is realistic rather than theoretical. There is no batch tier on Flash-Lite's cheaper sibling — there is no cheaper sibling — and no off-peak discount of the kind DeepSeek offers.
Which Gemini to use
Cost-sensitive chat and extraction — Flash-Lite at $0.30/$2.50. It is the cheapest output rate outside China and the cheapest cached rate in the entire dataset at $0.03.
Default product model through 2026 — 3.8 Flash at $0.75/$3.75, with a plan for 2× in January.
Coding and agent workloads — 3.7 Flash, same price as 3.8 but tuned for tool use.
Long documents under 200K — 3.1 Pro at $2/$12, or 2.5 Pro at $1.25/$10 if the older model still passes your evals.
Anything above 200K tokens — compare against Claude Sonnet 5 before defaulting to Gemini; the surcharge is real.
FAQ
Is Gemini really doubling in price?
The introductory rates for Gemini 3.8 Flash and 3.7 Flash end on 31 December 2026 and double to $1.50 input and $7.50 output per million tokens on 1 January 2027. The other four models in this dataset are not affected by that change.
Does Gemini charge for prompt caching?
No write fee, and cached reads bill at 10% of the input rate. That is the most forgiving cache policy of the four major Western providers.
When does the long-context tier start?
Above 200,000 input tokens on 3.8 Flash, 3.7 Flash, 3.1 Pro and 2.5 Pro. Gemini 3.5 Flash and Flash-Lite have no long-context tier.
What is the cheapest Gemini model?
Gemini 3.5 Flash-Lite at $0.30 input and $2.50 output per million tokens, with a $0.03 cached rate.
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