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...
Every major provider prices output at 4–5× input. xAI prices it at 2–3×. Grok 4.3 charges $1.25 per million input tokens and $2.50 per million output
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Every major provider prices output at 4–5× input. xAI prices it at 2–3×. Grok 4.3 charges $1.25 per million input tokens and $2.50 per million output — a ratio of 2.0. Grok 4.6 charges $2.00 and $6.00 — a ratio of 3.0. OpenAI, Anthropic and Google all sit at 5× across their current lineups; DeepSeek is at 4×.
That single design choice changes which workloads Grok is good at. For a support bot where input dwarfs output, the low ratio is irrelevant and Grok looks expensive next to Gemini Flash. For content generation, code synthesis or long-form drafting — where the model writes far more than it reads — a 2× ratio can beat models with cheaper input by a wide margin. This guide works out exactly where each case applies.
Per-million rates across providers, October 2026. The output-to-input ratio matters as much as the rate.
Grok's rates
Model
Input
Cached
Write
Output
Output ratio
Context
Long-context tier
Grok 4.3
$1.25
$0.20
None
$2.50
2.0×
1M
Above 200K: $2.50 / $5.00
Grok 4.6
$2.00
$0.50
None
$6.00
3.0×
500K
Above 200K: $4.00 / $12.00
Verified October 2026. Both models support the Batch API at 50% off. Neither charges a cache-write fee. Note that the newer 4.6 model has the smaller context window: 500K against 4.3's 1M.
The ratio, in one workload
Take a generation task: 1,000 requests, each reading a 1,000-token brief and writing a 4,000-token article. That is 1 million input tokens and 4 million output tokens — the inverse of a chat workload, and the shape where output price dominates.
Model
Input cost
Output cost
Total
Grok 4.3
$1.25
$10.00
$11.25
Gemini 3.8 Flash
$0.75
$15.00
$15.75
Grok 4.6
$2.00
$24.00
$26.00
Claude Sonnet 5
$2.00
$40.00
$42.00
GPT-6 Sol
$2.00
$40.00
$42.00
Grok 4.3 beats both $2/$10 models by 3.7× on this workload despite having a higher input rate than Gemini. The entire advantage comes from the output line: $2.50 against $10.00. Any team generating long outputs and reading short inputs — marketing copy, documentation, code scaffolding, synthetic data — is paying for output ratio whether they realise it or not.
The same models on a chat workload
Reverse the shape: 1,000 conversations, 4,000 input tokens and 600 output tokens each.
Model
Support chat cost
vs Grok 4.3
GPT-6 Luna
$0.70
9.3× cheaper
Gemini 3.8 Flash
$5.25
1.2× cheaper
Grok 4.3
$6.50
—
Claude Haiku 4.5
$7.00
1.1× more
Grok 4.6
$11.60
1.8× more
GPT-6 Sol
$14.00
2.2× more
On chat, Grok 4.3 is mid-table: cheaper than Claude Sonnet and GPT-6 Sol, more expensive than Gemini Flash and far more expensive than the budget tiers. The model does not become worse — the workload simply stops rewarding its design.
Caching is shallower than the industry standard
Grok's cached-input rate is 16% of base on 4.3 ($0.20 against $1.25) and 25% on 4.6 ($0.50 against $2.00). OpenAI, Anthropic and Google all charge 10%; DeepSeek charges 2%.
There is no cache-write fee, so caching is still free to try and profitable from the first reuse. But the saving per cached token is smaller: on a 45,000-token retrieval block reused across 1,000 requests, Grok 4.3 bills $8.99 in cached reads where Gemini 3.8 Flash bills $3.37.
Model
Cached RAG workload
Cache discount
DeepSeek V4.1 Flash
$2.97
98%
Gemini 3.8 Flash
$10.87
90%
Grok 4.3
$17.74
84%
Claude Sonnet 5
$29.10
90%
Grok 4.6
$38.48
75%
The 200K cliff and the shrinking window
Both Grok models apply a long-context tier above 200,000 input tokens, doubling the input rate and doubling the output rate: 4.3 moves to $2.50/$5.00, 4.6 to $4.00/$12.00. That is the same threshold Gemini uses and earlier than OpenAI's 272,000.
Grok 4.3's 1M context at a flat $1.25 below 200K is competitive. Grok 4.6's 500K window is the smallest of any non-budget model in this dataset — a surprising regression for a newer model, and a real constraint for document-heavy work.
How to tell whether your workload is output-heavy
The rule is mechanical: divide output tokens by input tokens. If the ratio is above 1, Grok's pricing helps you. If it is below 0.5, the low output rate is irrelevant and you are paying a premium on input.
Workload
Output : input
Grok 4.3 position
RAG question answering
0.02
Worst case — 98% of tokens are input
Support chat
0.15
Weak — input dominates
Code completion
0.30
Neutral
Summarisation
0.25
Neutral
Article generation
4.00
Best case — output dominates
Synthetic data
6.00
Best case
Most production systems are in the top half of that table: they read far more than they write. That is why the general advice to \"use the model with cheap output\" is usually wrong — and why Grok, despite a genuinely clever price structure, does not win most evaluations on cost.
The exception is worth naming precisely, because it is a large and growing category. Any system whose job is to produce rather than answer — drafting, scaffolding, translating a corpus, generating test cases — has an output ratio above 1, and there Grok 4.3's $2.50 output rate beats every $10 output model by a factor that no input discount can recover.
Batch compounds the ratio advantage
Grok 4.3 supports the Batch API at 50% off, which stacks multiplicatively with the ratio advantage rather than competing with it. On the generation workload above — 1,000 requests, 1,000 input and 4,000 output tokens — batch takes Grok 4.3 from $11.25 to $5.63, against $21.00 for Claude Sonnet 5 in batch. The ratio stays at 3.7×, but the absolute gap falls from $30.75 to $15.37 per thousand requests — which is what matters when the volume is in the millions.
When Grok makes sense
Output-heavy generation — the 2× ratio on 4.3 is the lowest in the market and beats $2/$10 models by 3.7× at a 4:1 output ratio.
Batch jobs with long outputs — 50% off plus the ratio advantage compounds.
1M-token documents under 200K — flat rate, no write fee.
Not for cheap chat — GPT-6 Luna is 9.3× cheaper on a support workload.
Not for deep-cache RAG — a 16% cache rate is the weakest discount among the major providers.
FAQ
How much does Grok 4 cost per million tokens?
Grok 4.3 costs $1.25 input and $2.50 output. Grok 4.6 costs $2.00 input and $6.00 output. Above 200,000 input tokens both move to a higher tier.
Why is Grok's output cheaper relative to input?
xAI prices output at 2–3× input rather than the 4–5× used by OpenAI, Anthropic, Google and DeepSeek. That makes Grok comparatively strong on generation-heavy workloads and comparatively weak on input-heavy ones.
Does Grok charge for prompt caching?
No write fee. Cached reads bill at $0.20 per million on 4.3 and $0.50 on 4.6 — 16% and 25% of their base input rates, both higher than the 10% industry standard.
Which Grok has the larger context window?
Grok 4.3 at 1,000,000 tokens. Grok 4.6 is capped at 500,000.
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