DeepSeek V4 vs GPT-5.5 vs Claude Opus 4.7: API Cost Comparison

DeepSeek V4 changed the API cost conversation. Its V4 Flash model lists at $0.14 per 1M cache-miss input tokens, $0.028 per 1M cache-hit input tokens, and $0.28 per 1M output tokens. OpenAI GPT-5.5 lists at $5 per 1M input tokens, $0.50 cached input, and $30 per 1M output tokens. Claude Opus 4.7 lists at $5 per 1M input tokens and $25 per 1M output tokens, with an important tokenizer caveat.

If you run a SaaS feature, agent workflow, support bot, or RAG product on top of a frontier model, those differences are not abstract. They decide whether your product margin survives.

Official Pricing Table

All prices below are USD per 1M tokens and should be rechecked before procurement or production migration.

Model	Input	Cached Input	Output	Source
GPT-5.5	$5.00	$0.50	$30.00	OpenAI API pricing
GPT-5.4	$2.50	$0.25	$15.00	OpenAI API pricing
DeepSeek V4 Flash	$0.14 cache miss	$0.028	$0.28	DeepSeek API docs
DeepSeek V4 Pro	$1.74 cache miss	$0.145	$3.48	DeepSeek API docs
Claude Opus 4.7	$5.00	$0.50 cache hits	$25.00	Anthropic pricing

Anthropic also states that Claude Opus 4.7 may use up to 35% more tokens for the same fixed text because of its tokenizer. That means a nominally similar per-token price can produce a larger bill if your actual prompts tokenize longer.

Scenario 1: 1M Tokens, 70% Input / 30% Output, No Caching

Model	Input Cost	Output Cost	Total
GPT-5.5	$3.50	$9.00	$12.50
GPT-5.4	$1.75	$4.50	$6.25
DeepSeek V4 Pro	$1.22	$1.04	$2.26
DeepSeek V4 Flash	$0.10	$0.08	$0.18
Claude Opus 4.7, with 35% token overhead scenario	$4.73	$10.13	$14.85

DeepSeek V4 Flash is the clear cost outlier. The important question is not whether it is cheaper; it is whether it is good enough for your task.

Scenario 2: Same Workload With Cached Input

Assume the same 700K input and 300K output tokens, but all input tokens are cache hits.

Model	Cached Input Cost	Output Cost	Total
GPT-5.5	$0.35	$9.00	$9.35
GPT-5.4	$0.18	$4.50	$4.68
DeepSeek V4 Pro	$0.10	$1.04	$1.15
DeepSeek V4 Flash	$0.02	$0.08	$0.10
Claude Opus 4.7, with 35% token overhead scenario	$0.47	$10.13	$10.60

Caching helps every provider, but it mostly lowers input cost. If your workload generates long answers, output pricing still dominates the final bill.

Scenario 3: 100M Tokens Per Month For A SaaS Product

Assume 70M input tokens, 30M output tokens, and a 50% input cache hit rate. This is the default scenario in the calculator above.

Model	Blended Input	Output	Monthly Total
GPT-5.5	$192.50	$900.00	$1,092.50
GPT-5.4	$96.25	$450.00	$546.25
DeepSeek V4 Pro	$65.98	$104.40	$170.38
DeepSeek V4 Flash	$5.88	$8.40	$14.28
Claude Opus 4.7, with 35% token overhead scenario	$259.88	$1,012.50	$1,272.38

At this scale, the difference between model choices becomes product strategy. A usage-based SaaS feature that loses money on GPT-5.5 may be viable on DeepSeek V4 Flash if quality is acceptable.

Scenario 4: RAG Workload, 90% Input / 10% Output

RAG systems often process long context and produce short answers. Assume 900K input and 100K output tokens.

Model	Input Cost	Output Cost	Total
GPT-5.5	$4.50	$3.00	$7.50
GPT-5.4	$2.25	$1.50	$3.75
DeepSeek V4 Pro	$1.57	$0.35	$1.92
DeepSeek V4 Flash	$0.13	$0.03	$0.16
Claude Opus 4.7, with 35% token overhead scenario	$6.08	$3.38	$9.45

For RAG, prompt caching and chunk reuse can matter as much as the model's base price. You should measure cache hit rate before forecasting your production bill.

Which Model Is The Best Value?

Use Case	Start With	Why
High-volume, cost-sensitive chatbot	DeepSeek V4 Flash	Lowest listed price by a wide margin
Complex reasoning, still cost-sensitive	DeepSeek V4 Pro	More expensive than Flash, still far below GPT-5.5 / Opus output pricing
OpenAI ecosystem lock-in	GPT-5.4	Same API family at half GPT-5.5 price
Best OpenAI capability	GPT-5.5	Choose only if capability delta justifies 2x GPT-5.4 pricing
Claude-specific workflows	Claude Opus 4.7	Measure actual tokenization before budgeting

The calculator on this page is deliberately simple. It does not rank quality, latency, safety, uptime, privacy posture, or enterprise discounts. It answers one narrow question: what happens to your bill when the token mix and cache rate change?

Cost Formula

Use this formula when you model your own workload:

monthly cost =
  input_tokens_millions * blended_input_price
  + output_tokens_millions * output_price

blended_input_price =
  cache_hit_rate * cached_input_price
  + (1 - cache_hit_rate) * cache_miss_input_price

For Claude Opus 4.7, run the same prompts through the tokenizer before committing to a budget. The article scenarios above show a 35% token-count adjustment because Anthropic flags that as an upper-end caveat for the new tokenizer.

FAQ

Is DeepSeek V4 cheaper than GPT-5.5?

Yes on official token pricing. DeepSeek V4 Flash is dramatically cheaper than GPT-5.5 on both input and output tokens. Whether it is a better product choice depends on quality, latency, reliability, and compliance needs.

Is GPT-5.5 twice as expensive as GPT-5.4?

On OpenAI's official pricing page, GPT-5.5 lists at exactly 2x GPT-5.4 pricing for input, cached input, and output tokens.

Why does Claude Opus 4.7 need a tokenizer adjustment?

Anthropic states that Claude Opus 4.7's tokenizer may use up to 35% more tokens for the same fixed text. If you compare only per-token rates without measuring token counts, you can underestimate Claude's real bill.

Should I switch my production app to DeepSeek V4 Flash?

Run a real evaluation first. Compare output quality, latency, safety behavior, uptime, rate limits, and cost on your actual prompts. The price gap is large enough to justify testing, but price alone is not a production migration plan.

Related Reading

• How Much Does GPT Image 2 Cost Per Image
• GPT Image 2 Inference Cost Deep Dive
• What Is a Tokenizer and Why Does It Matter for Images
• AI Image API Cost Calculator

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