DeepSeek V4 Pro API Pricing: Frontier Reasoning at Open-Weights Economics
Comprehensive DeepSeek V4 Pro API pricing analysis ($1.32/M input, $3.96/M output), off-peak discounts, prompt caching breaks, and frontier reasoning benchmarks.
Full specs, context window and API limits →How much does DeepSeek V4 Pro cost per million tokens?
DeepSeek V4 Pro costs $1.32 per million input tokens and $3.96 per million output tokens ($1.98/M blended at 3:1). Delivers state-of-the-art mathematical and code reasoning at disruptive price points. Verified 2026-09-08.
Verified 2026-09-07 — source
How much does DeepSeek V4 Pro cost per 1,000 requests?
Computed from generated token pricing. Each row assumes the listed input and output tokens per request; output is adjusted by this model's measured 3.30× verbosity factor.
| Request shape | Input tokens | Output tokens | Cost / 1,000 requests |
|---|---|---|---|
| Short | 100 | 50 | $0.7854 |
| Medium | 1,000 | 500 | $7.8540 |
| Long | 4,000 | 2,000 | $31.4160 |
Formula: ((input price × input tokens) + (output price × output tokens × verbosity factor)) ÷ 1,000,000 × 1,000. Assumptions: short 100/50, medium 1,000/500, long 4,000/2,000 input/output tokens per request. Verbosity run: 2026-06-16T20:31:30.728Z.
Batch 61 · exact-model pricing decision contributions · verified 2026-09-07
Exact model boundary: DeepSeek DeepSeek V4 Pro (deepseek-v4-pro). Pricing cards, context tiers, caching multipliers, and task pages remain fact owners.
Peak versus off-peak UTC schedule discount ledger
Frozen Batch 61 scenario board. Formula / deterministic rule: bill = (standard_hours * peak_rate + off_peak_hours * off_peak_rate); off-peak window is 16:30-00:30 UTC Boundary: Owns scheduled off-peak pricing calculations for DeepSeek V4 Pro.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-deepseek-v4-pro-m1-r1peak daytime processing | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=peak daytime processing; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — peak daytime processing is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m1-r2off-peak scheduled batch | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=off-peak scheduled batch; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — off-peak scheduled batch is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m1-r350/50 blended daily traffic | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=50/50 blended daily traffic; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 50/50 blended daily traffic is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m1-r4weekend batch backlog | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=weekend batch backlog; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — weekend batch backlog is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m1-r5burst schedule override | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=burst schedule override; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — burst schedule override is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m1-r6unsupported time window | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=unsupported time window; schedule window; UTC hours; input rate; output rate; 100K call cost; net savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unsupported time window has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: DeepSeek API official pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Prompt cache write/read economics and hit-rate sensitivity
Frozen Batch 61 scenario board. Formula / deterministic rule: net_input = uncached_tokens * 0.27 + cached_tokens * 0.07; cache read offers ~74% discount Boundary: Owns DeepSeek context caching economics.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-deepseek-v4-pro-m2-r10% cache hit (pure uncached) | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=0% cache hit (pure uncached); cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 0% cache hit (pure uncached) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m2-r225% occasional prefix reuse | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=25% occasional prefix reuse; cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 25% occasional prefix reuse is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m2-r350% repeated document QA | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=50% repeated document QA; cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 50% repeated document QA is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m2-r475% agent system prompt reuse | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=75% agent system prompt reuse; cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 75% agent system prompt reuse is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m2-r590% high-frequency API loop | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=90% high-frequency API loop; cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 90% high-frequency API loop is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m2-r6unsupported cache structure | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=unsupported cache structure; cache hit rate; uncached input tokens; cached input tokens; effective input cost; total request cost; savings; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unsupported cache structure has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: DeepSeek API official pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Thinking-token output budget and reasoning break-even
Frozen Batch 61 scenario board. Formula / deterministic rule: total_cost = (input * 0.27 + (thinking_out + answer_out) * 1.10) / 1M; visible CoT is billed as output Boundary: Owns reasoning token expense forecasting.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-deepseek-v4-pro-m3-r1direct answer (no thinking) | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=direct answer (no thinking); reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — direct answer (no thinking) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m3-r21K thinking tokens | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=1K thinking tokens; reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 1K thinking tokens is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m3-r34K moderate reasoning | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=4K moderate reasoning; reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 4K moderate reasoning is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m3-r416K deep mathematical proof | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=16K deep mathematical proof; reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 16K deep mathematical proof is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m3-r532K complex software design | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=32K complex software design; reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 32K complex software design is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-deepseek-v4-pro-m3-r6uncontrolled thinking loop | model=deepseek-v4-pro; provider=DeepSeek; slug=deepseek-v4-pro; scenario=uncontrolled thinking loop; reasoning depth; thinking tokens; answer tokens; total output tokens; cost per call; budget state; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — uncontrolled thinking loop is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
First-party provenance: DeepSeek API documentation; verification date 2026-09-07. Missing or conflicting joins fail closed.
Method and limitations: formulas are deterministic; observed and assumed inputs are labeled; no missing provider, host, account, region, realm, alias, snapshot, revision, weight, artifact, control, tool, modality, workload, rate-period, timestamp, or result is transferred. Run the DeepSeek V4 Pro Batch 61 scenario →
Continuous SEO Builder · Batch 73 Audit · 2026-09-08Owner:
deepseek-v4-proDeepSeek V4 Pro API Pricing: Frontier Reasoning at Open-Weights Economics
DeepSeek V4 Pro costs $1.32 per million input tokens and $3.96 per million output tokens ($1.98/M blended at 3:1). Delivers state-of-the-art mathematical and code reasoning at disruptive price points. Verified 2026-09-08.
Module 1 · DeepSeek V4 Pro Frontier Token Rate Card
Blended Cost = (Input Tokens × $1.32 + Output Tokens × $3.96) / 1,000,000
DeepSeek V4 Pro provides frontier-class cognitive power at sub-$2 per million blended economics.
Boundary: Standard pay-as-you-go rate card; off-peak window and prompt caching discounts apply.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Competitive programming solution verification (2K in, 1K out): $0.006600 per problem |
| Scenario 2 | Complex software architecture review (16K in, 3K out): $0.033000 per review pass |
| Scenario 3 | Mathematical proof generation and check (4K in, 2K out): $0.013200 per theorem |
| Scenario 4 | Full-stack pull request defect scan (32K in, 4K out): $0.058080 per PR audit |
| Scenario 5 | Autonomous multi-step coding agent loop (64K in, 6K out): $0.108240 per task cycle |
| Scenario 6 | Monthly 50M token enterprise developer tier: $99.00 total infrastructure spend |
Module 2 · DeepSeek V4 Pro Off-Peak Window & Prompt Caching Savings
Discounted Cost = (Cached Input × $0.132 + Off-Peak Tokens × 0.50 + Generation × $3.96) / 1,000,000
Leveraging off-peak scheduling and context caching minimizes operational overhead for dev teams.
Boundary: Evaluates 90% prompt caching discount plus 50% off-peak window tariff reduction (16:30–00:30 UTC).
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Off-peak batch pipeline processing: cuts overall input/output token tariffs by 50% |
| Scenario 2 | Codebase AST context cache (50K tokens): 84% prompt cost reduction on repeated runs |
| Scenario 3 | Combined off-peak + prompt cache: effective blended rate drops below $0.65/M |
| Scenario 4 | Zero cache retention storage fees charged during active continuous sessions |
| Scenario 5 | Enables large-scale nightly regression testing of massive enterprise monorepos |
| Scenario 6 | Net infrastructure budget reduction of 62% for asynchronous developer CI pipelines |
Module 3 · DeepSeek V4 Pro vs Western Flagship TCO Comparison
TCO Savings = Western Flagship ($15-$30/M) - DeepSeek V4 Pro ($1.98/M) = 87%-93% Net Cost Reduction
Delivers elite code and analytical reasoning performance at less than one-tenth Western flagship prices.
Boundary: Compares DeepSeek V4 Pro against frontier Western alternatives on code and math tasks.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | DeepSeek V4 Pro ($1.98/M blended) vs Claude Opus 5 ($30.00/M blended): 93.4% cost savings |
| Scenario 2 | DeepSeek V4 Pro vs GPT-5.6 Sol ($8.00/M blended): 75.3% operational cost reduction |
| Scenario 3 | Matches or exceeds Western frontier models on HumanEval and MATH-500 benchmarks |
| Scenario 4 | High-volume production tier (100M tokens/mo): saves >$2,500/mo compared to Western flagships |
| Scenario 5 | Standard OpenAI-compatible REST API allows effortless drop-in gateway integration |
| Scenario 6 | Strongly recommended for cost-conscious AI engineering and high-throughput coding agents |
How fast is DeepSeek V4 Pro?
Tokens / sec
68
TTFT
480 ms
Rank
#22 of 31
$ / M ÷ t/s
$0.03
Measured with 5 runs on a fixed prompt — see the full methodology.
How much does DeepSeek V4 Pro cost at scale?
| Tokens / month | Est. cost (blended 3:1) |
|---|---|
| 100,000 | $0.20 |
| 1,000,000 | $1.98 |
| 10,000,000 | $19.80 |
| 100,000,000 | $198.00 |
How does DeepSeek V4 Pro compare with other models?
What is DeepSeek V4 Pro best for?
What should you explore next for DeepSeek V4 Pro?
Looking for a cheaper option?
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Which DeepSeek V4 Pro head-to-head comparisons are available?
DeepSeek V4 Pro vs Claude Opus 4.8DeepSeek V4 Pro vs Claude Sonnet 5DeepSeek V4 Pro vs Gemini 3.1 ProDeepSeek V4 Pro vs Gemini 3.7 Flash
What are common questions about DeepSeek V4 Pro?
Is DeepSeek V4 Pro cheaper than Claude Haiku 4.5?
DeepSeek V4 Pro costs $1.98/M blended tokens, Claude Haiku 4.5 costs $2.00/M — DeepSeek V4 Pro is cheaper.
How much does 1 million tokens cost with DeepSeek V4 Pro?
At a 3:1 input:output ratio, 1 million blended tokens costs approximately $1.98. Pure input costs $1.32/M; pure output costs $3.96/M.
What does DeepSeek V4 Pro cost at high volume?
At 100 million blended tokens a month, DeepSeek V4 Pro costs approximately $198.00. See the cost-at-scale table below for other volumes.