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DeepSeek V4 Pro (0813) vs DeepSeek V4.1 Flash

vs

Which one, when

Both share a 1,000,000-token context and 393,216-token max output with chat, code, reasoning and tool use, so the split is price and inputs: deepseek-v4-pro-0813 costs $1.32 per million input and $3.96 per million output, while the newer deepseek-v4.1-flash runs $0.3 and $1.2, roughly 4.4x cheaper on input and 3.3x on output, with cache reads at $0.03 versus $0.132. Pick deepseek-v4.1-flash for high-volume work or anything needing image input, since it adds vision. Reach for deepseek-v4-pro-0813 if you are pinned to that text-only Pro generation.

Benchmarks

LeadsAbove averageNo peer higherDeepSeek V4 Pro (0813)214 / 211 / 21DeepSeek V4.1 Flash1316 / 194 / 19

15 measured on both.

DeepSeek V4 Pro (0813) DeepSeek V4.1 Flash other models measured peer average no peer scored higher
NL2Repo
61.5%
64%
Cybergym
83.3%
no peer scored higher 88.1%
GDPval-AA v2 Elo · 1508-1769 per Z.ai · 2026-09-04
1590
N/A
GPQA Diamond
92.4%
90.9%
Agents' Last Exam
25.7%
31.8%
BabyVision with tools
N/A
89.6%

Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

DeepSeek V4 Pro (0813) DeepSeek V4.1 Flash Δ
Input / 1M tokens $1.32 $0.3 4.4×
Output / 1M tokens $3.96 $1.2 3.3×
Cache read / 1M tokens $0.132 $0.03 4.4×
Cache write no separate charge no separate charge -

Rates from the live catalog at build time; each model page carries the current card.

Where they sit - input price per 1M tokens across all 69 chat models on this billing unit (log scale)

Capabilities

DeepSeek V4 Pro (0813) DeepSeek V4.1 Flash
Tool use yes yes
Thinking control always on yes - vendor dial not published
Structured output yes -
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime no fixed TTL (evicted when unused) no fixed TTL (evicted when unused)
Minimum cached prefix not published not published

Specs

DeepSeek V4 Pro (0813) DeepSeek V4.1 Flash
Input modalities text text image
Output modalities text text
Released 2026-08-13 2026-09-10
Context window 1M 1M
Max output 393K 393K
Thinking parameter reasoning_effort -
Accepted values
reasoning_effort
  • the model card documents low
  • high
  • max
-

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4 Pro (0813) · DeepSeek V4.1 Flash

One prompt, both models - measured through the gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. CHECK exactly 3 sentences

DeepSeek V4 Pro (0813) passed · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

out 308 tok (+226 thinking) latency 5.1 s

DeepSeek V4.1 Flash passed · 3 sentences

Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.

out 423 tok (+327 thinking) latency 14.6 s

Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. CHECK fix passes tests

DeepSeek V4 Pro (0813) passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

out 1315 tok (+1130 thinking) latency 16.9 s

DeepSeek V4.1 Flash passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.

out 918 tok (+733 thinking) latency 11.4 s

Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. CHECK valid JSON, schema exact

DeepSeek V4 Pro (0813) passed · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

out 2155 tok (+2121 thinking) latency 26.0 s

DeepSeek V4.1 Flash passed · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}

out 1707 tok (+1667 thinking) latency 15.1 s

Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. CHECK 120 words, 0 banned words

DeepSeek V4 Pro (0813) passed · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

out 2845 tok (+2694 thinking) latency 25.5 s

DeepSeek V4.1 Flash passed · 116 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.

out 917 tok (+770 thinking) latency 9.0 s

Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.

Switch between them with one line

Both ids are in every tab below - the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro-0813",
    # model="deepseek-v4.1-flash",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Which is cheaper, DeepSeek V4 Pro (0813) or DeepSeek V4.1 Flash?

DeepSeek V4.1 Flash is cheaper on input / 1m tokens ($0.3 vs $1.32, 4.4× apart). Other rows may point the other way - the table above carries the full card, and real cost depends on your mix.

Can I A/B test DeepSeek V4 Pro (0813) against DeepSeek V4.1 Flash without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key - switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.

Do DeepSeek V4 Pro (0813) and DeepSeek V4.1 Flash support prompt caching?

Yes - both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

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