// head_to_head

DeepSeek V4 Flash 0423 vs DeepSeek V4 Pro 0813

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

DeepSeek V4 Pro 0813 scores higher, DeepSeek V4 Flash 0423 costs less — it depends on your workload.

DeepSeek V4 Pro 0813 is ahead by 12.0 points overall, and DeepSeek V4 Flash 0423 lists 19× cheaper per blended million tokens. Whether 12.0 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Flash 0423 also leads on measured cost per point of capability, at $0.0083 per point.

deepseek

DeepSeek V4 Flash 0423

Blended / 1M
$0.096
Context
1.0M
Released
Apr 24, 2026
Overall score
65.5
reasoningtool callingprompt caching

deepseek

DeepSeek V4 Pro 0813

Blended / 1M
$1.78
Context
1.0M
Released
Aug 12, 2026
Overall score
77.4
reasoningtool callingprompt caching

Specs and pricing

MetricDeepSeek V4 Flash 0423DeepSeek V4 Pro 0813
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

65.577.4win
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.0083win$0.0241
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$0.096win$1.78
Input price / 1M$0.077win$1.19
Output price / 1M$0.154win$3.56
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.015win$0.040
Context window1.0M1.0M
Max output tokens384K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — DeepSeek V4 Flash 0423 on top, DeepSeek V4 Pro 0813 below, both out of 100.

Agentic coding
37.6
54.9
Coding
69.2
77.2
Reasoning
70.6
85.8
Mathematics
79.6
95.1
Data analysis
68.0
79.2
Language
70.1
82.1
Instruction following
63.1
67.7

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadDeepSeek V4 Flash 0423DeepSeek V4 Pro 0813
Support chatbot

1.2K in / 400 out × 200K requests

$26.32/mo$487.56/mo
RAG assistant

8K in / 600 out × 100K requests

$46.12/mo$704.88/mo
Coding agent

40K in / 4K out × 20K requests

$39.35/mo$592.42/mo
Document extraction

20K in / 1.5K out × 50K requests

$85.31/mo$1397.88/mo
Bulk classification

500 in / 20 out × 5M requests

$176.78/mo$2752.20/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

DeepSeek V4 Flash 0423

Lowest measured cost per point of capability at $0.0083 per point — the gap compounds with every request.

Quality matters more than the bill

DeepSeek V4 Pro 0813

Highest overall LiveBench score of the two at 77.4.

The workload is coding or agentic work

DeepSeek V4 Pro 0813

Leads on agentic coding — 54.9 against 37.6.

DeepSeek V4 Flash 0423 vs DeepSeek V4 Pro 0813 FAQ

Which is better, DeepSeek V4 Flash 0423 or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 scores higher, DeepSeek V4 Flash 0423 costs less — it depends on your workload. DeepSeek V4 Pro 0813 is ahead by 12.0 points overall, and DeepSeek V4 Flash 0423 lists 19× cheaper per blended million tokens. Whether 12.0 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Flash 0423 also leads on measured cost per point of capability, at $0.0083 per point.

Is DeepSeek V4 Flash 0423 cheaper than DeepSeek V4 Pro 0813?

DeepSeek V4 Flash 0423 is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash 0423 lists at $0.096 per million tokens and DeepSeek V4 Pro 0813 at $1.78 — DeepSeek V4 Flash 0423 is 19× cheaper. Input and output are priced separately — DeepSeek V4 Flash 0423 charges $0.077 in and $0.154 out, DeepSeek V4 Pro 0813 charges $1.19 and $3.56 — so the model that looks cheaper flips depending on how output-heavy your workload is.

DeepSeek V4 Flash 0423 vs DeepSeek V4 Pro 0813: which scores higher on benchmarks?

DeepSeek V4 Flash 0423 scores 65.5 and DeepSeek V4 Pro 0813 scores 77.4 overall on LiveBench, the mean of its seven categories. That is a 12.0-point lead for DeepSeek V4 Pro 0813. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, DeepSeek V4 Flash 0423 or DeepSeek V4 Pro 0813?

DeepSeek V4 Flash 0423. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. DeepSeek V4 Flash 0423 works out at $0.0083 per point and DeepSeek V4 Pro 0813 at $0.0241.

Does DeepSeek V4 Flash 0423 or DeepSeek V4 Pro 0813 have a bigger context window?

They are effectively the same — 1.0M for DeepSeek V4 Flash 0423 and 1.0M for DeepSeek V4 Pro 0813.

Do DeepSeek V4 Flash 0423 and DeepSeek V4 Pro 0813 support prompt caching?

Both publish a cached-input rate: $0.015 per million for DeepSeek V4 Flash 0423 and $0.040 for DeepSeek V4 Pro 0813, against full input rates of $0.077 and $1.19. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • ScoresLiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.