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Seed-2.0-Code vs DeepSeek V4 Pro 0423

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

Seed-2.0-Code and DeepSeek V4 Pro 0423 are priced within ~10% of each other.

Seed-2.0-Code does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

bytedance-seed

Seed-2.0-Code

Blended / 1M
$1.13
Context
262K
Released
Aug 12, 2026
Overall score
Not evaluated
reasoningtool callingimage inputvideo input

deepseek

DeepSeek V4 Pro 0423

Blended / 1M
$1.19
Context
1.0M
Released
Apr 24, 2026
Overall score
71.6
reasoningtool callingprompt caching

Specs and pricing

MetricSeed-2.0-CodeDeepSeek V4 Pro 0423
LiveBench overall

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

71.6
Cost per point

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

$0.0261
Blended price / 1M

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

$1.13$1.19
Input price / 1M$0.500win$0.955
Output price / 1M$3.00$1.91win
Cached input / 1M

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

$0.080
Context window262K1.0Mwin
Max output tokens131K384Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Seed-2.0-Code on top, DeepSeek V4 Pro 0423 below, both out of 100.

Agentic coding
42.6
Coding
70.0
Reasoning
82.7
Mathematics
90.7
Data analysis
74.5
Language
78.1
Instruction following
62.4

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.

WorkloadSeed-2.0-CodeDeepSeek V4 Pro 0423
Support chatbot

1.2K in / 400 out × 200K requests

$360.00/mo$319.06/mo
RAG assistant

8K in / 600 out × 100K requests

$580.00/mo$528.58/mo
Coding agent

40K in / 4K out × 20K requests

$640.00/mo$426.68/mo
Document extraction

20K in / 1.5K out × 50K requests

$725.00/mo$1,055/mo
Bulk classification

500 in / 20 out × 5M requests

$1,550/mo$2,141/mo
Run these two through the cost calculator

Which should you pick?

You need to fit large documents in one call

DeepSeek V4 Pro 0423

Wider context window — 1.0M against 262K.

Seed-2.0-Code vs DeepSeek V4 Pro 0423 FAQ

Which is better, Seed-2.0-Code or DeepSeek V4 Pro 0423?

Seed-2.0-Code and DeepSeek V4 Pro 0423 are priced within ~10% of each other. Seed-2.0-Code does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is Seed-2.0-Code cheaper than DeepSeek V4 Pro 0423?

They cost about the same. Both land near $1.13 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.

Does Seed-2.0-Code or DeepSeek V4 Pro 0423 have a bigger context window?

DeepSeek V4 Pro 0423 has the larger context window: 262K for Seed-2.0-Code against 1.0M for DeepSeek V4 Pro 0423. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Seed-2.0-Code and DeepSeek V4 Pro 0423 support prompt caching?

DeepSeek V4 Pro 0423 publishes a cached-input rate of $0.080 per million tokens against a full input rate of $0.955. The catalogue lists no separate cached rate for Seed-2.0-Code, which means the provider does not price it separately here — not that caching is unavailable.

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.