// head_to_head

Claude Sonnet 5 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 wins outright — it scores higher and costs less.

DeepSeek V4 Pro 0813 leads by 1.4 points overall while listing 2.2× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. DeepSeek V4 Pro 0813 also leads on measured cost per point of capability, at $0.0241 per point.

anthropic

Claude Sonnet 5

Blended / 1M
$4.00
Context
1M
Released
Jun 30, 2026
Overall score
76.0
reasoningtool callingimage inputfile inputprompt 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

MetricClaude Sonnet 5DeepSeek V4 Pro 0813
LiveBench overall

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

76.077.4win
Cost per point

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

$0.2691$0.0241win
Blended price / 1M

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

$4.00$1.78win
Input price / 1M$2.00$1.19win
Output price / 1M$10.00$3.56win
Cached input / 1M

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

$0.200$0.040win
Context window1M1.0M
Max output tokens128K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Sonnet 5 on top, DeepSeek V4 Pro 0813 below, both out of 100.

Agentic coding
59.4
54.9
Coding
80.7
77.2
Reasoning
88.7
85.8
Mathematics
92.9
95.1
Data analysis
71.7
79.2
Language
75.0
82.1
Instruction following
63.9
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.

WorkloadClaude Sonnet 5DeepSeek V4 Pro 0813
Support chatbot

1.2K in / 400 out × 200K requests

$1150.40/mo$487.56/mo
RAG assistant

8K in / 600 out × 100K requests

$1480.00/mo$704.88/mo
Coding agent

40K in / 4K out × 20K requests

$1392.00/mo$592.42/mo
Document extraction

20K in / 1.5K out × 50K requests

$2660.00/mo$1397.88/mo
Bulk classification

500 in / 20 out × 5M requests

$5100.00/mo$2752.20/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

DeepSeek V4 Pro 0813

Lowest measured cost per point of capability at $0.0241 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

Claude Sonnet 5

Leads on agentic coding — 59.4 against 54.9.

Claude Sonnet 5 vs DeepSeek V4 Pro 0813 FAQ

Which is better, Claude Sonnet 5 or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 wins outright — it scores higher and costs less. DeepSeek V4 Pro 0813 leads by 1.4 points overall while listing 2.2× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. DeepSeek V4 Pro 0813 also leads on measured cost per point of capability, at $0.0241 per point.

Is Claude Sonnet 5 cheaper than DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 is cheaper. On a 3:1 input:output blend, Claude Sonnet 5 lists at $4.00 per million tokens and DeepSeek V4 Pro 0813 at $1.78 — DeepSeek V4 Pro 0813 is 2.2× cheaper. Input and output are priced separately — Claude Sonnet 5 charges $2.00 in and $10.00 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.

Claude Sonnet 5 vs DeepSeek V4 Pro 0813: which scores higher on benchmarks?

Claude Sonnet 5 scores 76.0 and DeepSeek V4 Pro 0813 scores 77.4 overall on LiveBench, the mean of its seven categories. That is a 1.4-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, Claude Sonnet 5 or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813. 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. Claude Sonnet 5 works out at $0.2691 per point and DeepSeek V4 Pro 0813 at $0.0241.

Does Claude Sonnet 5 or DeepSeek V4 Pro 0813 have a bigger context window?

They are effectively the same — 1M for Claude Sonnet 5 and 1.0M for DeepSeek V4 Pro 0813.

Do Claude Sonnet 5 and DeepSeek V4 Pro 0813 support prompt caching?

Both publish a cached-input rate: $0.200 per million for Claude Sonnet 5 and $0.040 for DeepSeek V4 Pro 0813, against full input rates of $2.00 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.