// head_to_head

Claude Opus 4.7 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

Claude Opus 4.7 scores higher, DeepSeek V4 Pro 0423 costs less — it depends on your workload.

Claude Opus 4.7 is ahead by 5.0 points overall, and DeepSeek V4 Pro 0423 lists 15× cheaper per blended million tokens. Whether 5.0 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Pro 0423 also leads on measured cost per point of capability, at $0.0261 per point.

anthropic

Claude Opus 4.7

Blended / 1M
$10.00
Context
1M
Released
Apr 16, 2026
Overall score
76.5
reasoningtool callingimage inputfile inputprompt caching

deepseek

DeepSeek V4 Pro 0423

Blended / 1M
$0.665
Context
1.0M
Released
Apr 24, 2026
Overall score
71.6
reasoningtool callingprompt caching

Specs and pricing

MetricClaude Opus 4.7DeepSeek V4 Pro 0423
LiveBench overall

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

76.5win71.6
Cost per point

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

$0.2846$0.0261win
Blended price / 1M

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

$10.00$0.665win
Input price / 1M$5.00$0.532win
Output price / 1M$25.00$1.06win
Cached input / 1M

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

$0.500$0.044win
Context window1M1.0M
Max output tokens128K384Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Opus 4.7 on top, DeepSeek V4 Pro 0423 below, both out of 100.

Agentic coding
50.7
42.6
Coding
82.1
70.0
Reasoning
87.2
82.7
Mathematics
92.9
90.7
Data analysis
78.3
74.5
Languagetoo close to call
77.9
78.1
Instruction following
66.7
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.

WorkloadClaude Opus 4.7DeepSeek V4 Pro 0423
Support chatbot

1.2K in / 400 out × 200K requests

$2876.00/mo$177.72/mo
RAG assistant

8K in / 600 out × 100K requests

$3700.00/mo$294.42/mo
Coding agent

40K in / 4K out × 20K requests

$3480.00/mo$237.67/mo
Document extraction

20K in / 1.5K out × 50K requests

$6650.00/mo$587.52/mo
Bulk classification

500 in / 20 out × 5M requests

$12,750/mo$1192.77/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

DeepSeek V4 Pro 0423

Lowest measured cost per point of capability at $0.0261 per point — the gap compounds with every request.

Quality matters more than the bill

Claude Opus 4.7

Highest overall LiveBench score of the two at 76.5.

The workload is coding or agentic work

Claude Opus 4.7

Leads on agentic coding — 50.7 against 42.6.

Claude Opus 4.7 vs DeepSeek V4 Pro 0423 FAQ

Which is better, Claude Opus 4.7 or DeepSeek V4 Pro 0423?

Claude Opus 4.7 scores higher, DeepSeek V4 Pro 0423 costs less — it depends on your workload. Claude Opus 4.7 is ahead by 5.0 points overall, and DeepSeek V4 Pro 0423 lists 15× cheaper per blended million tokens. Whether 5.0 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Pro 0423 also leads on measured cost per point of capability, at $0.0261 per point.

Is Claude Opus 4.7 cheaper than DeepSeek V4 Pro 0423?

DeepSeek V4 Pro 0423 is cheaper. On a 3:1 input:output blend, Claude Opus 4.7 lists at $10.00 per million tokens and DeepSeek V4 Pro 0423 at $0.665 — DeepSeek V4 Pro 0423 is 15× cheaper. Input and output are priced separately — Claude Opus 4.7 charges $5.00 in and $25.00 out, DeepSeek V4 Pro 0423 charges $0.532 and $1.06 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Opus 4.7 vs DeepSeek V4 Pro 0423: which scores higher on benchmarks?

Claude Opus 4.7 scores 76.5 and DeepSeek V4 Pro 0423 scores 71.6 overall on LiveBench, the mean of its seven categories. That is a 5.0-point lead for Claude Opus 4.7. 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 Opus 4.7 or DeepSeek V4 Pro 0423?

DeepSeek V4 Pro 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. Claude Opus 4.7 works out at $0.2846 per point and DeepSeek V4 Pro 0423 at $0.0261.

Does Claude Opus 4.7 or DeepSeek V4 Pro 0423 have a bigger context window?

They are effectively the same — 1M for Claude Opus 4.7 and 1.0M for DeepSeek V4 Pro 0423.

Do Claude Opus 4.7 and DeepSeek V4 Pro 0423 support prompt caching?

Both publish a cached-input rate: $0.500 per million for Claude Opus 4.7 and $0.044 for DeepSeek V4 Pro 0423, against full input rates of $5.00 and $0.532. 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.