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DeepSeek V4 Flash Vision Exp vs Kimi K3

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

Kimi K3 scores higher, DeepSeek V4 Flash Vision Exp costs less — it depends on your workload.

Kimi K3 is ahead by 2.4 points overall, and DeepSeek V4 Flash Vision Exp lists 18× cheaper per blended million tokens. Whether 2.4 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Flash Vision Exp also leads on measured cost per point of capability, at $0.0277 per point.

deepseek

DeepSeek V4 Flash Vision Exp

Blended / 1M
$0.330
Context
1.0M
Released
Aug 21, 2026
Overall score
76.8
reasoningtool callingimage inputprompt caching

moonshotai

Kimi K3

Blended / 1M
$6.00
Context
1.0M
Released
Jul 16, 2026
Overall score
79.2
reasoningtool callingimage inputvideo inputprompt caching

Specs and pricing

MetricDeepSeek V4 Flash Vision ExpKimi K3
LiveBench overall

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

76.879.2win
Cost per point

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

$0.0277win$0.1909
Blended price / 1M

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

$0.330win$6.00
Input price / 1M$0.220win$3.00
Output price / 1M$0.660win$15.00
Cached input / 1M

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

$0.0070win$0.300
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 Vision Exp on top, Kimi K3 below, both out of 100.

Agentic coding
65.1
62.2
Coding
68.2
81.4
Reasoning
85.4
90.7
Mathematics
87.8
84.4
Data analysistoo close to call
79.5
78.7
Language
80.4
85.5
Instruction followingtoo close to call
71.0
71.4

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.

WorkloadDeepSeek V4 Flash Vision ExpKimi K3
Support chatbot

1.2K in / 400 out × 200K requests

$90.26/mo$1725.60/mo
RAG assistant

8K in / 600 out × 100K requests

$130.40/mo$2220.00/mo
Coding agent

40K in / 4K out × 20K requests

$109.52/mo$2088.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$258.85/mo$3990.00/mo
Bulk classification

500 in / 20 out × 5M requests

$509.50/mo$7650.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

DeepSeek V4 Flash Vision Exp

Lowest measured cost per point of capability at $0.0277 per point — the gap compounds with every request.

Quality matters more than the bill

Kimi K3

Highest overall LiveBench score of the two at 79.2.

The workload is coding or agentic work

DeepSeek V4 Flash Vision Exp

Leads on agentic coding — 65.1 against 62.2.

DeepSeek V4 Flash Vision Exp vs Kimi K3 FAQ

Which is better, DeepSeek V4 Flash Vision Exp or Kimi K3?

Kimi K3 scores higher, DeepSeek V4 Flash Vision Exp costs less — it depends on your workload. Kimi K3 is ahead by 2.4 points overall, and DeepSeek V4 Flash Vision Exp lists 18× cheaper per blended million tokens. Whether 2.4 points is worth that depends on how much a wrong answer costs you. DeepSeek V4 Flash Vision Exp also leads on measured cost per point of capability, at $0.0277 per point.

Is DeepSeek V4 Flash Vision Exp cheaper than Kimi K3?

DeepSeek V4 Flash Vision Exp is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash Vision Exp lists at $0.330 per million tokens and Kimi K3 at $6.00 — DeepSeek V4 Flash Vision Exp is 18× cheaper. Input and output are priced separately — DeepSeek V4 Flash Vision Exp charges $0.220 in and $0.660 out, Kimi K3 charges $3.00 and $15.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.

DeepSeek V4 Flash Vision Exp vs Kimi K3: which scores higher on benchmarks?

DeepSeek V4 Flash Vision Exp scores 76.8 and Kimi K3 scores 79.2 overall on LiveBench, the mean of its seven categories. That is a 2.4-point lead for Kimi K3. 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 Vision Exp or Kimi K3?

DeepSeek V4 Flash Vision Exp. 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 Vision Exp works out at $0.0277 per point and Kimi K3 at $0.1909.

Does DeepSeek V4 Flash Vision Exp or Kimi K3 have a bigger context window?

They are effectively the same — 1.0M for DeepSeek V4 Flash Vision Exp and 1.0M for Kimi K3.

Do DeepSeek V4 Flash Vision Exp and Kimi K3 support prompt caching?

Both publish a cached-input rate: $0.0070 per million for DeepSeek V4 Flash Vision Exp and $0.300 for Kimi K3, against full input rates of $0.220 and $3.00. 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.