Standard Compute
Model Comparison 2026

DeepSeek V3.2 vs Kimi K2.7 Code

DeepSeek V3.2
DeepSeek · Balanced · Open weights
$0.31/1M · 164K ctx
Kimi K2.7 Code
Moonshot AI · Balanced · Open weights
$1.56/1M · 262K ctx

Community Vote

Pick a winner in each dimension — change your vote anytime.

Output Quality
Correctness and depth of what it produces
Agentic Ability
Tool calls, instruction-following, and multi-step tasks
Speed
Tokens per second and time-to-first-token
Value for $
How much capability you get per dollar
Reliability
Consistent results — fewer refusals, loops, and format breaks

Specs & Pricing, Side by Side

Spec
DeepSeek V3.2
Kimi K2.7 Code
Maker
DeepSeek
Moonshot AI
Blended price / 1M
$0.31
$1.56
Input / output
$0.27 in · $0.40 out / 1M tokens
$0.73 in · $3.50 out / 1M tokens
Context window
164K
262K
Open weights
Yes
Yes
Tool use
Yes
Yes
Reasoning
Yes
Yes
Output Quality
7.6
8.6
Agentic Ability
7.4
9.2
Speed
7.8
7.8
Value for $
9.4
9.2
Reliability
8.6
8.9

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: DeepSeek V3.2 or Kimi K2.7 Code?

Updated 2026-08-02

Choose DeepSeek V3.2 if you want dependable open-weight output on a tight budget. Choose Kimi K2.7 Code if you want cheap, capable open-weight coding agents.

In our editorial scoring, Kimi K2.7 Code leads in 3 of five dimensions (output quality, agentic ability and reliability), while DeepSeek V3.2 leads in 1. On price, DeepSeek V3.2 runs about $0.31 per 1M tokens (blended) and is open-weight; Kimi K2.7 Code is about $1.56 and open-weight.

Where DeepSeek V3.2 falls short
  • Shorter 128K context
  • Superseded by V4 on quality
Full DeepSeek V3.2 breakdown →
Where Kimi K2.7 Code falls short
  • Below the very top models on hard reasoning
  • Throughput varies by host
Full Kimi K2.7 Code breakdown →

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