Source-tracked model comparison

Kimi K2.6 vs Kimi K2.7 Code: which costs less for a practical API workload?

Compare verified pricing and model limits for routing within the Kimi K2.6 family. The cost example uses a classification and routing workload and does not assume equal model quality.

Direct cost answer: Kimi K2.6 is estimated at $492.50 per month and Kimi K2.7 Code at $492.50 for 700 input tokens, 80 output tokens, and 500,000 monthly requests. Kimi K2.6 is $0.00 lower under these assumptions. This does not identify a quality winner.
Same-provider comparisonClassification and routingSources checked 2026-07-17 / 2026-07-17
Tracked facts

Pricing and model limits

Prices are USD per 1M tokens under each model's verified default profile.

FieldKimi K2.6Kimi K2.7 Code
API access providerKimi APIKimi API
Model creatorMoonshot AIMoonshot AI
API model IDkimi-k2.6kimi-k2.7-code
Input / 1M$0.95$0.95
Cached input / 1M$0.16$0.19
Output / 1M$4.00$4.00
Context window262,144 tokens262,144 tokens
Maximum outputUnavailable tokensUnavailable tokens
Accepted inputtext, imagetext
Example workload

Classification and routing cost scenario

700 input and 80 output tokens per request, 500,000 monthly requests, and 0% cached input.

Moonshot AI

Kimi K2.6

$492.50 / month
Input cost
$332.50
Output cost
$160.00
Per 1,000 calls
$0.985
Pricing profile
standard
View model details
Moonshot AI

Kimi K2.7 Code

$492.50 / month
Input cost
$332.50
Output cost
$160.00
Per 1,000 calls
$0.985
Pricing profile
standard
View model details

Cost result: Kimi K2.6 is $0.00 lower per month for these assumptions. This is a price comparison, not a model-quality ranking.

StackLens assessment

Questions to answer before choosing

  • Which model meets the required quality threshold on representative inputs?
  • How do output length, retries, and caching change the measured cost?
  • Which provider and deployment path fit the team's operational requirements?
Workload caveat

What this estimate leaves out

Quality and latency are not assumed equal across models; validate labels on a representative evaluation set.

Latency, reliability, output quality, retries, regional processing, and provider-specific tool charges can change the practical decision.