Last refreshed 2026-06-04. Next refresh: weekly.
Why use Kimi K2.5 on Replicate API?
Replicate API offers Kimi K2.5 with pay-as-you-go pricing at $0.60/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Kimi K2.5 across 10 providers to find the best fit for your use caseInput / 1M
$0.60
Output / 1M
$3.00
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install replicateAuth
export REPLICATE_API_TOKEN=...Call
import replicate
output = replicate.run(
"moonshotai/kimi-k2.5",
input={"prompt": "Hello"}Model ID
moonshotai/kimi-k2.5Request example
import replicate
# reads REPLICATE_API_TOKEN from env
# moonshotai/kimi-k2.5 format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"moonshotai/kimi-k2.5",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "moonshotai/kimi-k2.5", not the LLMReference slug "kimi-k2-5".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; rename it only if your application config maps the new variable.
Compare Kimi K2.5 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | — | — |
| Fireworks AI | $0.60 | $3.00 |
| OpenRouter | $0.44 | $2.00 |
| Together AI | $0.50 | $2.80 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.60 |
| Output tokens | $3.00 |
Capabilities
VisionMultimodalJSON / Tool useStructured Outputs
About Kimi K2.5
Kimi K2.5 is Moonshot AI's Kimi model focused on code generation and software engineering. It offers a 256K-token context window and scores 87.9 on GPQA.
Get Started
Model Specs
Released2026-03-15
Parameters1T (MoE, 384 experts)
Context256k
ArchitectureMixture of Experts