Moonshot AI
Kimi family · v2.5
Kimi K2.5
Retiring in 17dmoonshotai/kimi-k2.5Kimi K2.5 on Sylphx Models · 262K of context. One Responses contract, with list prices published per million tokens.
- Released
- Jan 27, 2026 · 241 days old
- Retires
- Oct 11, 2026 · 17 days left
- Provider
- Moonshot AI
- Context
- 262,144 tokens
Pricing
USD per million tokens- Input
- $0.5198
- Output
- $2.599
- Cached input
- $0.08085
$1.04 for 750,000 input and 250,000 output tokens.
Assumption: one batch of 750,000 input plus 250,000 output tokens at the published list prices — recompute for your own mix. Blended $1.039 per million at a 3:1 mix.
- Chat replies · 3:1 input:output
- $1.039 / M
- Long documents · 9:1 input:output
- $0.7277 / M
- Heavy output · 1:1 input:output
- $1.559 / M
- Cache write
- Not published
- Currency and unit
- USD / million tokens
Cached input reads cost 84% less than fresh input tokens.
Specification
- Context window
- 262,144 tokens
- Max output
- 235,929 tokens
- Input modalities
- Not published
- Output modalities
- Not published
- Family
- Kimi
- Version
- v2.5
- Provider
- Moonshot AI
- Released
- Jan 27, 2026 · 241 days old
- Model id
- moonshotai/kimi-k2.5
Data posture
StandardBy default, prompts and outputs sent to this model are not used to train models.
- Training on what you send · default
- No
- Zero Data Retention · default
- Yes
curl https://api.sylphx.ai/v1/responses \
-H "Authorization: Bearer $SYLPHX_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/kimi-k2.5",
"input": "Summarise this incident report in three bullets.",
"stream": false
}'
How to call it
Send the official Responses document to the base URL above with your organization key and this exact model id. Streaming arrives as ordered events with one terminal event.
- Retries: send an
Idempotency-Keyand a retry replays the original response instead of billing twice. - Errors: typed envelopes tell you whether to retry, wait, or fix the request.
- Keys: mint and revoke organization keys from the console.
Related models
Derived from this catalog: same provider first, then the same family, then the nearest neighbours by input price and context window.
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We do not publish benchmark scores for this model — we have not verified any. These external leaderboards run their own tests, so check them against your own workload.