Kimi K2 Instruct
moonshotai/kimi-k2-instructPar Moonshot AI · famille: kimi · sorti 2025-07-11 · fin de connaissance: 2024-10
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacités
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding52
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents55
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit10/15
- Provider availability10/10
JSON / structured output46
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume16/20
Cost efficiency54
- Headline price (log-scaled)54/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness96
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $1.50 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $3.00 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $1.20 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $2.80 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $2.40 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Détail des tarifs
Tarif recommandé de nano-gpt · baseten/Kimi-K2-Instruct-FP4
Fournisseur le moins cher : nvidia · Unknown entrée + Unknown sortie
Disponible chez 12 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| Fireworks AI fireworks-ai | accounts/fireworks/models/kimi-k2-instruct | $1.00 | $3.00 | 128K | 2025-07-11 |
| Groq groq | moonshotai/kimi-k2-instruct | $1.00 | $3.00 | 131K | 2025-07-14 |
| Deep Infra deepinfra | moonshotai/Kimi-K2-Instruct | $0.500 | $2.00 | 131K | 2025-07-11 |
| Hugging Face huggingface | moonshotai/Kimi-K2-Instruct | $1.00 | $3.00 | 131K | 2025-07-14 |
| NanoGPT nano-gpt | baseten/Kimi-K2-Instruct-FP4 | $0.100 | $2.00 | 128K | 2025-07-11 |
| NanoGPT nano-gpt | moonshotai/kimi-k2-instruct | $0.100 | $2.00 | 256K | 2025-07-01 |
| Alibaba (China) alibaba-cn | moonshot-kimi-k2-instruct | $0.574 | $2.29 | 131K | 2025-01-01 |
| Jiekou.AI jiekou | moonshotai/kimi-k2-instruct | $0.570 | $2.30 | 131K | 2026-01 |
| NovitaAI novita-ai | moonshotai/kimi-k2-instruct | $0.570 | $2.30 | 131K | 2025-07-11 |
| Nvidia nvidia | moonshotai/kimi-k2-instruct | Unknown | Unknown | 128K | 2025-01-01 |
| Cortecs cortecs | kimi-k2-instruct | $0.551 | $2.65 | 131K | 2025-07-11 |
| SiliconFlow siliconflow | moonshotai/Kimi-K2-Instruct | $0.580 | $2.29 | 131K | 2025-07-13 |
Incohérences de données entre fournisseurs
- context_window varies: 128000, 131000, 131072, 256000
- release_date varies (span 365d): 2025-01-01, 2025-07-01, 2025-07-11, 2025-07-13, 2025-07-14, 2026-01
Les fournisseurs rapportent des valeurs différentes pour ce modèle. Les infos clés ci-dessus utilisent un fournisseur représentatif ; voir le tableau pour le détail par fournisseur.
Frequently asked questions
How much does Kimi K2 Instruct cost?
Kimi K2 Instruct costs $0.100 per 1M input tokens and $2.00 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Kimi K2 Instruct?
Kimi K2 Instruct has a context window of 128K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Kimi K2 Instruct support tool calling?
Yes. Kimi K2 Instruct supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Kimi K2 Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Kimi K2 Instruct in our data source. Verify with Moonshot AI's official documentation before relying on it in production.
Can Kimi K2 Instruct accept image input?
No. Kimi K2 Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Kimi K2 Instruct open-weight?
Yes. Kimi K2 Instruct's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Kimi K2 Instruct?
If Kimi K2 Instruct doesn't fit, consider Kimi K2.5, Kimi K2 Thinking, Kimi K2.6. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
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- Kimi K2 Instruct 0905$0.39 in / $1.90 out
- Kimi K2 Instruct 0905$0.40 in / $2.00 out
Capability lists this model is in
Dernière mise à jour :
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.