Kimi K2 Instruct vs Kimi K2.7 Code
At a Glance
| Compare |  Kimi K2 InstructMoonshot AI | Kimi K2.7 CodeMoonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #9 of 44$0.042 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.57Openrouter ↗ · Aug 29, 2026 | $0.68Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $2.30Openrouter ↗ · Aug 29, 2026 | $3.21Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Kimi-K2-Instruct | Kimi K2.7 Code |
|---|---|---|
| Developer | Moonshot AI | Moonshot AI |
| Family | Kimi K2 Instruct | Kimi K2 7 |
| Model | Kimi-K2-Instruct | Kimi K2.7 Code |
| Version | Kimi-K2-Instruct | Kimi K2.7 Code |
| Lifecycle | active | active |
| Released | 2025-07-11 | 2026-06-11 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 1T | 1T |
| Active parameters | 32B | 32B |
| License | other | modified-mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard), Openrouter (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, tools | agents, chat, coding, reasoning, tools, vision |
Kimi K2 Instruct Capabilities
Kimi K2.7 Code Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2 Instruct vs Kimi K2.7 Code FAQs
Is Kimi K2 Instruct or Kimi K2.7 Code better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2 Instruct and Kimi K2.7 Code, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2 Instruct or Kimi K2.7 Code?+
Kimi K2 Instruct is $0.57 and Kimi K2.7 Code is $0.68 per million tokens, so Kimi K2 Instruct is cheaper on this metric. Kimi K2 Instruct is $2.30 and Kimi K2.7 Code is $3.21 per million tokens, so Kimi K2 Instruct is cheaper on this metric.
Which has a larger context window, Kimi K2 Instruct or Kimi K2.7 Code?+
Kimi K2.7 Code has the larger sourced context window. Kimi K2 Instruct supports 131K and Kimi K2.7 Code supports 262K.
Which performs better in benchmarks, Kimi K2 Instruct or Kimi K2.7 Code?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2 Instruct or Kimi K2.7 Code be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2 Instruct is open weight; Kimi K2.7 Code is open weight.
Can Kimi K2 Instruct and Kimi K2.7 Code understand images?+
Kimi K2 Instruct is not documented with image input; Kimi K2.7 Code is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2 Instruct or Kimi K2.7 Code?+
Neither has a larger sourced maximum output. Kimi K2 Instruct is — and Kimi K2.7 Code is —.
Do Kimi K2 Instruct and Kimi K2.7 Code support reasoning and tool use?+
Kimi K2 Instruct: tool calling. Kimi K2.7 Code: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2 Instruct or Kimi K2.7 Code?+
Kimi K2 Instruct has 2 sourced provider routes; Kimi K2.7 Code has 4, so Kimi K2.7 Code has broader tracked availability.
Which offers better value, Kimi K2 Instruct or Kimi K2.7 Code?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.