Kimi K2.7 Code vs Kimi K3
Why this pair: Moonshot AI coding-agent and general frontier models
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Kimi K2.7 Code | Kimi-K3 |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | UnrankedNot in the 22-model eligible cohort | #11 of 2271.3 score |
| CostLower is better · Published-token output estimate | UnrankedNot in the 36-model eligible cohort | #24 of 36$0.194 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | UnrankedNot in the 22-model eligible cohort | #13 of 2251.7 score |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Kimi K2.7 Code | Kimi-K3 |
|---|---|---|
| Developer | Moonshot AI | Moonshot AI |
| Family | Kimi K2 7 | Kimi K3 |
| Model | Kimi K2.7 Code | Kimi-K3 |
| Version | Kimi K2.7 Code | Kimi-K3 |
| Lifecycle | active | active |
| Released | 2026-06-11 | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,049K |
| Total parameters | 1T | 2.8T |
| Active parameters | 32B | 104B |
| License | modified-mit | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | agents, chat, coding, reasoning, tools, vision | chat, generation, reasoning |
17 comparable fields · 11 material differences · Editorially curated pair passes the primary-source comparison gate
Kimi K2.7 Code Capabilities
Kimi K3 Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.7 Code vs Kimi K3 FAQs
Is Kimi K2.7 Code or Kimi K3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.7 Code and Kimi K3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.7 Code or Kimi K3?+
Kimi K2.7 Code is $0.66 and Kimi K3 is $2.85 per million tokens, so Kimi K2.7 Code is cheaper on this metric. Kimi K2.7 Code is $3.40 and Kimi K3 is $14.25 per million tokens, so Kimi K2.7 Code is cheaper on this metric.
Which has a larger context window, Kimi K2.7 Code or Kimi K3?+
Kimi K3 has the larger sourced context window. Kimi K2.7 Code supports 262K and Kimi K3 supports 1,049K.
Which performs better in benchmarks, Kimi K2.7 Code or Kimi K3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2.7 Code or Kimi K3 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.7 Code is open weight; Kimi K3 is open weight.
Can Kimi K2.7 Code and Kimi K3 understand images?+
Kimi K2.7 Code is documented with image input; Kimi K3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.7 Code or Kimi K3?+
Neither has a larger sourced maximum output. Kimi K2.7 Code is — and Kimi K3 is —.
Do Kimi K2.7 Code and Kimi K3 support reasoning and tool use?+
Kimi K2.7 Code: reasoning, tool calling, and image input. Kimi K3: reasoning and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.7 Code or Kimi K3?+
Kimi K2.7 Code has 4 sourced provider routes; Kimi K3 has 5, so Kimi K3 has broader tracked availability.
Which offers better value, Kimi K2.7 Code or Kimi K3?+
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.