Kimi K3 vs GLM 5V Turbo
At a Glance
| Compare |  Kimi K3Moonshot AI |  GLM 5V TurboZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #17 of 4670.3 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #30 of 44$0.194 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #21 of 3852.5 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.85Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $14.25Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 200K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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-K3 | GLM-5V-Turbo |
|---|---|---|
| Developer | Moonshot AI | Z.ai |
| Family | Kimi K3 | Glm 5v |
| Model | Kimi-K3 | GLM-5V-Turbo |
| Version | Kimi-K3 | GLM-5V-Turbo |
| Lifecycle | active | active |
| Released | 2026-07-16 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 200K |
| Total parameters | 2.8T | Unknown |
| Active parameters | 104B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Z.ai (Standard) |
| Capabilities | chat, generation, reasoning | agents, chat, computer-use, reasoning, tools, vision |
Kimi K3 Capabilities
GLM 5V Turbo Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K3 vs GLM 5V Turbo FAQs
Is Kimi K3 or GLM 5V Turbo better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K3 and GLM 5V Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K3 or GLM 5V Turbo?+
Only Kimi K3 has a directly sourced input price: $2.85 per million tokens. Only Kimi K3 has a directly sourced output price: $14.25 per million tokens.
Which has a larger context window, Kimi K3 or GLM 5V Turbo?+
Kimi K3 has the larger sourced context window. Kimi K3 supports 1,049K and GLM 5V Turbo supports 200K.
Which performs better in benchmarks, Kimi K3 or GLM 5V Turbo?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K3 or GLM 5V Turbo be self-hosted?+
Kimi K3 is the only model in this pair currently marked as self-hostable. Kimi K3 is open weight; GLM 5V Turbo is not marked open weight.
Can Kimi K3 and GLM 5V Turbo understand images?+
Kimi K3 is documented with image input; GLM 5V Turbo is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K3 or GLM 5V Turbo?+
Neither has a larger sourced maximum output. Kimi K3 is — and GLM 5V Turbo is 131K.
Do Kimi K3 and GLM 5V Turbo support reasoning and tool use?+
Kimi K3: reasoning and image input. GLM 5V Turbo: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K3 or GLM 5V Turbo?+
Kimi K3 has 5 sourced provider routes; GLM 5V Turbo has 1, so Kimi K3 has broader tracked availability.
Which offers better value, Kimi K3 or GLM 5V Turbo?+
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.