Kimi K2 Thinking vs GLM 5.3 Flash
At a Glance
| Compare |  Kimi K2 ThinkingMoonshot AI |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.60Openrouter ↗ · Sep 23, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Openrouter ↗ · Sep 23, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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
| Benchmark | Kimi-K2-Thinking | GLM-5.3-Flash |
|---|---|---|
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 78.5589% of row best · points · Kimi K2 Thinking · 7,821 output tokens / case | 88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Kimi-K2-Thinking | GLM-5.3-Flash |
|---|---|---|
| Developer | Moonshot AI | Z.ai |
| Family | Kimi K2 Thinking | Glm 5 3 Flash |
| Model | Kimi-K2-Thinking | GLM-5.3-Flash |
| Version | Kimi-K2-Thinking | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | 2025-11-06 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 1T | 320B |
| Active parameters | 32B | 18B |
| License | other | 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), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
Kimi K2 Thinking Capabilities
GLM 5.3 Flash Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Kimi-K2-ThinkingMoonshot AI | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
 Gemini 3.1 ProGoogle DeepMind | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
Kimi-K2-ThinkingMoonshot AI | vs | Grok 4.6xAI | cross-developer peerstext |
Kimi-K2-ThinkingMoonshot AI | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Kimi-K2-ThinkingMoonshot AI | vs | Kimi-K3Moonshot AI | family variantstext |
 MiniMax-M3MiniMax | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
Kimi-K2-ThinkingMoonshot AI | vs | GLM-5.3Z.ai | cross-developer peerstext |
Kimi-K2-ThinkingMoonshot AI | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
 Mistral Large 3Mistral AI | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Kimi K2 Thinking vs GLM 5.3 Flash FAQs
Is Kimi K2 Thinking or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2 Thinking and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2 Thinking or GLM 5.3 Flash?+
Kimi K2 Thinking is $0.60 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Kimi K2 Thinking is $2.50 and GLM 5.3 Flash is $0.25 per million tokens, so GLM 5.3 Flash is cheaper on this metric.
Which has a larger context window, Kimi K2 Thinking or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced context window. Kimi K2 Thinking supports 262K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Kimi K2 Thinking or GLM 5.3 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2 Thinking or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2 Thinking is open weight; GLM 5.3 Flash is open weight.
Can Kimi K2 Thinking and GLM 5.3 Flash understand images?+
Kimi K2 Thinking is not documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2 Thinking or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. Kimi K2 Thinking is 131K and GLM 5.3 Flash is 131K.
Do Kimi K2 Thinking and GLM 5.3 Flash support reasoning and tool use?+
Kimi K2 Thinking: reasoning and tool calling. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2 Thinking or GLM 5.3 Flash?+
Kimi K2 Thinking has 2 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, Kimi K2 Thinking or GLM 5.3 Flash?+
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.