Kimi K2 Thinking vs GLM 5.3
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Kimi-K2-Thinking | GLM-5.3 |
|---|---|---|
| CostLower is better · Published-token output estimate | UnrankedNot in the 36-model eligible cohort | #28 of 36$0.248 per LiveBench case |
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-Thinking | GLM-5.3 |
|---|---|---|
| Developer | Moonshot AI | Z.ai |
| Family | Kimi K2 Thinking | Glm 5 3 |
| Model | Kimi-K2-Thinking | GLM-5.3 |
| Version | Kimi-K2-Thinking | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2025-11-06 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 1T | Unknown |
| Active parameters | 32B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| 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, reasoning, structured_outputs, tools |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Kimi K2 Thinking Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
Related Comparisons
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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 |  Hy4 previewTencent | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
Kimi K2 Thinking vs GLM 5.3 FAQs
Is Kimi K2 Thinking or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2 Thinking and GLM 5.3, 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?+
Kimi K2 Thinking is $0.60 and GLM 5.3 is $1.20 per million tokens, so Kimi K2 Thinking is cheaper on this metric. Kimi K2 Thinking is $2.50 and GLM 5.3 is $4.00 per million tokens, so Kimi K2 Thinking is cheaper on this metric.
Which has a larger context window, Kimi K2 Thinking or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Kimi K2 Thinking supports 262K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Kimi K2 Thinking or GLM 5.3?+
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 be self-hosted?+
Kimi K2 Thinking is the only model in this pair currently marked as self-hostable. Kimi K2 Thinking is open weight; GLM 5.3 is not marked open weight.
Can Kimi K2 Thinking and GLM 5.3 understand images?+
Kimi K2 Thinking is not documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2 Thinking or GLM 5.3?+
Neither has a larger sourced maximum output. Kimi K2 Thinking is 131K and GLM 5.3 is 131K.
Do Kimi K2 Thinking and GLM 5.3 support reasoning and tool use?+
Kimi K2 Thinking: reasoning and tool calling. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2 Thinking or GLM 5.3?+
Kimi K2 Thinking has 2 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Kimi K2 Thinking or GLM 5.3?+
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.