Requesty

kimi-k2 vs glm-5.3-flash

Side-by-side comparison of kimi-k2 and glm-5.3-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. glm-5.3-flash outperforms kimi-k2 on 3 of 3 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
kimi-k225.9%
glm-5.3-flash46.2%
Coding Indexcoding
kimi-k2N/A
glm-5.3-flash71.5%
Math Indexmath
kimi-k294.7%
glm-5.3-flashN/A
GPQA Diamondreasoning
kimi-k283.8%
glm-5.3-flash91.2%
AIME 2025math
kimi-k294.7%
glm-5.3-flashN/A
LiveCodeBenchcoding
kimi-k285.3%
glm-5.3-flashN/A
Terminal-Bench Hardagentic
kimi-k231.1%
glm-5.3-flashN/A
τ²-Benchagentic
kimi-k293.0%
glm-5.3-flashN/A
SciCodecoding
kimi-k2N/A
glm-5.3-flash51.6%
MMLU Proknowledge
kimi-k284.8%
glm-5.3-flashN/A
Humanity's Last Examreasoning
kimi-k223.8%
glm-5.3-flash39.9%

Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.

Pricing & specifications

kimi-k2glm-5.3-flash
Input price / 1M$0.60$0.15$0.07
Output price / 1M$2.50$0.50$0.25
Context window262K tokens1M tokens
Max output262K tokens128K tokens
Vision inputN/AYes
Tool callingYesYes
ReasoningYesYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderGoogle LLC (Vertex AI)Z.ai

Questions people ask

Is kimi-k2 better than glm-5.3-flash?
glm-5.3-flash outperforms kimi-k2 on 3 of 3 shared benchmarks. See the benchmark comparison above for specifics: kimi-k2 and glm-5.3-flash have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, kimi-k2 or glm-5.3-flash?
glm-5.3-flash is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while glm-5.3-flash costs $0.07/$0.25. The prices shown are what you pay: glm-5.3-flash runs at 50% off its list rate of $0.15 per 1M input and $0.50 per 1M output until 9 Sep 2026.
Can I use kimi-k2 and glm-5.3-flash through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "vertex/kimi-k2" and "zai/glm-5.3-flash", no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. glm-5.3-flash supports up to 1M tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.

Switch between kimi-k2 and glm-5.3-flash with one line of code

Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.