Kimi K3 vs GLM-5.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Kimi K3 by Moonshot AI and GLM-5.2 by Zhipu AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Kimi K3 provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, GLM-5.2 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Kimi K3
2.8T Open ReasonerMonumental 2.8-trillion parameter open-weights multimodal frontier model featuring 1M context window and toggleable max-effort thinking.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
GLM-5.2
Value Long-ContextMassive million-token open-source flagship delivering sustained multi-turn reasoning, vast repository synthesis, and frontier autonomy.
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Kimi K3 has a larger context window (1M vs 1M tokens).
- GLM-5.2 is faster at 60 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 67.5%).
| Feature | Kimi K3 | GLM-5.2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 91 |
Provider | Moonshot AI | Zhipu AI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 131K | 32K |
Throughput Speed Tokens generated per second | 58 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 74.5% | 67.5% |
GPQA Diamond Graduate-level reasoning | 79% | 73.5% |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K3 · Strengths & Key Advantages
- Largest open-weights foundation model in existence (2.8T parameters)
- 1480 LMSYS Chatbot Arena Elo rivaling proprietary closed frontier leaders
- Full 1M token context window paired with massive 131K max output tokens
Considerations & Trade-offs
- Extreme hardware prerequisites (distributed multi-node cluster deployment)
- Generates extensive reasoning traces that demand careful token budget management
GLM-5.2 · Strengths & Key Advantages
- Full 1M token context window paired with a staggering 1440 Arena Elo score
- 67.5% SWE-bench score rivaling the most capable commercial proprietary models
- Complete open-weights freedom with extensive enterprise customization options
Considerations & Trade-offs
- Colossal memory footprint necessitates multi-node accelerator clusters
- Text-only model; visual tasks require integration with GLM vision pipelines
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K3 | GLM-5.2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 35K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 12K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K3 | GLM-5.2 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k3 | glm-5-2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-06 |
| Knowledge Cutoff | March 2026 | February 2026 |
| Architecture | Colossus-Scale Multimodal Sparse Mixture-of-Experts | Massive Million-Token Open MoE Frontier Transformer |
| Parameters | 2.8T total (MoE) | 350B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Kimi K3 vs GLM-5.2: Quick Answers
Which is more intelligent, Kimi K3 or GLM-5.2?
Kimi K3 is more intelligent. It scores 93 compared with 91 on the composite Intelligence Index.
Which model is faster?
GLM-5.2 is faster, generating 60 tokens per second compared with 58 tokens per second.
Which model has lower latency?
GLM-5.2 has lower latency, with a time to first token of 0.65s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Kimi K3 if your priority is National AI research facilities, sovereign enterprise clouds, and frontier STEM reasoning. Its performance in demanding tasks justifies its operational token cost. Choose GLM-5.2 if your workflow prioritizes Large-scale repository refactoring, national research initiatives, and sovereign enterprise intelligence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Kimi K3 compare to GLM-5.2 in API token pricing and cost per token?+
Both Kimi K3 and GLM-5.2 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Kimi K3 or GLM-5.2?+
Kimi K3 supports a context window of 1,049K tokens (~786k words), while GLM-5.2 supports 1,000K tokens (~750k words). Therefore, Kimi K3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Kimi K3 or GLM-5.2?+
Kimi K3 scores 74.5% on SWE-bench Verified while GLM-5.2 scores 67.5%. For autonomous software engineering and multi-file debugging, Kimi K3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Kimi K3 and GLM-5.2?+
Kimi K3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-5.2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K3 and GLM-5.2?+
Kimi K3 generates approximately 58 tokens per second, while GLM-5.2 delivers 60 tokens per second. In latency-sensitive production environments, GLM-5.2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K3 and GLM-5.2 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GLM-5.2, and route deep architectural or complex reasoning queries to Kimi K3. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs