GLM-4.7 vs Kimi K2 Thinking
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GLM-4.7 by Zhipu AI and Kimi K2 Thinking by Moonshot 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 K2 Thinking provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, GLM-4.7 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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GLM-4.7
Dependable GLMThe pinnacle of the GLM-4 generation, offering rock-solid stability, refined instruction following, and advanced tool use.
$0.00 / 1M
$0.00 / 1M
204.8K tokens
52%
Kimi K2 Thinking
Thinking KimiSpecialized deliberate reasoning model trained with reinforcement learning for slow research, multi-hour plan formation, and math proofs.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
50%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Kimi K2 Thinking has a larger context window (262K vs 205K tokens).
- GLM-4.7 is faster at 72 tok/s vs 55 tok/s.
- GLM-4.7 leads on SWE-bench coding (52.0% vs 50.0%).
| Feature | GLM-4.7 | Kimi K2 Thinking |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 85 |
Provider | Zhipu AI | Moonshot 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 | 204.8K tokens~307 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 32K |
Throughput Speed Tokens generated per second | 72 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 52% | 50% |
GPQA Diamond Graduate-level reasoning | 66% | 70% |
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
GLM-4.7 · Strengths & Key Advantages
- Extremely high instruction-following compliance with minimal refusal drift
- Superb structured output reliability across complex JSON and SQL schemas
- Completely open-weights release with zero proprietary restrictions
Considerations & Trade-offs
- Text-only; lacks vision perception
- Substantial GPU footprint for full precision serving
Kimi K2 Thinking · Strengths & Key Advantages
- Exhaustive step-by-step mathematical reasoning and verification loops
- Spacious 262K context window accommodating dense scientific whitepapers
- Open-weights release with unrestricted academic and commercial utility
Considerations & Trade-offs
- Extended reasoning chains increase response latency noticeably
- Overly verbose on simple conversational requests
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7 | Kimi K2 Thinking |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 20K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 16K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7 | Kimi K2 Thinking |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7 | kimi-k2-thinking |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2025-11 |
| Knowledge Cutoff | October 2025 | September 2025 |
| Architecture | Mature Dense/MoE Hybrid Autoregressive Transformer | Deliberate Reasoning Open Sparse Mixture-of-Experts |
| Parameters | 180B MoE | 1T total (32B active 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 | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 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 | text |
| Output Modalities | text | text |
GLM-4.7 vs Kimi K2 Thinking: Quick Answers
Which is more intelligent, GLM-4.7 or Kimi K2 Thinking?
Kimi K2 Thinking is more intelligent. It scores 85 compared with 84 on the composite Intelligence Index.
Which model is faster?
GLM-4.7 is faster, generating 72 tokens per second compared with 55 tokens per second.
Which model has lower latency?
GLM-4.7 has lower latency, with a time to first token of 0.54s compared with 0.95s.
Which model has a larger context window?
Kimi K2 Thinking has a larger context window, supporting 262.1K tokens compared with 204.8K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose GLM-4.7 if your priority is Enterprise ERP automation, SQL database querying agents, and rigorous legal drafting. Its performance in demanding tasks justifies its operational token cost. Choose Kimi K2 Thinking if your workflow prioritizes Formal mathematical proofs, algorithmic architecture, and comprehensive scientific research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does GLM-4.7 compare to Kimi K2 Thinking in API token pricing and cost per token?+
Both GLM-4.7 and Kimi K2 Thinking 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: GLM-4.7 or Kimi K2 Thinking?+
GLM-4.7 supports a context window of 205K tokens (~154k words), while Kimi K2 Thinking supports 262K tokens (~197k words). Therefore, Kimi K2 Thinking can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-4.7 or Kimi K2 Thinking?+
GLM-4.7 scores 52% on SWE-bench Verified while Kimi K2 Thinking scores 50%. For autonomous software engineering and multi-file debugging, GLM-4.7 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GLM-4.7 and Kimi K2 Thinking?+
GLM-4.7 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Kimi K2 Thinking provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-4.7 and Kimi K2 Thinking?+
GLM-4.7 generates approximately 72 tokens per second, while Kimi K2 Thinking delivers 55 tokens per second. In latency-sensitive production environments, GLM-4.7 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-4.7 and Kimi K2 Thinking 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-4.7, and route deep architectural or complex reasoning queries to GLM-4.7. 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