GLM-4.7 vs Kimi K2 Thinking
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
किसी भी मॉडल को बदलें और तुलना करें
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GLM-4.7
ओपन-वेट्स मॉडलZhipu AI द्वारा विकसित GLM-4.7 एक उन्नत AI मॉडल है, जिसमें 204K टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
204.8K tokens
52%
Kimi K2 Thinking
ओपन-वेट्स मॉडलMoonshot AI द्वारा विकसित Kimi K2 Thinking एक उन्नत AI मॉडल है, जिसमें 262K टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
262.1K tokens
50%
एक नज़र में मुख्य अंतर
- 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% |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkसत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.7 · Strengths & Key Advantages
- Zhipu AI की उन्नत Mature Dense/MoE Hybrid Autoregressive Transformer आर्किटेक्चर और 204K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Kimi K2 Thinking · Strengths & Key Advantages
- Moonshot AI की उन्नत Deliberate Reasoning Open Sparse Mixture-of-Experts आर्किटेक्चर और 262K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
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 |
तकनीकी विशिष्टताएँ (Specs)
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: त्वरित प्रश्नोत्तर (FAQ)
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.
निष्कर्ष
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.
अक्सर पूछे जाने वाले प्रश्न (FAQ)
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.
स्रोत: GetInfoUs Independent AI Benchmarks & Official API Specs