Kimi K2.6 vs Qwen3.6 35B-A3B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Moonshot AI 的 Kimi K2.6 与 Alibaba 的 Qwen3.6 35B-A3B 是当前备受关注的两款主力 AI 模型。Kimi K2.6 提供 262.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Qwen3.6 35B-A3B 提供 262.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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Kimi K2.6
自主智能体与工具专家Moonshot AI 推出的 Kimi K2.6 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 262K 上下文窗口、68 Token/秒生成速度与 87/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
54%
Qwen3.6 35B-A3B
自主智能体与工具专家Alibaba 推出的 Qwen3.6 35B-A3B 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 262K 上下文窗口、110 Token/秒生成速度与 80/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3.6 35B-A3B is faster at 110 tok/s vs 68 tok/s.
| Feature | Kimi K2.6 | Qwen3.6 35B-A3B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 80 |
Provider | Moonshot AI | Alibaba |
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 | 262.1K tokens~393 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 68 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 54% | N/A |
GPQA Diamond Graduate-level reasoning | 71% | 62% |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2.6 · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 54% · GPQA Diamond 71%）
- 高达 68 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 590 毫秒
- 开放模型权重 (1.5T total (40B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Qwen3.6 35B-A3B · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（GPQA Diamond 62% · MMLU-Pro 78%）
- 高达 110 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 380 毫秒
- 开放模型权重 (35B total (3B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K2.6 | Qwen3.6 35B-A3B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 25K | 16K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 4K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2.6 | Qwen3.6 35B-A3B |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-6 | qwen3-6-35b-a3b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | February 2026 | February 2026 |
| Architecture | Refined Multimodal Agentic MoE Transformer | Tri-Modal Sparse Mixture-of-Experts Transformer |
| Parameters | 1.5T total (40B active MoE) | 35B total (3B 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 | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 930 GB VRAM (4-bit Quantized) · 3225 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 22 GB VRAM (4-bit Quantized) · 75 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, vision, audio |
| Output Modalities | text | text |
Kimi K2.6 vs Qwen3.6 35B-A3B: 常见问题解答 (FAQ)
Which is more intelligent, Kimi K2.6 or Qwen3.6 35B-A3B?
Kimi K2.6 is more intelligent. It scores 87 compared with 80 on the composite Intelligence Index.
Which model is faster?
Qwen3.6 35B-A3B is faster, generating 110 tokens per second compared with 68 tokens per second.
Which model has lower latency?
Qwen3.6 35B-A3B has lower latency, with a time to first token of 0.38s compared with 0.59s.
综合结论
综合来看，Kimi K2.6（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Qwen3.6 35B-A3B（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Kimi K2.6 在综合智能与编程基准测试中表现更强，Qwen3.6 35B-A3B 拥有更快的 Token 生成速度，而 Kimi K2.6 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Kimi K2.6 和 Qwen3.6 35B-A3B 哪个更强？+
在综合智能指数与编程基准评测中，Kimi K2.6 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Kimi K2.6 与 Qwen3.6 35B-A3B 的 API 价格哪个更便宜？+
Kimi K2.6 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Qwen3.6 35B-A3B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Kimi K2.6 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Kimi K2.6 支持 262.1K Token 上下文窗口，而 Qwen3.6 35B-A3B 支持 262.1K Token。在流式生成速度方面，Qwen3.6 35B-A3B 的吞吐表现更快。
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.
数据来源: Moonshot AI 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。