Kimi K2.6 vs Qwen3.6 35B-A3B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Kimi K2.6 by Moonshot AI and Qwen3.6 35B-A3B by Alibaba 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, both models provide an identical memory horizon with 262K tokens. In latency-sensitive production environments, Qwen3.6 35B-A3B 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.
بدّل وقارن بين أي نموذجين
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Kimi K2.6
نموذج مفتوح الأوزاننموذج Kimi K2.6 المقدم من Moonshot AI يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
54%
Qwen3.6 35B-A3B
نموذج مفتوح الأوزاننموذج Qwen3.6 35B-A3B المقدم من Alibaba يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
أهم الفروقات في لمحة
- 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% |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2.6 · Strengths & Key Advantages
- بنية Refined Multimodal Agentic MoE Transformer متطورة من Moonshot AI مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Qwen3.6 35B-A3B · Strengths & Key Advantages
- بنية Tri-Modal Sparse Mixture-of-Experts Transformer متطورة من Alibaba مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
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: إجابات سريعة (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.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Kimi K2.6 if your priority is Autonomous browser agents, computer-use automation, and full-stack software maintenance. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.6 35B-A3B if your workflow prioritizes Multimodal desktop agents, voice-annotated document review, and video summarization. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Kimi K2.6 compare to Qwen3.6 35B-A3B in API token pricing and cost per token?+
Both Kimi K2.6 and Qwen3.6 35B-A3B 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 K2.6 or Qwen3.6 35B-A3B?+
Both Kimi K2.6 and Qwen3.6 35B-A3B offer an identical context window of 262K tokens (~197k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Kimi K2.6 or Qwen3.6 35B-A3B?+
Kimi K2.6 scores 54% on SWE-bench Verified, while Qwen3.6 35B-A3B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Kimi K2.6 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Kimi K2.6 and Qwen3.6 35B-A3B?+
Kimi K2.6 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.6 35B-A3B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K2.6 and Qwen3.6 35B-A3B?+
Kimi K2.6 generates approximately 68 tokens per second, while Qwen3.6 35B-A3B delivers 110 tokens per second. In latency-sensitive production environments, Qwen3.6 35B-A3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K2.6 and Qwen3.6 35B-A3B 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 Qwen3.6 35B-A3B, and route deep architectural or complex reasoning queries to Kimi K2.6. 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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs