Head-to-Head Model Comparison

Kimi K2.6 vs Qwen3.6 35B-A3B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Kimi K2.6Profile

Qwen3.6 35B-A3BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Moonshot AI

Kimi K2.6

Model Agen Otonom & Alat

Kimi K2.6 dari Moonshot AI adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 262K, kecepatan 68 token/detik, indeks kecerdasan 87/100, s

Intelligence Index87 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

Alibaba

Qwen3.6 35B-A3B

Model Agen Otonom & Alat

Qwen3.6 35B-A3B dari Alibaba adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 262K, kecepatan 110 token/detik, indeks kecerdasan 80/100

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

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.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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%

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3.6 35B-A3B

+62% faster generation

Context

Tied

262.1K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Kimi K2.6Qwen3.6 35B-A3B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

54%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

71%vs62%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

83.5%vs78%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1395ptsvs1325pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs110 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Kimi K2.60.59s

Qwen3.6 35B-A3B0.38s

Est. End-to-End Response (500 tokens)TTFT + generation time

Kimi K2.67.94s

Qwen3.6 35B-A3B4.93s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Kimi K2.6 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.6 35B-A3B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Kimi K2.6 · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 54% · GPQA Diamond 71%)
Kecepatan generasi tinggi 68 token/detik dengan latensi token pertama (TTFT) hanya 590 ms
Bobot terbuka (1.5T total (40B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Qwen3.6 35B-A3B · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (GPQA Diamond 62% · MMLU-Pro 78%)
Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 380 ms
Bobot terbuka (35B total (3B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Kimi K2.6 vs Qwen3.6 35B-A3B: Jawaban Cepat (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.

Kesimpulan

Baik Kimi K2.6 (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.6 35B-A3B (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Kimi K2.6 unggul dalam penalaran dan pemrograman, Qwen3.6 35B-A3B memberikan kecepatan generasi token lebih tinggi, dan Kimi K2.6 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Kimi K2.6 atau Qwen3.6 35B-A3B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Kimi K2.6 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Kimi K2.6 vs Qwen3.6 35B-A3B?+

Kimi K2.6 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3.6 35B-A3B sebesar $0.00 / $0.00. Secara keseluruhan, Kimi K2.6 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Kimi K2.6 mendukung konteks 262.1K token dibandingkan 262.1K token pada Qwen3.6 35B-A3B, sementara Qwen3.6 35B-A3B unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

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

Ganti & Bandingkan Model Apa Saja

Kimi K2.6

Qwen3.6 35B-A3B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Kimi K2.6 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.6 35B-A3B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Kimi K2.6 vs Qwen3.6 35B-A3B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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