Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Kimi K2.6Profile

Qwen3.6 35B-A3BProfile

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Popular Matchups:

Moonshot AI

Kimi K2.6

Autonomes Agenten-Modell

Kimi K2.6 von Moonshot AI ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 262K Kontextfenster, 68 Token/s Durchsatz, einem Intelligenz-Index von 87/100 und kostenlosem Open-We

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

Autonomes Agenten-Modell

Qwen3.6 35B-A3B von Alibaba ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 262K Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 80/100 und kostenlosem Open

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

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%

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

262K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 54% · GPQA Diamond 71%)
Hohe Generierungsgeschwindigkeit von 68 Token/Sekunde bei nur 590 ms Erstlatenz (TTFT)
Offene Modellgewichte (1.5T total (40B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

262K Kontextfenster und bis zu 16K maximale Output-Token (GPQA Diamond 62% · MMLU-Pro 78%)
Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 380 ms Erstlatenz (TTFT)
Offene Modellgewichte (35B total (3B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Kimi K2.6 vs Qwen3.6 35B-A3B: Schnelle Antworten (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.

Fazit

Sowohl Kimi K2.6 (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen3.6 35B-A3B (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Kimi K2.6 führt bei komplexem Reasoning und Coding, Qwen3.6 35B-A3B liefert den höheren Token-Durchsatz und Kimi K2.6 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Kimi K2.6 oder Qwen3.6 35B-A3B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Kimi K2.6 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Kimi K2.6 und Qwen3.6 35B-A3B?+

Kimi K2.6 kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Qwen3.6 35B-A3B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Kimi K2.6 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Kimi K2.6 bietet 262.1K Tokens Kontext gegenüber 262.1K Tokens bei Qwen3.6 35B-A3B. Bei der Generierungsgeschwindigkeit liegt Qwen3.6 35B-A3B vorn.

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

Beliebige Modelle wechseln & vergleichen

Kimi K2.6

Qwen3.6 35B-A3B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Kimi K2.6 · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Kimi K2.6 vs Qwen3.6 35B-A3B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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