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  5. Claude Opus 4.6vsQwen3.6 35B A3B
Updated Apr 2026

Claude Opus 4.6vsQwen3.6 35B A3B

Qwen3.6 35B A3B is cheaper than Claude Opus 4.6 at $0.1612/M vs $5/M input tokens.

Claude Opus 4.6 and Qwen3.6 35B A3B compared across 53 shared prompts
SpecClaude Opus 4.6Qwen3.6 35B A3B
Input price$5/M tokens$0.1612/M tokens
Output price$25/M tokens$0.9653/M tokens
Context window1.0M tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedFeb 2026Apr 2026
Our Verdict
Claude Opus 4.6
Claude Opus 4.6
Qwen3.6 35B A3B
Qwen3.6 35B A3BRunner-up

Not enough votes to call it. On the specs, Claude Opus 4.6 has the edge: bigger model tier, bigger context window, major provider backing.

Qwen3.6 35B A3B costs 26x less per token.

Too close to call
API pricing

Cost per 1M tokens

Claude Opus 4.6
Input
$5.00
Output
$25.00
Qwen3.6 35B A3B
Input
$0.16
31× cheaper
Output
$0.97
26× cheaper

Qwen3.6 35B A3B is cheaper on both: 31× input, 26× output.

Where to run it

14 hosts, cheapest first

Claude Opus 4.64 hosts
HostInOutContextUptime
Amazon Bedrock$5.00 in·$25.00 out·1M·99.9% upAzure AI Foundry$5.00 in·$25.00 out·1M·97.2% upAnthropic$5.00 in·$25.00 out·1M·99.9% upGoogle Vertex AI$5.00 in·$25.00 out·1M·100% up
Qwen3.6 35B A3B10 hosts
HostInOutContextUptime
DDarkbloomfp4$0.05 in·$0.70 out·262k·99.9% upAAkashMLfp8$0.10 in·$0.90 out·262k·99.8% upDDeepInfrafp8$0.10 in·$0.95 out·262k·97.4% upVVenicefp8$0.10 in·$1.00 out·256k·100% upIio.netfp8$0.13 in·$0.94 out·262k·99.7% upPParasailfp8$0.15 in·$1.00 out·262k·99.7% up
4 more hostsFewer hosts
AAtlasCloudfp8$0.19 in·$1.11 out·262k·99.8% upPPhala$0.20 in·$1.27 out·262k·99.6% upSSiliconFlowfp8$0.20 in·$1.60 out·262k·97.8% upCCoreWeavefp8$0.25 in·$1.25 out·262k·99.9% up

Per million tokens. Prices and uptime via OpenRouter, checked 9 Sep 2026.

Writing DNA

Style Comparison

Similarity
63%

Claude Opus 4.6 uses 2.3x more hedging

Claude Opus 4.6
Qwen3.6 35B A3B
56%Vocabulary59%
33wSentence Length15w
0.73Hedging0.31
5.3Bold4.9
2.7Lists2.7
1.16Emoji1.60
1.23Headings0.75
0.02Transitions0.05
Based on 26 + 27 text responses
Research

What we learned reading every model

FAQ

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