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  5. Claude 3.7 SonnetvsQwen3.8 Omni Flash
Updated Sep 2026

Claude 3.7 SonnetvsQwen3.8 Omni Flash

Qwen3.8 Omni Flash is cheaper than Claude 3.7 Sonnet at $0.15/M vs $3/M input tokens.

Claude 3.7 Sonnet and Qwen3.8 Omni Flash compared across 9 shared prompts
SpecClaude 3.7 SonnetQwen3.8 Omni Flash
Input price$3/M tokens$0.15/M tokens
Output price$15/M tokens$0.47/M tokens
Context window200K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedFeb 2025Sep 2026
Side by side

Same prompt, both answers

Opening lines from 2 of the 9 prompts both answered. Formatting stripped, words untouched.

Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

Claude 3.7 Sonnet415 words
Analysis of MindMeld AI's Weakest Pitch Claims Weakest Claim #1: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy." Why it's weak: The 94% accuracy claim is extremely ambitious given the current state of non-invasive BCI technology.
Read the full answer
Qwen3.8 Omni Flash2,971 words
The three weakest claims—ranked by how likely they are to fail investor diligence—are: “94% accuracy… works with any language, any device.” “TAM: $180B” based on 3.5B smartphone users. “FDA clearance” consuming 40% of the Series A raise.
Read the full answer

Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

Claude 3.7 Sonnet721 words
LLM Explanations for Different Audiences For the Experienced Software Engineer Large language models like GPT operate fundamentally as massive pattern recognition systems, but with architectural innovations that allow them to handle context at unprecedented scale.
Read the full answer
Qwen3.8 Omni Flash913 words
For the experienced software engineer A large language model is best thought of as a gigantic, parameterized probabilistic function that maps a sequence of tokens to a probability distribution over the next token.
Read the full answer
Our Verdict
Claude 3.7 Sonnet
Claude 3.7 Sonnet
Qwen3.8 Omni Flash
Qwen3.8 Omni Flash

Not enough votes to call it. On the specs, nothing separates them.

Qwen3.8 Omni Flash costs 32x less per token.

Too close to call
API pricing

Cost per 1M tokens

Claude 3.7 Sonnet
Input
$3.00
Output
$15.00
Qwen3.8 Omni Flash
Input
$0.15
20× cheaper
Output
$0.47
32× cheaper

Qwen3.8 Omni Flash is cheaper on both: 20× input, 32× output.

Where to run it

1 host

Claude 3.7 Sonnet

No hosts listed on OpenRouter.

Qwen3.8 Omni Flash1 host
HostInOutContextUptime
Alibaba Clouddegraded$0.15 in·$0.47 out·1M·99% up

Per million tokens. Prices and uptime via OpenRouter, checked 25 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Claude 3.7 Sonnet is developed by Anthropic while Qwen3.8 Omni Flash is developed by Qwen. Claude 3.7 Sonnet has a 200K token context window vs Qwen3.8 Omni Flash's 1.0M. You can compare their actual outputs across 9 challenges on Rival to see how they differ in practice.

It depends on your use case. Claude 3.7 Sonnet and Qwen3.8 Omni Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 9 challenges so you can judge which fits your needs best.

Claude 3.7 Sonnet costs $3/M input tokens and Qwen3.8 Omni Flash costs $0.15/M input tokens. Qwen3.8 Omni Flash is $2.85/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

This page shows a side-by-side comparison of Claude 3.7 Sonnet and Qwen3.8 Omni Flash across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.

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Model pages

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