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  5. Claude Opus 5.5vsClaude Sonnet 4.6
Updated Sep 2026

Claude Opus 5.5vsClaude Sonnet 4.6

Claude Sonnet 4.6 is cheaper than Claude Opus 5.5 at $3/M vs $4/M input tokens.

Claude Opus 5.5 and Claude Sonnet 4.6 compared across 19 shared prompts
SpecClaude Opus 5.5Claude Sonnet 4.6
Input price$4/M tokens$3/M tokens
Output price$20/M tokens$15/M tokens
Context window1.0M tokens1.0M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedSep 2026Feb 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 19 prompts both answered. Formatting stripped, words untouched.

Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

Claude Opus 5.53,435 words
The Transistor in 1920: A Counterfactual Through 1980 First, a Reality Check on the Premise A transistor "invented" in 1920 would not have changed the world in 1920. The actual 1947 transistor rested on three foundations that did not exist yet: Quantum theory of solids.
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Claude Sonnet 4.66,173 words
The Transistor at 1920: A Speculative History Methodological Note First This is a serious analytical exercise, not science fiction. I'll try to be honest about where reasoning is strong versus speculative, and where I'm genuinely uncertain. The goal is to trace mechanisms, not just assert outcomes.
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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

Claude Opus 5.5900 words
HarborTech Services: 5-Year LBO Underwriting Conventions: $m. Interest is charged on beginning-of-year balances, which avoids circularity. Term Loan (TL) amortization is 1% of the original $480m, or $4.8m per year. Mezz PIK accrues to principal. All FCF after mandatory debt service sweeps to the TL.
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Claude Sonnet 4.61,614 words
HarborTech Services (HTS) – LBO Underwriting Analysis SECTION 0: ENTRY CAPITALIZATION Enterprise Value at Entry: EV = 12.0x × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m (funded by equity) Debt at Close: Tranche Multiple Amount Term Loan 4.0x × $120m $480m Mezzanine 1.5x × $120m $180m Total Debt 5.5x…
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Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

Claude Opus 5.52,280 words
Architecture Review: Real-Time Collaborative Editor The most serious problems cluster in three areas: the sync model (client-clock LWW), cross-server propagation (per-server WebSockets with DB polling), and the CDN/auth layer, which can leak data between users. Issues are grouped by area below.
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Claude Sonnet 4.6587 words
Comprehensive Architecture Review: Real-Time Collaborative Document Editor Executive Summary This architecture has critical flaws that will cause data loss, security vulnerabilities, and poor user experience at scale. I've identified 18 distinct issues across 6 categories.
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Michelin Star Recipe Design

Design an innovative recipe for a multi-component dish worthy of a Michelin 3-star restaurant. The recipe should feature an unusual…

Claude Opus 5.52,253 words
"Hive & Tide" Langoustine, Beeswax, White Chocolate & Oscietra Caviar, Hay-Baked Celeriac, Fermented Green Strawberry, Sea Buckthorn I. The Concept Most coastal menus celebrate the meeting of sea and land. This dish is about a stranger, quieter meeting: the meadow and the seabed, the bee and the langoustine.
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Claude Sonnet 4.63,507 words
"Tidal Memory" A Meditation on the Meeting of Land and Sea CONCEPTUAL NARRATIVE This dish was born from a single image: the precise moment at low tide when a rocky coastline exhales — releasing the mineral breath of the ocean while the surrounding pine forest absorbs it.
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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 Opus 5.51,442 words
How Large Language Models Work: Three Explanations For the Experienced Software Engineer Think of an LLM as a single, very large pure function: f(tokens[0..n]) → probability distribution over tokens[n+1]. Text is chunked into tokens (roughly word fragments, from a vocabulary of about 100k).
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Claude Sonnet 4.61,375 words
For the Experienced Software Engineer You're right to be skeptical of "predicting the next word" as a description — that framing makes it sound like autocomplete on steroids, which undersells what's actually happening architecturally.
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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 Opus 5.5936 words
The 3 Weakest Claims in the MindMeld AI Pitch "Decodes neural patterns into text with 94% accuracy. Works with any language, any device." (Slide 3) Why it's weak: It far exceeds the published state of the art. The best results for thought-to-text come from invasive implants.
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Claude Sonnet 4.6905 words
MindMeld AI Pitch Deck: Critical Analysis The 3 Weakest Claims Weakness #1: The TAM Calculation ($180B Targeting 3.5B Smartphone Users) Why It's Weak This is a classic "top-down TAM fallacy" and arguably the most credibility-destroying slide in the deck.
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Our Verdict
Claude Opus 5.5
Claude Opus 5.5
Claude Sonnet 4.6
Claude Sonnet 4.6

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

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Claude Opus 5.5
Input
$4.00
Output
$20.00
Claude Sonnet 4.6
Input
$3.00
1.3× cheaper
Output
$15.00
1.3× cheaper

Claude Sonnet 4.6 is cheaper on both: 1.3× input, 1.3× output.

Where to run it

8 hosts

Claude Opus 5.54 hosts
HostInOutContextUptime
Amazon Bedrock$4.00 in·$20.00 out·1M·99.8% upAzure AI Foundry$4.00 in·$20.00 out·1M·100% upAnthropic$4.00 in·$20.00 out·1M·100% upGoogle Vertex AI$4.00 in·$20.00 out·1M·100% up
Claude Sonnet 4.64 hosts
HostInOutContextUptime
Amazon Bedrock$3.00 in·$15.00 out·1M·99.9% upAzure AI Foundry$3.00 in·$15.00 out·1M·85.2% upAnthropic$3.00 in·$15.00 out·1M·100% upGoogle Vertex AI$3.00 in·$15.00 out·1M·99.9% up

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

Research

What we learned reading every model

FAQ

Common questions

Both are developed by Anthropic but target different use cases. Claude Opus 5.5 has a 1.0M token context window vs Claude Sonnet 4.6's 1.0M. You can compare their actual outputs across 19 challenges on Rival to see how they differ in practice.

It depends on your use case. Claude Opus 5.5 and Claude Sonnet 4.6 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 19 challenges so you can judge which fits your needs best.

Claude Opus 5.5 costs $4/M input tokens and Claude Sonnet 4.6 costs $3/M input tokens. Claude Sonnet 4.6 is $1.00/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 Opus 5.5 and Claude Sonnet 4.6 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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